Energy recovery control method and device applied to multi-axis robot

Abstract

The invention provides an energy recovery control method and device applied to a multi-axis robot, and the method comprises the steps: determining the to-be-recovered total output energy and the total demand energy needed by the operation of the multi-axis robot when the multi-axis robot is detected to meet an energy recovery condition; on the basis of the total output energy to be recycled, the total demand energy, the current available storage remaining amount of the flywheel energy storage unit and the current available storage remaining amount of the super-capacitor unit and the health state of the super-capacitor unit, a multi-target optimization decision is executed, and the energy distribution proportion between the flywheel energy storage unit and the super-capacitor unit is determined; and on the basis of the energy distribution proportion, the to-be-recycled energy corresponding to the to-be-recycled total output energy is stored to a flywheel energy storage unit and a super-capacitor unit, and according to the scheme, joint braking and arm body gravitational potential energy are cooperatively recycled, and a flywheel and super-capacitor composite energy storage mode is adopted to be matched with a multi-target optimization distribution strategy; and efficient recovery and management of multi-source and dynamic regeneration energy of the multi-axis robot are achieved.

Description

Technical Field

[0001] The present invention relates to the field of robotics, and in particular to an energy recovery control method and apparatus for multi-axis robots. Background Technology

[0002] With the continuous improvement of industrial automation, multi-axis industrial robots are increasingly widely used in high-dynamic, high-load scenarios such as handling, assembly, and palletizing. These robots frequently start, stop, lift, accelerate, and decelerate during operation, highlighting energy consumption issues and necessitating improvements in system energy efficiency. Therefore, researching and applying efficient mechanical energy recovery and reuse technologies is of great significance for reducing robot operating costs and improving energy sustainability.

[0003] Currently, to recover robot energy, a recovery system based on a single energy storage unit is typically used. For example, by installing current and speed sensors at the drive ends of the motors at each joint of the robot, the braking and power generation status of the motors is detected, and the generated regenerated electrical energy is fed back to the DC bus and finally stored in a single type of energy storage unit.

[0004] However, the above-mentioned technical solutions have the following drawbacks: they use a single energy storage unit to cope with the complex energy recovery conditions of robots. Due to the inherent contradiction between power density and energy density in a single energy storage unit, it is difficult to simultaneously meet the robot's dual requirements for rapid absorption / release of instantaneous high-power energy and continuous, large-scale energy buffering, resulting in poor adaptability between the energy storage system and dynamically changing recovery power. In addition, existing solutions lack a decision-making mechanism that can collaboratively manage multiple energy sources and the real-time load demand of the system. Energy flow is in a passive and decentralized state, failing to achieve globally optimal energy allocation and utilization, which seriously restricts the overall efficiency and practicality of the energy recovery system. Summary of the Invention

[0005] This application provides an energy recovery control method and device for multi-axis robots, which achieves efficient recovery and management of multi-source, dynamically regenerated energy of multi-axis robots by coordinating the recovery of joint braking and arm gravitational potential energy, and by using a flywheel and supercapacitor composite energy storage method in conjunction with a multi-objective optimization allocation strategy.

[0006] In a first aspect, embodiments of this application provide an energy recovery control method for a multi-axis robot. The multi-axis robot has a large arm that carries the main load and is capable of pitching motion. The large arm is driven by at least one drive joint. The multi-axis robot is equipped with a flywheel energy storage unit and a supercapacitor unit for recovering and storing energy. The method includes:

[0007] When the multi-axis robot meets the energy recovery conditions based on the real-time collected joint motion state data and arm motion state data, the total output energy to be recovered and the total energy required for the operation of the multi-axis robot are determined.

[0008] Based on the total output energy to be recovered, the total energy demand, the current available storage capacity and health status of the flywheel energy storage unit and the supercapacitor unit, a multi-objective optimization decision is performed to determine the energy allocation ratio between the flywheel energy storage unit and the supercapacitor unit.

[0009] Based on the energy allocation ratio, the energy to be recovered, corresponding to the total output energy to be recovered, is stored in the flywheel energy storage unit and the supercapacitor unit, respectively.

[0010] Secondly, embodiments of this application also provide an energy recovery control device for a multi-axis robot. The multi-axis robot has a large arm that bears the main load and is capable of pitching motion. The large arm is driven by at least one drive joint. The multi-axis robot is equipped with a flywheel energy storage unit and a supercapacitor unit for recovering and storing energy. The device includes:

[0011] The energy recovery determination module is used to determine the total output energy to be recovered and the total energy required for the operation of the multi-axis robot when the multi-axis robot is detected to meet the energy recovery conditions based on real-time collected joint motion state data and arm motion state data.

[0012] The energy allocation determination module is used to perform multi-objective optimization decision-making based on the total output energy to be recovered, the total energy demand, the current available storage capacity and health status of the flywheel energy storage unit and the supercapacitor unit, and to determine the energy allocation ratio between the flywheel energy storage unit and the supercapacitor unit.

[0013] The energy distribution and recovery module is used to store the energy to be recovered, corresponding to the total output energy to be recovered, into the flywheel energy storage unit and the supercapacitor unit, respectively, based on the energy distribution ratio.

[0014] Thirdly, embodiments of this application also provide an electronic device, which includes:

[0015] One or more processors;

[0016] Storage device for storing one or more programs.

[0017] When one or more programs are executed by one or more processors, the one or more processors implement an energy recovery control method for a multi-axis robot as described in any of the embodiments of this application.

[0018] Fourthly, embodiments of this application also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform an energy recovery control method for a multi-axis robot as described in any of the embodiments of this application.

[0019] This application provides an energy recovery control method for a multi-axis robot. The multi-axis robot has a large arm that carries the main load and is capable of pitching motion. The large arm is driven by at least one drive joint. The multi-axis robot is equipped with a flywheel energy storage unit and a supercapacitor unit for recovering and storing energy. The method includes: when the multi-axis robot meets the energy recovery conditions based on real-time collected joint motion state data and arm motion state data, determining the total output energy to be recovered and the total energy required for the operation of the multi-axis robot; then, based on the total output energy to be recovered, the total energy required, the current available storage capacity and health status of the flywheel energy storage unit and the supercapacitor unit, performing multi-objective optimization decision-making to determine the energy allocation ratio between the flywheel energy storage unit and the supercapacitor unit; and then, based on the energy allocation ratio, storing the energy to be recovered corresponding to the total output energy to be recovered into the flywheel energy storage unit and the supercapacitor unit respectively. The technical solution of this application expands the energy source by integrating joint braking energy and arm gravitational potential energy, and introduces a composite energy storage system composed of a flywheel and a supercapacitor as the execution carrier. Combined with multi-objective optimization decision-making based on real-time supply and demand status and energy storage unit health, an energy allocation ratio is generated, realizing the coordinated recovery and optimized storage of multi-source, dynamically regenerated energy. This solves the problems of poor dynamic recovery power adaptability and lack of coordinated management of multi-source energy flow in existing energy storage methods, and improves the overall energy efficiency and response speed of the multi-axis robot energy recovery system. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the exemplary embodiments of this application, the accompanying drawings used in describing the embodiments are briefly introduced below. Obviously, the accompanying drawings described are only a portion of the embodiments to be described in this application, and not all of them. For those skilled in the art, other drawings can be obtained from these drawings without any creative effort.

[0021] Figure 1 A schematic flowchart illustrating an energy recovery control method for a multi-axis robot provided in an embodiment of this application;

[0022] Figure 2 This is a schematic diagram of a layered architecture involved in an embodiment of this application;

[0023] Figure 3 A schematic flowchart illustrating another energy recovery control method for a multi-axis robot provided in this application embodiment;

[0024] Figure 4 This is an exemplary structural diagram of the energy recovery control cabinet involved in the embodiments of this application;

[0025] Figure 5 This is a schematic diagram of an energy recovery control device for a multi-axis robot according to an embodiment of the present invention.

[0026] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0027] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present application, not the entire structure.

[0028] Before introducing the technical solution provided in the embodiments of this application, the application scenario of the solution can be described first. This embodiment is applicable to scenarios where various multi-axis industrial robots need to recover and reuse energy when performing high-dynamic, high-load operations. Currently, although energy recovery systems based on single energy storage units are widely used for recovering braking energy of robot joints, traditional methods have obvious limitations. In practical applications, due to the complexity of robot operating conditions, regenerated energy has characteristics such as high instantaneous power, violent fluctuations, and dispersed sources. Moreover, the energy recovery process needs to take into account multiple factors such as the real-time load demand of the system, the state of the energy storage unit, and the lifespan of the equipment. A single energy storage architecture is difficult to balance power response and energy buffering. Traditional energy management methods also lack global collaborative optimization capabilities, which can easily lead to problems such as low energy recovery efficiency, poor system stability, and short lifespan of energy storage equipment. Therefore, there is an urgent need for an energy recovery control method that can collaboratively manage multi-source energy, adapt to composite energy storage systems, and make intelligent decisions based on real-time operating conditions to improve the overall energy efficiency and economy of robot systems.

[0029] Example 1

[0030] Figure 1 This is a flowchart illustrating an energy recovery control method for multi-axis robots provided in this application embodiment. This embodiment is applicable to various multi-axis industrial robots that need to recover and reuse energy when performing high-dynamic, high-load operations. The method can be executed by an energy recovery control device applied to multi-axis robots. This device can be implemented in the form of software and / or hardware. The hardware can be a controller, such as a mobile terminal, PC, or server.

[0031] The multi-axis robot provided in this embodiment has a large arm that carries the main load and is capable of pitching motion. The large arm is driven by at least one drive joint. The multi-axis robot is equipped with a flywheel energy storage unit and a supercapacitor unit for energy recovery and storage.

[0032] Multi-axis robots refer to industrial automated mechanical devices with multiple degrees of freedom of motion. The main arm is the core robotic arm segment in a multi-axis robot, connecting the base and the end effector, and capable of pitching and swinging to bear the main workload. The drive joint actuator is a servo drive system that provides power to the robot's various motion axes and achieves precise position, speed, or torque control. A flywheel energy storage unit is a mechanical energy storage device that converts electrical energy into kinetic energy through a high-speed rotating flywheel for storage, and releases the kinetic energy back into electrical energy by deceleration when needed. A supercapacitor unit is an electrochemical energy storage device based on the double-layer principle, capable of rapidly absorbing and releasing high-power instantaneous electrical energy, with high power density and long cycle life.

[0033] This can be understood as follows: in a multi-axis robot structure, the core moving component is a large arm that bears the main mass and external force load of the system and can pitch around a horizontal axis. The pitch freedom of this large arm is powered by at least one drive joint, which also enables position and speed adjustment. At the same time, two types of heterogeneous energy storage devices are integrated into the robot body or control system: the flywheel energy storage unit uses a high-speed rotating mass to convert mechanical energy into rotational kinetic energy and temporarily stores it; the supercapacitor unit uses the electrochemical double layer principle to quickly store electrical energy in the form of charge. Together, they constitute an energy recovery and storage subsystem, so that the reverse mechanical power generated by the robot during deceleration or load lowering can be converted and stored in the flywheel rotating body and the supercapacitor respectively, realizing efficient recovery and short-term reuse of braking energy.

[0034] like Figure 1 As shown, the energy recovery control method for multi-axis robots provided in this embodiment of the invention includes the following steps:

[0035] S110. When the multi-axis robot meets the energy recovery conditions based on the real-time collected joint motion state data and arm motion state data, determine the total output energy to be recovered and the total energy required for the operation of the multi-axis robot.

[0036] Joint motion state data refers to the set of physical quantities used to characterize the joint dynamics, which are collected and processed in real time by sensors (such as Hall effect current sensors, incremental encoders, and strain gauge torque sensors) installed at each drive joint of a multi-axis robot. Joint motion state data includes at least the rotational speed reflecting the speed of joint rotation, the output torque reflecting the magnitude of the mechanical load on the joint, and the current reflecting the operating state of the joint motor. These data together constitute the determination of whether the joint is in an energy-consuming state or a braking and power-generating state.

[0037] Among them, arm motion state data refers to the set of data collected and processed in real time by specific sensors (such as absolute encoders and triaxial accelerometers) installed on the multi-axis robot arm, which is used to accurately describe the posture and kinematic state of the arm in space. Arm motion state data includes at least angle data to determine the pitch angle of the arm relative to the reference position, acceleration data to reflect the acceleration of the arm's center of mass, and the end-effector linear velocity calculated by the kinematic model. These data together form the basis for calculating the change of gravitational potential energy of the arm and determining whether it is in a potential energy release state.

[0038] The energy recovery condition is a preset logical criterion. It identifies and confirms whether the multi-axis robot currently has a source of regenerative energy available for recovery by analyzing joint motion data and arm motion data in real time. Optionally, the specific criterion for determining the energy recovery condition can be: the logical condition is satisfied only when, based on joint motion data, it is determined that at least one drive joint is in a braking and power generation state, and / or based on arm motion data, it is determined that the upper arm is in a gravitational potential energy release state.

[0039] The total output energy to be recovered refers to the total energy production rate that can be captured from the moving parts of the multi-axis robot and converted into electrical energy. The total energy required for the operation of the multi-axis robot refers to the overall rate at which all its driven joints consume electrical energy to maintain the robot's intended motion and load operation.

[0040] Specifically, joint motion state data reflecting the dynamic characteristics of each drive joint and arm motion state data reflecting the posture and kinematic characteristics of the upper arm can be jointly analyzed. Based on preset logic, it can be determined whether there is recyclable energy available, i.e., whether the energy recovery conditions are met. If the conditions are met, two parallel calculation processes are started simultaneously: The first process is to summarize and calculate the total power output of all identified energy sources, including drive joints in braking and power generation state and the upper arm in gravitational potential energy release state, at the same moment, so as to obtain the total output energy to be recovered, which represents the current rate of energy recovery; The second process is to identify and summarize the total power demand of all drive joints in energy consumption state at the same moment, so as to obtain the total energy demand required for the operation of the multi-axis robot, which represents the current energy consumption rate of the robot.

[0041] Based on the above embodiments, the method may optionally further include: detecting whether the energy recovery conditions are met when the multi-axis robot is in operation, based on the real-time collected joint motion state data and arm motion state data.

[0042] The joint motion data includes joint rotational speed data and joint output torque data, while the arm motion data includes upper arm pitch angle data and upper arm acceleration data. Joint rotational speed data refers to the angular velocity parameter, measured in real-time by an incremental encoder, used to quantify the speed of the drive joint's rotational movement. Joint output torque data refers to the physical quantity, measured in real-time by a strain gauge torque sensor, used to quantify the magnitude of the torque transmitted to the mechanical load by the drive joint. Upper arm pitch angle data refers to the attitude parameter, measured in real-time by an absolute encoder, used to accurately describe the tilt angle of the upper arm relative to a preset horizontal reference plane. Upper arm acceleration data refers to the dynamic parameter, measured in real-time by a triaxial accelerometer, used to reflect the acceleration vector of the upper arm's center of mass in three-dimensional space.

[0043] Based on this, specific implementation methods for detecting whether the energy recovery conditions are met when using a multi-axis robot, based on real-time collected joint motion state data and arm motion state data, may include:

[0044] Based on real-time collected joint rotation speed data and joint output torque data, it is determined whether at least one drive joint is in a braking and power generation state.

[0045] Among them, the braking and power generation state refers to a specific working state in which the actual rotation direction of the motor of a certain drive joint of a multi-axis robot is opposite to the load direction of its output torque during motion control. This causes the servo motor of the joint to switch from the electric mode that consumes electrical energy to the power generation mode that converts mechanical energy into electrical energy, thereby feeding back regenerated electrical energy to the DC bus.

[0046] Specifically, within each control cycle, the synchronously acquired joint speed data, which characterizes the speed of joint rotation, and the joint output torque data, which characterizes the joint load torque, can be compared and analyzed in terms of direction. Specifically, for each drive joint, when the rotation direction represented by the current joint speed data is different from (i.e., opposite in sign) to the load torque represented by the joint output torque data, the joint is determined to be in a state where the motor output torque resists its direction of motion, and is thus logically determined to be in a braking and power generation state that converts mechanical energy into electrical energy. As long as one or more joints are determined in this way, it is confirmed that the condition that at least one drive joint is in a braking and power generation state is met.

[0047] Based on real-time collected data on the pitch angle and acceleration of the upper arm, it is determined whether the upper arm is in a state of gravitational potential energy release.

[0048] Among them, the gravitational potential energy release state refers to a specific energy conversion state in which the posture (characterized by the pitch angle data of the upper arm) and the center of mass motion trend (characterized by the end linear velocity derived from the acceleration data of the upper arm and the kinematic model) of the upper arm during its movement indicate that the height of the total center of mass of the upper arm and its load is decreasing, and the stored gravitational potential energy is being converted into electrical energy through the drive system.

[0049] Specifically, within each control cycle, by analyzing the changing trend of the upper arm pitch angle data (e.g., the angle increases), and combining the direction of the end effector linear velocity calculated by the kinematic model based on the upper arm motion acceleration data (e.g., the linear velocity is negative downwards), when the logical judgment indicates that the height of the upper arm's center of gravity is decreasing, that is, its stored potential energy is being converted into other forms of energy through the joint drive system, it is determined that the upper arm is currently in a state of gravitational potential energy release.

[0050] If at least one drive joint is in a braking and power generation state, and / or the upper arm is in a gravitational potential energy release state, then the multi-axis robot is determined to meet the energy recovery condition.

[0051] Specifically, if at least one of the two identification results is true, the multi-axis robot is determined to meet the energy recovery conditions. That is, either it is confirmed that at least one drive joint is in a braking and power generation state that feeds mechanical energy back into electrical energy; or it is confirmed that the upper arm is in a gravitational potential energy release state that converts its gravitational potential energy into electrical energy through the drive system; or it is confirmed that at least one drive joint is in a braking and power generation state that feeds mechanical energy back into electrical energy, and the upper arm is in a gravitational potential energy release state that converts its gravitational potential energy into electrical energy through the drive system. In this case, the multi-axis robot as a whole has met the prerequisites for initiating a series of control processes such as subsequent energy recovery, management, and storage, i.e., it meets the energy recovery conditions.

[0052] Based on the above embodiments, optionally, the specific steps for determining the total output energy to be recovered may include:

[0053] (1) In response to the detection that at least one drive joint is in a braking power generation state, the braking regeneration power of each drive joint in the braking power generation state is determined based on the output torque and rotational speed of the drive joint.

[0054] Among them, regenerative braking power refers to the instantaneous power value of a certain drive joint that converts mechanical energy into electrical energy and feeds it back to the DC bus at the current instant.

[0055] Specifically, after confirming that at least one drive joint meets the braking and regeneration state criteria, a power calculation process is triggered for each identified joint. This calculation process uses the joint output torque and joint speed fed back in real time by each drive joint in this state as the core input parameters. Based on the specific physical relationship characterizing the motor's power generation characteristics (i.e., the conversion relationship between output torque, speed and electric power), the instantaneous value of the recoverable electric power generated by each joint combining its mechanical load torque with reverse motion at the current moment is calculated one by one. This value is defined as the braking and regeneration power of that joint.

[0056] For example, the calculation method for regenerative braking power can be expressed as follows:

[0057]

[0058] In the formula, This indicates the regenerative braking power of a specific drive joint. This indicates the output torque of the drive joint. This indicates the rotational speed of the drive joint.

[0059] (2) In response to the detection that the upper arm is in a state of gravitational potential energy release, the gravitational potential energy release power of the upper arm is determined based on the attitude angle, motion acceleration and real-time mass parameters of the upper arm and the load.

[0060] Among them, gravitational potential energy release power refers to the instantaneous power value of the gravitational potential energy released when the center of gravity of the boom and its load decreases in height, which is effectively converted into electrical energy by the drive system per unit time.

[0061] Specifically, once the boom meets the gravitational potential energy release criterion, the power calculation process for the boom is immediately triggered. This calculation process uses the boom attitude angle (i.e., pitch angle), boom motion acceleration (used to solve for linear velocity through a kinematic model), and the total mass parameters of the boom and load obtained in real time through an online load identification algorithm as core inputs. Based on the conversion model between the rate of change of gravitational potential energy and electrical power, the instantaneous power value that can be effectively converted into electrical energy by the drive system from the potential energy released by the center of gravity of the boom and its load during the descent of the boom and its load per unit time is calculated. This value is defined as the gravitational potential energy release power of the boom.

[0062] For example, the calculation method for the gravitational potential energy release power of the upper arm can be expressed as follows:

[0063]

[0064] In the formula, This represents the gravitational potential energy release power of the upper arm at a certain driving joint. This indicates the real-time mass parameters of the boom and load. This represents the distance from the center of mass of the upper arm to the axis of joint rotation. Indicates the attitude angle of the upper arm. This indicates the linear velocity at the end of the upper arm, calculated based on the arm's acceleration.

[0065] In this embodiment, the real-time mass parameters of the boom and the load, and the distance from the boom's center of mass to the joint rotation axis are determined as follows: the boom is adjusted to a horizontal reference position, and the absolute encoder zero point is set; the total mass of the boom and the load is calculated in real time through a load identification algorithm, and the distance from the boom's center of mass to the joint rotation axis is determined in combination with mechanical structure parameters, providing an accurate parameter basis for the calculation of the boom's gravitational potential energy.

[0066] (3) Determine the total output energy to be recovered based on each braking regeneration power and / or gravitational potential energy release power.

[0067] In this embodiment, the determination of the total output energy to be recovered is discussed in three cases: The first case is when only at least one drive joint is detected to be in a braking power generation state, in which case the total output energy to be recovered is determined as the arithmetic sum of the braking regeneration power of all drive joints in the braking power generation state; the second case is when only the upper arm is detected to be in a gravitational potential energy release state, in which case the total output energy to be recovered is determined as the gravitational potential energy release power of the upper arm; the third case is when at least one drive joint is detected to be in a braking power generation state and the upper arm is detected to be in a gravitational potential energy release state, in which case the total output energy to be recovered is determined as the arithmetic sum of all the above-mentioned braking regeneration power and gravitational potential energy release power. This result represents the total electrical power that the system can capture at the current moment in the composite energy recovery scenario.

[0068] Based on the above embodiments, optionally, the specific steps for determining the total energy required for the operation of a multi-axis robot may include:

[0069] (1) Based on the real-time collected joint rotation speed data and joint output torque data, the first drive joint in the energy consumption state is identified.

[0070] In this context, "energy consumption state" refers to a working state in which the motor rotation direction of a certain drive joint of a multi-axis robot is the same as the direction of its output torque, indicating that the joint is consuming electrical energy to output mechanical work to drive the load's movement. The first drive joint is the drive joint that is identified as being in the energy consumption state.

[0071] Specifically, within each control cycle, for each drive joint of the multi-axis robot, when the logic determines that the rotation direction represented by the current joint speed data is the same as the load torque direction represented by the joint output torque data, it is determined that the motor of that joint is in electric mode that consumes electrical energy to generate torque in the same direction to drive mechanical motion. Then, the joint is marked and classified as being in an energy consumption state. All these marked joints are collectively referred to as the first drive joint in the context of this step.

[0072] (2) For each first drive joint, based on the output torque and speed of the current first drive joint, determine the required power corresponding to the current first drive joint.

[0073] Here, the current first drive joint refers to any specific drive joint in an energy-consuming state that is being processed one by one. Required power refers to the instantaneous electrical power consumed by a particular drive joint at the current moment to maintain its movement and load. The sum of the required power of all first drive joints constitutes the total required energy.

[0074] Specifically, the processing procedure for each first drive joint is consistent. For the specific first drive joint being processed, the joint output torque and joint speed fed back in real time are used as the core input parameters. Based on the specific physical relationship that characterizes the conversion of electrical energy into mechanical energy by the motor in electric state, the instantaneous electrical power value that the joint needs to draw from the power source per unit time to maintain its current load torque and rotational motion is calculated. This calculated power value is defined as the required power corresponding to the current first drive joint.

[0075] (3) Determine the total energy demand based on the sum of the power demanded by each power demand.

[0076] In this embodiment, the power demand values ​​calculated at the same time for all the first drive joints are summed arithmetically, and the resulting total power value is defined as the total energy demand required for the operation of the multi-axis robot. It represents the overall instantaneous rate of electrical energy consumption required by the entire robot system to maintain the normal operation of all drive joints of the multi-axis robot under workload in the current control cycle.

[0077] S120: Based on the total output energy to be recovered, the total energy demand, the current available storage capacity and health status of the flywheel energy storage unit and the supercapacitor unit, perform multi-objective optimization decision-making to determine the energy allocation ratio between the flywheel energy storage unit and the supercapacitor unit.

[0078] The currently available storage capacity of a flywheel energy storage unit refers to the difference between its maximum designed storage capacity and the currently stored energy at any given time. It represents the remaining capacity of the unit to accommodate and store recoverable energy. The health status of a flywheel energy storage unit is a quantified percentage value derived from a comprehensive evaluation of multiple parameters, including its operating history (e.g., cycle count), critical component operating conditions (e.g., bearing temperature), and system integrity (e.g., vacuum level). It characterizes the unit's performance retention and aging degradation relative to its initial state, reflecting its reliability and expected lifespan.

[0079] The currently available storage capacity of a supercapacitor cell refers to the difference between its rated maximum charge storage capacity and its currently stored charge capacity at any given time, representing the remaining capacity of the cell to accept charge (electrical energy). The health status of a supercapacitor cell is a quantified percentage calculated using a bottleneck constraint model, taking into account key indicators such as capacity retention rate, internal resistance degradation, and thermal aging history. It characterizes the cell's degradation relative to its initial performance and its current usable performance level.

[0080] Among them, the energy allocation ratio is a set of normalized weighted coefficients used to accurately divide the flow of the total output energy to be recovered. This set of ratios contains two interrelated and complementary sub-ratios: the first sub-ratio defines the share of the total output energy to be recovered that should be allocated to the flywheel energy storage unit, and the second sub-ratio defines the share of the total output energy to be recovered that should be allocated to the supercapacitor unit.

[0081] Specifically, the total output energy to be recovered and the total energy demand can be calculated in real time as the basic inputs reflecting the global energy supply and demand situation. At the same time, the current available storage capacity and health status of the flywheel energy storage unit and the supercapacitor unit are combined to characterize the real-time acceptance capability and long-term reliability of the two energy storage units. By combining the above multi-dimensional parameters and under the mathematical model framework that considers multiple optimization objectives such as overall recovery efficiency, energy storage equipment life balance and system response speed, the optimization algorithm is used to solve and make decisions in real time. Finally, the optimal ratio of the total output energy to be recovered to be distributed to the flywheel energy storage unit and the supercapacitor unit under the current operating conditions is calculated, i.e., the energy allocation ratio is determined.

[0082] S130. Based on the energy allocation ratio, the energy to be recovered, corresponding to the total output energy to be recovered, is stored in the flywheel energy storage unit and the supercapacitor unit respectively.

[0083] Among them, the energy to be recovered refers to the total amount of electrical energy that can be captured and stored during the actual continuous process, which is represented by the instantaneous power of the total output energy to be recovered, after the multi-axis robot is detected to meet the energy recovery conditions, within one control cycle or a period of time.

[0084] In this embodiment, after obtaining the energy allocation ratio, precise control commands are generated accordingly to perform real-time diversion and storage control on the temporally continuous energy flow generated by the total output energy to be recovered. Specifically, according to the share specified in the energy allocation ratio, a portion of the energy to be recovered can be converted into high-speed rotating kinetic energy by controlling a bidirectional power converter and a permanent magnet synchronous motor in a constant-power acceleration manner, and stored in the flywheel energy storage unit. At the same time, another portion of the energy to be recovered can be converted into electric field energy by controlling a bidirectional DC / DC converter in a constant-current charging manner, and stored in the supercapacitor unit, thereby fully realizing the coordinated and optimized storage of the energy to be recovered between the two heterogeneous energy storage units.

[0085] This application provides an energy recovery control method for a multi-axis robot. The multi-axis robot has a large arm that carries the main load and is capable of pitching motion. The large arm is driven by at least one drive joint. The multi-axis robot is equipped with a flywheel energy storage unit and a supercapacitor unit for recovering and storing energy. The method includes: when the multi-axis robot meets the energy recovery conditions based on real-time collected joint motion state data and arm motion state data, determining the total output energy to be recovered and the total energy required for the operation of the multi-axis robot; then, based on the total output energy to be recovered, the total energy required, the current available storage capacity and health status of the flywheel energy storage unit and the supercapacitor unit, performing multi-objective optimization decision-making to determine the energy allocation ratio between the flywheel energy storage unit and the supercapacitor unit; and then, based on the energy allocation ratio, storing the energy to be recovered corresponding to the total output energy to be recovered into the flywheel energy storage unit and the supercapacitor unit respectively. The technical solution of this application expands the energy source by integrating joint braking energy and arm gravitational potential energy, and introduces a composite energy storage system composed of a flywheel and a supercapacitor as the execution carrier. Combined with multi-objective optimization decision-making based on real-time supply and demand status and energy storage unit health, an energy allocation ratio is generated, realizing the coordinated recovery and optimized storage of multi-source, dynamically regenerated energy. This solves the problems of poor dynamic recovery power adaptability and lack of coordinated management of multi-source energy flow in existing energy storage methods, and improves the overall energy efficiency and response speed of the multi-axis robot energy recovery system.

[0086] As one embodiment, the energy recovery control method provided in this application can be implemented using a layered architecture. Figure 2 This is a schematic diagram of a layered architecture for the method, such as... Figure 2 As shown, the architecture mainly consists of a perception layer, an allocation layer, and a control layer that are logically connected in sequence. This three-layer collaborative architecture enables the coordinated recovery and optimized utilization of energy across multiple joints.

[0087] Specifically, the perception layer is responsible for real-time acquisition of the multi-axis robot's energy state data, which further includes a joint braking energy detection module and an upper arm gravitational potential energy monitoring module. The joint braking energy detection module typically consists of Hall effect current sensors, incremental encoders, and strain gauge torque sensors deployed at the drive ends of each joint to acquire joint current, rotational speed, and torque information. The upper arm gravitational potential energy monitoring module consists of an absolute encoder installed at the upper arm joint and a triaxial accelerometer located at the upper arm's center of mass to acquire upper arm angle and motion acceleration information. The perception data from both modules is transmitted via shielded cables or a fieldbus to a unified data acquisition unit for aggregation and preprocessing. In this example, data from each module can also be synchronously transmitted via an industrial fieldbus at a 1kHz sampling frequency, using a unified timestamp to ensure data synchronization, and a Kalman filter algorithm is applied to fuse the data from the joint braking energy detection module and the upper arm gravitational potential energy monitoring module, improving the accuracy and reliability of the monitoring data.

[0088] The allocation layer is responsible for receiving fused data from the perception layer and executing intelligent decisions. Its core is the power allocation strategy module, which dynamically calculates and outputs the optimal energy allocation ratio between the flywheel energy storage unit and the supercapacitor unit based on multi-dimensional information such as real-time collected joint motion state, arm posture, total system energy demand, and real-time status of energy storage units (such as available storage capacity and health status) through a built-in multi-objective optimization algorithm. This achieves the best balance between system efficiency, equipment lifespan, and response speed.

[0089] The control layer, as the final execution unit, is responsible for translating the decision instructions from the allocation layer into precise control of the hardware devices. Its core is the energy recovery actuator, which physically comprises a flywheel energy storage unit, a supercapacitor unit, a bidirectional power converter connected to the DC bus, and a permanent magnet synchronous motor (or reluctance motor) driving the flywheel, forming a composite energy storage system. The robot's multi-joint cooperative controller is electrically connected to this energy recovery actuator and generates corresponding control signals based on the energy allocation ratio issued by the allocation layer. These signals drive the bidirectional DC / DC converter to perform constant current charging of the supercapacitor and control the bidirectional power converter and the permanent magnet synchronous motor to perform constant power charging and discharging of the flywheel, thereby collaboratively completing energy recovery and storage.

[0090] In this embodiment, by adopting the aforementioned layered architecture of sensing, allocation, and control working in tandem, the sensing layer first expands and integrates data from two core energy sources: joint braking energy and upper arm gravitational potential energy. Secondly, the allocation layer formulates refined energy management strategies based on global real-time operating conditions. Finally, the control layer drives a heterogeneous composite energy storage system composed of a flywheel and a supercapacitor to perform energy recovery. This method effectively solves the problems of single energy source, poor adaptability between energy storage methods and dynamic recovery power, and lack of coordinated management of multi-joint energy flow in existing energy recovery methods, ultimately achieving a significant improvement in the overall energy recycling efficiency of multi-axis robots.

[0091] Example 2

[0092] Figure 3 This is a schematic diagram of an energy recovery control method for a multi-axis robot provided in this application embodiment. Based on the foregoing embodiments, this embodiment will provide a detailed description of the specific implementation of S120 and S130. For specific implementation methods, please refer to the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0093] like Figure 3 As shown, the method specifically includes the following steps:

[0094] S210. When the multi-axis robot meets the energy recovery conditions based on the real-time collected joint motion state data and arm motion state data, the total output energy to be recovered and the total energy required for the operation of the multi-axis robot are determined.

[0095] S220. Obtain a pre-constructed comprehensive objective function; wherein the comprehensive objective function includes at least an efficiency function term for improving overall recovery efficiency, a health function term for balancing the lifespan degradation of flywheel energy storage units and supercapacitor units, and a response speed function term for shortening energy distribution delay.

[0096] The comprehensive objective function is a mathematical framework constructed to solve for the energy allocation ratio. Its role is to quantify multiple optimization objectives into a single-valued function with an extremum, serving as the core mathematical model for multi-objective optimization decision-making. The efficiency function term is a component of the comprehensive objective function; its mathematical model aims to quantify and maximize the overall efficiency of the total energy conversion process from the total output energy to be recovered to the final energy storage in the flywheel energy storage unit and supercapacitor unit. The health function term is another component of the comprehensive objective function; its mathematical model aims to quantify and promote the balanced maintenance of the health status of both the flywheel energy storage unit and the supercapacitor unit, thereby optimizing the long-term reliability of the system. The response speed function term is yet another component of the comprehensive objective function; its mathematical model aims to quantify and minimize the time delay of the entire process from identifying energy recovery to completing the corresponding energy allocation and primary storage, thereby improving the system's adaptability to rapidly changing operating conditions.

[0097] Specifically, the comprehensive objective function is a mathematical expression composed of efficiency, health, and response speed functions, among others, weighted by specific coefficients. This expression can be represented as:

[0098]

[0099] In the formula, , , These are configurable weighting coefficients, corresponding to the optimization priorities of the efficiency function, health function, and response speed function, respectively.

[0100] Let be the efficiency function term, and its value is... , This refers to the energy proportion factor allocated to the flywheel energy storage unit. This refers to the energy ratio factor allocated to the supercapacitor unit. This refers to the charging and discharging efficiency of the flywheel energy storage unit. This refers to the charging and discharging efficiency of a supercapacitor cell;

[0101] The average health status of the energy storage unit is represented by the value of [value missing]. ; This indicates the health status of the flywheel energy storage unit. Health status of the supercapacitor unit;

[0102] This represents the system response time, a variable that indicates the time required for the system to complete energy allocation adjustments from detecting energy recovery. It is modeled as the supercapacitor allocation ratio. It is a function of the rate of change of total input energy (dP_total / dt). Supercapacitors have a fast response, therefore their distribution ratio... The higher the level, the better for the overall system. The shorter the length, the more drastic the change in input power (the larger the dP total / dt), and the higher the requirement for fast response capability.

[0103] S230: Based on the real-time rate of change of the total output energy to be recovered, the health status of the flywheel energy storage unit, and the current load power of the multi-axis robot, generate pre-constraint rules for the energy allocation ratio.

[0104] The real-time rate of change of the total output energy to be recovered refers to a first-order differential physical quantity characterizing the speed and trend of the change of the total output energy to be recovered over time. It quantifies whether the recoverable power is rising rapidly, falling rapidly, or remaining stable. The current load power of the multi-axis robot refers to the instantaneous total electrical power consumed by all its drive joints and auxiliary systems to perform the work task at the current moment, excluding energy recovery-related actions. Pre-constraint rules are mandatory boundary conditions or logical restrictions on the feasible region of energy allocation ratios. Their function is to ensure that the optimization results first meet the basic requirements of system safety, equipment protection, and emergency response.

[0105] Specifically, before invoking the comprehensive objective function for refined optimization, a set of prerequisite constraints on the feasible range of energy allocation ratios are first established based on several key real-time state parameters. Specifically, the intensity and urgency of the energy feedback process are determined by analyzing the real-time rate of change of the total output energy to be recovered; the ability and reliability to withstand high loads are determined by assessing the health status of the flywheel energy storage unit; and the overall load level of the system is determined by monitoring the current load power of the multi-axis robot. Based on these analytical results, mandatory rules are directly formulated, such as setting minimum or maximum limits for the allocation ratio of the flywheel energy storage unit or supercapacitor unit. This ensures that the energy allocation ratio obtained in subsequent optimizations first meets fundamental and priority engineering constraints such as system dynamic response, protection of critical equipment, and stability under high load conditions.

[0106] In this embodiment, optionally, the specific implementation of generating pre-constraint rules for energy allocation ratio based on the real-time rate of change of the total output energy to be recovered, the health status of the flywheel energy storage unit, and the current load power of the multi-axis robot may include: setting a lower limit for the energy allocation ratio of the supercapacitor unit when the rate of change of the total output energy to be recovered exceeds a first preset threshold; setting a lower limit for the energy allocation ratio of the flywheel energy storage unit when the rate of change of the total output energy to be recovered is lower than a second preset threshold; limiting the upper limit for the energy allocation ratio of the flywheel energy storage unit when the health status of the flywheel energy storage unit is lower than a preset health threshold; and setting a minimum energy allocation ratio for the supercapacitor unit when the current load power of the multi-axis robot exceeds a ratio threshold of the rated power.

[0107] In this embodiment, the specific implementation of the pre-constraint rule can be based on four logical conditions to set mandatory boundaries for the energy allocation ratio: First, if the rate of change of the total output energy to be recovered exceeds a first preset threshold, the energy allocation ratio of the supercapacitor unit is forcibly stipulated not to be lower than a certain set lower limit to ensure sufficient rapid absorption capacity for rapidly changing energy flows; Second, if the rate of change of the total output energy to be recovered is lower than a second preset threshold, the energy allocation ratio of the flywheel energy storage unit is forcibly stipulated not to be lower than a certain set lower limit to guide stable energy to be allocated to a medium more suitable for long-term storage; Third, if the health status of the flywheel energy storage unit is lower than a preset health threshold, its energy allocation ratio is forcibly stipulated not to exceed a certain reduced upper limit to protect the device whose performance has degraded from overload damage; Fourth, if the current load power of the multi-axis robot exceeds the rated power ratio threshold, the energy allocation ratio of the supercapacitor unit must reach a minimum value to utilize its rapid discharge characteristics to ensure the immediate supply capacity of the system power demand under high load.

[0108] For example, a specific implementation of generating pre-constraint rules includes: when the rate of change of the total output energy to be recovered is greater than 2000 watts per second, setting a lower limit of no less than 1.2 times its baseline value for the allocation ratio of the supercapacitor unit; when the rate of change of the total output energy to be recovered is less than 500 watts per second, setting a lower limit of no less than 1.1 times its baseline value for the allocation ratio of the flywheel energy storage unit; when the health status of the flywheel energy storage unit is less than 80%, limiting the upper limit of the allocation ratio of the flywheel energy storage unit to 90% of its original calculated value; when the current load power of the multi-axis robot exceeds 80% of its rated power, mandating that the allocation ratio of the supercapacitor unit shall not be less than 0.6.

[0109] S240. Under the premise of satisfying the pre-constraint rules, with the total energy demand, the current available storage capacity and health status of each flywheel energy storage unit and supercapacitor unit as inputs, optimize the comprehensive objective function and solve for the energy allocation ratio of each flywheel energy storage unit and supercapacitor unit.

[0110] Specifically, under the premise of ensuring that the candidate energy allocation ratios do not violate the safety and performance boundaries defined by the pre-constraint rules, within this limited feasible solution range, the total demand energy reflecting the real-time power demand of the system, the current available storage capacity and health status of the flywheel energy storage units and the supercapacitor units (characterizing the current acceptance capacity of each energy storage medium) are all substituted into the pre-constructed mathematical model of the comprehensive objective function as key parameters. The model is then solved using an optimization algorithm to find a set of specific energy allocation ratios for the flywheel energy storage units and the supercapacitor units that simultaneously and optimally balance multiple objectives such as overall efficiency, equipment lifespan, and response speed. Thus, the optimal energy allocation ratio for the flywheel energy storage units can be output in real time. Optimal energy distribution ratio with supercapacitor cells And satisfy .

[0111] S250: Based on the portion of the energy allocated to the supercapacitor unit in the energy distribution ratio, generate a first energy storage control command to execute the supercapacitor charging operation.

[0112] Specifically, based on the energy allocation ratio determined by multi-objective optimization decision-making, which specifies the share that should be accepted by the supercapacitor unit, this ratio can be converted into a series of specific electronic instructions that can drive hardware actions. The contents of these first energy storage control instructions include: setting the bidirectional DC / DC converter connected to the supercapacitor unit to buck mode; calculating and setting a safe charging voltage that matches the current terminal voltage of the supercapacitor unit; monitoring the state of charge and temperature of the supercapacitor unit in real time and dynamically setting its maximum allowable charging current accordingly; and finally, dynamically adjusting the switching duty cycle of the converter through a closed-loop control algorithm to implement constant current charging of the supercapacitor unit with the maximum allowable charging current as the target, thereby accurately completing the storage task of the energy to be recovered allocated to the unit.

[0113] Based on the above embodiments, optionally, the specific implementation steps for performing the supercapacitor charging operation may include:

[0114] (1) Control the bidirectional DC / DC converter connected to the supercapacitor unit to operate in buck mode.

[0115] Specifically, specific mode commands and parameters can be sent to the bidirectional DC / DC converter to cause its internal power switching devices to operate according to the timing logic of the buck topology. This allows the high-voltage regenerative energy received by the converter from the DC bus to be converted into a lower DC charging voltage that matches the current actual terminal voltage of the supercapacitor unit and is within its safe range through the filtering effect of high-frequency switches, inductors, and capacitors. This process is the primary circuit control action to ensure that the high-voltage regenerative energy from the robot can flow safely and efficiently into the supercapacitor unit for storage.

[0116] (2) Determine the required charging voltage for the supercapacitor cell based on the current terminal voltage of the supercapacitor cell.

[0117] Specifically, the instantaneous voltage value between the two poles of the supercapacitor unit can be sampled and read in real time. Using this as the core input, the target voltage value that the bidirectional DC / DC converter needs to accurately establish at its output terminal in order to allow energy to flow from the DC bus into the supercapacitor unit within the current control cycle can be calculated through a predetermined charging strategy or algorithm. This target voltage value usually needs to be slightly higher than the current terminal voltage of the supercapacitor unit to form a positive voltage difference to drive the charging current. At the same time, it must be strictly limited within its maximum rated voltage range to ensure safety. This determination process is the basis for achieving efficient and controllable constant current charging.

[0118] (3) Based on the real-time state of charge and temperature of the supercapacitor cell, the current maximum allowable charging current of the supercapacitor cell is dynamically determined.

[0119] Specifically, firstly, the instantaneous state of charge percentage and real-time internal or surface temperature of the supercapacitor cell during charging can be obtained through sensors or estimation algorithms. Then, based on the electrochemical characteristics and thermal safety model of the supercapacitor cell, these two dynamic parameters are input as key variables into a predefined calculation relationship or lookup table program. This allows for real-time calculation and output of the instantaneous charging current limit value that maximizes the charging speed without causing overcharging, overheating, or other damage to the safety or lifespan of the supercapacitor cell under the current specific state of charge and temperature conditions. This limit value is the dynamically determined current maximum allowable charging current and is the core setting target for subsequent closed-loop constant current charging control.

[0120] (4) With the maximum allowable charging current as the target, the switching duty cycle of the bidirectional DC / DC converter is dynamically adjusted by closed-loop proportional-integral-derivative control to achieve constant current charging of the supercapacitor unit.

[0121] Specifically, the dynamically determined maximum allowable charging current can be used as a constant current reference value input into a closed-loop control loop. The core controller of this loop employs a proportional-integral-derivative (PID) algorithm. This controller continuously samples the actual charging current output from the bidirectional DC / DC converter to the supercapacitor unit and compares it with the set maximum allowable charging current reference value. Based on the magnitude of the deviation, the cumulative history of the deviation, and the trend of the deviation, the PID algorithm calculates the control quantity that needs adjustment in real time. This control quantity is then converted into a real-time varying pulse-width modulation (PWM) signal, dynamically adjusting the on- and off-time ratio of the power switching devices in the bidirectional DC / DC converter. This precisely controls the converter's output power, ensuring that the actual charging current stably follows and equals the maximum allowable charging current, achieving fast, safe, and efficient constant-current charging of the supercapacitor unit.

[0122] S260. Based on the portion of the energy allocated to the flywheel energy storage unit in the energy distribution ratio, generate a second energy storage control command to execute the flywheel energy storage operation.

[0123] Specifically, based on the energy allocation ratio determined by multi-objective optimization decision-making, which explicitly specifies the share of energy to be accepted by the flywheel energy storage unit, this ratio can be converted into another set of specific electronic commands that drive the flywheel system hardware. The core content of these second energy storage control commands includes: setting the bidirectional power converter and permanent magnet synchronous motor connected to the flywheel energy storage unit to enter a constant power operation mode; issuing energy storage commands to the system; controlling the permanent magnet synchronous motor to accelerate rotation with a constant electrical power input; thereby converting the electrical energy allocated to the unit into kinetic energy stored in the high-speed rotating flywheel; and using a speed-current dual closed-loop control structure to precisely control the torque and current of this accelerated energy storage process, thereby accurately completing the task of storing the energy to be recovered allocated to the flywheel energy storage unit in the form of kinetic energy.

[0124] Based on the above embodiments, optionally, the specific implementation steps for performing flywheel energy storage operations may include:

[0125] (1) Control the bidirectional power converter and permanent magnet synchronous motor connected to the flywheel energy storage unit so that the bidirectional power converter and permanent magnet synchronous motor operate in constant power mode.

[0126] Among them, the bidirectional power converter is a power electronic device that enables bidirectional flow of electrical energy. It acts as an interface and regulator between the DC bus and the drive motor of the flywheel energy storage unit, possessing the dual functions of converting electrical energy on the DC bus into AC energy suitable for the drive motor for energy storage, and rectifying the AC energy generated by the motor back to the DC bus. The magnetic synchronous motor is a synchronous motor that uses permanent magnets to establish an excitation magnetic field. In this scheme, it serves as the drive and power generation execution component of the flywheel energy storage unit, mechanically coaxially connected to the flywheel rotor. In motor mode, it converts the input electrical energy into mechanical energy to drive the flywheel to accelerate and store energy. In power generation mode, it converts the mechanical energy released by the flywheel as it decelerates into electrical energy feedback to the system.

[0127] Specifically, coordinated control commands can be issued to the bidirectional power converter and permanent magnet synchronous motor that constitute the flywheel drive system, setting the overall operating mode of the combined system to maintain constant power exchange. In this mode, regardless of how the speed of the flywheel changes, the control system adjusts the electromagnetic torque of the permanent magnet synchronous motor to keep the instantaneous power input from the DC bus to the bidirectional power converter and finally transmitted to the permanent magnet synchronous motor (or conversely, the instantaneous power fed back from the motor to the bus) a constant value determined by the energy distribution ratio and the total output energy to be recovered. This achieves precise energy flow control for constant power accelerated charging or constant power decelerated discharging of the flywheel.

[0128] (2) According to the second energy storage control command, the permanent magnet synchronous motor is controlled to accelerate at constant power so as to convert the input electrical energy into kinetic energy and store it in the flywheel energy storage unit.

[0129] Specifically, after receiving and parsing the second energy storage control command, the permanent magnet synchronous motor is precisely controlled to operate according to the storage intent and specified power level contained in the command. Specifically, it drives the permanent magnet synchronous motor into an acceleration state and dynamically adjusts the electromagnetic torque of the motor through a closed-loop control algorithm to ensure that the instantaneous electrical power drawn by the motor from the bidirectional power converter remains strictly constant. This allows the permanent magnet synchronous motor to drive the coaxial flywheel rotor to receive electrical energy input at a constant power and continuously convert this electrical energy into incremental flywheel rotational kinetic energy, realizing the physical process of efficiently storing a specified share of the electrical energy to be recovered in the form of mechanical kinetic energy in the flywheel energy storage unit.

[0130] In this embodiment, optionally, the energy storage process of the permanent magnet synchronous motor can be precisely controlled by a speed-current dual closed-loop control structure; wherein, the outer loop of the permanent magnet synchronous motor is a speed control loop, which generates torque commands based on the target speed through proportional-integral-derivative control, and the inner loop of the permanent magnet synchronous motor is a current control loop, which generates pulse width modulation drive signals based on the torque commands through a field-oriented control algorithm.

[0131] The technical solution of this application, when determining the energy allocation ratio between the flywheel energy storage unit and the supercapacitor unit, introduces a pre-constructed comprehensive objective function that integrates multiple objectives such as efficiency, lifespan, and response speed as the optimization core. First, it uses key operating parameters such as the real-time rate of change of the total output energy to be recovered, the health status of the flywheel energy storage unit, and the current load power of the multi-axis robot to generate mandatory pre-constraint rules, setting safety and performance baselines for optimization. Based on this, it uses the total demand energy reflecting real-time supply and demand, as well as the current available storage capacity and health status of the flywheel energy storage unit and the supercapacitor unit (characterizing the real-time acceptance capacity and reliability of the energy storage unit) as dynamic inputs to perform real-time optimization of the comprehensive objective function within the constraints. This allows for the dynamic and intelligent determination of the energy allocation ratio between the flywheel energy storage unit and the supercapacitor unit. This achieves globally optimal allocation of robot-recovered energy while ensuring system safety response and equipment protection, maximizing overall system efficiency, balancing energy storage unit lifespan decay, and minimizing allocation delay, ultimately significantly improving the overall energy efficiency, economy, and dynamic response performance of the multi-axis robot energy recovery system.

[0132] As one embodiment, the energy recovery control method provided in this application can be integrated and deployed in a dedicated energy recovery control cabinet, which is electrically connected to a multi-axis robot to form a complete energy recovery system. Figure 4 This is a schematic diagram of an exemplary structure of the energy recovery control cabinet, such as... Figure 4 As shown, the control cabinet integrates: a flywheel energy storage unit and a supercapacitor unit as the core energy storage medium; a bidirectional power converter (including a grid-side converter for the DC bus interface and a machine-side converter for driving the flywheel motor) for bidirectional energy flow and voltage level conversion; a simple operation panel providing a human-machine interface; and a heat dissipation module to ensure stable operation of the equipment within the cabinet. The control method is implemented through software embedded in the control cabinet or the robot's main controller. This software, through real-time execution of sensing, distribution, and control steps, collaboratively manages the flywheel energy storage unit and supercapacitor unit within the cabinet, completing the recovery, storage, and reuse of multi-source regenerative energy generated during robot movement.

[0133] Next, we will introduce the energy recovery control method for multi-axis robots provided by the embodiments of the present invention using a specific implementation method.

[0134] For example, this solution can be applied to a 7-axis heavy-duty handling robot with a load capacity of 100 kg, a main arm mass of 50 kg, a distance of 1.2 m from the main arm's center of mass to the joint rotation axis, and a rated DC bus voltage of 400 V. In implementation, Hall effect current sensors, incremental encoders, and strain gauge torque sensors are installed at the motor ends of each drive joint to collect joint motion state data; an absolute encoder is installed at the main arm joint rotation axis, and a three-axis accelerometer is arranged at the main arm's center of mass to collect arm motion state data. The energy storage section consists of a flywheel energy storage unit with a rated power of 5 kW and a maximum speed of 30,000 rpm, and a group of supercapacitor units consisting of four 3000 Farad, 2.7 V supercapacitor cells connected in series, with a total voltage of 10.8 V, connected to a 400 V bus via a bidirectional DC / DC converter, forming a composite energy storage system. The main controller is an embedded industrial controller.

[0135] When the robot performs a typical work cycle including grasping, lifting, translating, lowering, and placing, during the lowering phase, the upper arm moves from a pitch angle of 60 degrees to 30 degrees, while the end effector descends at a speed of 0.5 meters per second. At this time, the perception layer analyzes real-time data and determines that joints 3 and 5 are in a braking power generation state, with calculated braking regeneration power of 300 watts and 200 watts respectively; simultaneously, the upper arm is in a gravitational potential energy release state, with calculated gravitational potential energy release power of 764 watts. The system summarizes the total output energy to be recovered as 1264 watts. On the other hand, the system identifies joints such as joint 4 as being in an energy consumption state, calculating that the total energy required for the robot's current operation is 600 watts. Therefore, the system determines that there is a surplus of 664 watts of energy available for recovery and storage.

[0136] At the allocation layer, the system first obtains the status of the energy storage units: the flywheel energy storage unit has a current available storage capacity of 1200 joules, and its health status is evaluated as 0.82 (i.e., 82%) based on a model based on cycle count, bearing temperature, and vacuum level; the supercapacitor unit has a current available storage capacity of 500 joules, and its health status is evaluated as 0.836 (i.e., 83.6%) based on a model based on capacity retention rate, internal resistance degradation rate, and thermal aging factor. Subsequently, the system loads a pre-built comprehensive objective function, which includes an efficiency function term aimed at improving overall recovery efficiency, a health function term aimed at balancing the lifetime decay of the two energy storage units, and a response speed function term aimed at shortening system response latency. Before solving, the system generates pre-constraint rules based on the real-time rate of change of the total output energy to be recovered, the health status of the flywheel energy storage unit, and the robot's current load power (e.g., ensuring that the supercapacitor allocation ratio does not fall below a certain lower limit for rapid response when power changes drastically). Under the premise of satisfying these rules, the system takes the total energy demand (600 watts) and the available storage capacity and health status of the two energy storage units as inputs, and performs real-time optimization of the comprehensive objective function. Finally, the energy distribution ratio between the flywheel energy storage unit and the supercapacitor unit is: flywheel accounts for 0.45 and supercapacitor accounts for 0.55.

[0137] At the control layer, the system generates and executes specific energy storage commands based on this ratio. For the 0.55 share allocated to the supercapacitor unit (corresponding to approximately 365.2 watts of power), the system controls the connected bidirectional DC / DC converter to operate in buck mode, determines the required charging voltage based on the current terminal voltage of the supercapacitor unit, and dynamically determines the current maximum allowable charging current based on its real-time state of charge and temperature. Subsequently, using this maximum allowable charging current as the set target, the system dynamically adjusts the switching duty cycle of the bidirectional DC / DC converter through closed-loop proportional-integral-derivative control to achieve rapid and safe constant current charging of the supercapacitor unit. For the 0.45 share allocated to the flywheel energy storage unit (corresponding to approximately 298.8 watts of power), the system controls the connected bidirectional power converter and permanent magnet synchronous motor to operate in constant power mode. According to the energy storage command, the system controls the permanent magnet synchronous motor to accelerate at constant power, converting electrical energy into kinetic energy for storage. This process is precisely controlled through a speed-current dual closed-loop structure. The outer loop (speed loop) generates torque commands based on the target speed, and the inner loop (current loop) generates pulse width modulation drive signals based on these commands, thereby achieving precise control of the flywheel energy storage process.

[0138] This embodiment demonstrates how, in a specific moment of operation, multi-source sensing of braking energy and gravitational potential energy of a multi-axis robot, intelligent allocation decision based on multi-objective optimization and real-time status, and precise coordinated control of heterogeneous energy storage units can be achieved, ultimately resulting in efficient energy recovery and optimized utilization.

[0139] Example 3

[0140] Figure 5 This is a schematic diagram of an energy recovery control device for a multi-axis robot provided in an embodiment of this application. The multi-axis robot has a large arm that carries the main load and is capable of pitching motion. The large arm is driven by at least one drive joint. The multi-axis robot is equipped with a flywheel energy storage unit and a supercapacitor unit for recovering and storing energy. The device includes:

[0141] The energy recovery determination module 310 is used to determine the total output energy to be recovered and the total energy required for the operation of the multi-axis robot when the multi-axis robot is detected to meet the energy recovery conditions based on real-time collected joint motion state data and arm motion state data.

[0142] The energy allocation determination module 320 is used to perform multi-objective optimization decision-making based on the total output energy to be recovered, the total energy demand, the current available storage capacity and health status of the flywheel energy storage unit and the supercapacitor unit, and to determine the energy allocation ratio between the flywheel energy storage unit and the supercapacitor unit.

[0143] The energy distribution and recovery module 330 is used to store the energy to be recovered, corresponding to the total output energy to be recovered, into the flywheel energy storage unit and the supercapacitor unit, respectively, based on the energy distribution ratio.

[0144] This application provides an energy recovery control device for a multi-axis robot. The multi-axis robot has a large arm that carries the main load and is capable of pitching motion. The large arm is driven by at least one drive joint. The multi-axis robot is equipped with a flywheel energy storage unit and a supercapacitor unit for recovering and storing energy. The method includes: when the multi-axis robot meets the energy recovery conditions based on real-time collected joint motion state data and arm motion state data, determining the total output energy to be recovered and the total energy required for the operation of the multi-axis robot; then, based on the total output energy to be recovered, the total energy required, the current available storage capacity and health status of the flywheel energy storage unit and the supercapacitor unit, performing multi-objective optimization decision-making to determine the energy allocation ratio between the flywheel energy storage unit and the supercapacitor unit; and then, based on the energy allocation ratio, storing the energy to be recovered corresponding to the total output energy to be recovered into the flywheel energy storage unit and the supercapacitor unit respectively. The technical solution of this application expands the energy source by integrating joint braking energy and arm gravitational potential energy, and introduces a composite energy storage system composed of a flywheel and a supercapacitor as the execution carrier. Combined with multi-objective optimization decision-making based on real-time supply and demand status and energy storage unit health, an energy allocation ratio is generated, realizing the coordinated recovery and optimized storage of multi-source, dynamically regenerated energy. This solves the problems of poor dynamic recovery power adaptability and lack of coordinated management of multi-source energy flow in existing energy storage methods, and improves the overall energy efficiency and response speed of the multi-axis robot energy recovery system.

[0145] Based on the above-mentioned device, the energy recovery control device applied to the multi-axis robot may optionally include: a recovery condition satisfaction judgment module, used to detect whether the multi-axis robot meets the energy recovery conditions based on real-time collected joint motion state data and arm motion state data;

[0146] Based on the above-mentioned device, optionally, the joint motion state data includes joint rotation speed data and joint output torque data, and the arm motion state data includes upper arm pitch angle data and upper arm motion acceleration data. The recovery condition satisfaction judgment module is specifically used to determine whether at least one drive joint is in a braking and power generation state based on the real-time collected joint rotation speed data and joint output torque data; and to determine whether the upper arm is in a gravitational potential energy release state based on the real-time collected upper arm pitch angle data and upper arm motion acceleration data. When at least one drive joint is in a braking and power generation state, and / or the upper arm is in a gravitational potential energy release state, the multi-axis robot is determined to meet the energy recovery condition.

[0147] Based on the above-mentioned device, optionally, the energy recovery determination module 310 is specifically used to, in response to detecting that at least one drive joint is in a braking power generation state, determine the braking regeneration power of each drive joint in the braking power generation state based on the output torque and rotational speed of the drive joint; in response to detecting that the boom is in a gravitational potential energy release state, determine the gravitational potential energy release power of the boom based on the attitude angle, motion acceleration and real-time mass parameters of the boom and the load; and determine the total output energy to be recovered based on each of the braking regeneration power and / or the gravitational potential energy release power.

[0148] Based on the above-mentioned device, optionally, the energy recovery determination module 310 is further used to identify the first drive joint in an energy consumption state based on the real-time collected joint rotation speed data and joint output torque data; for each first drive joint, determine the required power corresponding to the current first drive joint based on the current output torque and rotation speed of the first drive joint; and determine the total required energy based on the sum of the required power of each joint.

[0149] Based on the above-mentioned device, optionally, the energy ratio determination module 320 includes:

[0150] The objective function construction unit is used to obtain a pre-constructed comprehensive objective function; wherein, the comprehensive objective function includes at least an efficiency function term for improving the overall recovery efficiency, a health function term for balancing the lifespan degradation of the flywheel energy storage unit and the supercapacitor unit, and a response speed function term for shortening the energy distribution delay;

[0151] The constraint rule determination unit is used to generate pre-constraint rules for the energy allocation ratio based on the real-time rate of change of the total output energy to be recovered, the health status of the flywheel energy storage unit, and the current load power of the multi-axis robot.

[0152] An energy allocation determination unit is used to optimize the comprehensive objective function under the premise of satisfying the pre-constraint rules, taking the total energy demand, the current available storage capacity and health status of the flywheel energy storage unit and the supercapacitor unit as inputs, and solve for the energy allocation ratio of the flywheel energy storage unit and the supercapacitor unit.

[0153] Based on the above device, optionally, a constraint rule determination unit is used to set a lower limit for the energy allocation ratio of the supercapacitor unit when the rate of change of the total output energy to be recovered exceeds a first preset threshold; set a lower limit for the energy allocation ratio of the flywheel energy storage unit when the rate of change of the total output energy to be recovered is lower than a second preset threshold; limit the upper limit for the energy allocation ratio of the flywheel energy storage unit when the health status of the flywheel energy storage unit is lower than a preset health threshold; and set a minimum energy allocation ratio for the supercapacitor unit when the current load power of the multi-axis robot exceeds a proportional threshold of the rated power.

[0154] Based on the above-mentioned device, optionally, the energy distribution and recovery module 330 includes:

[0155] A capacitor energy storage control unit is used to generate a first energy storage control command based on the portion of the energy allocation ratio allocated to the supercapacitor unit, so as to execute a supercapacitor charging operation.

[0156] The flywheel energy storage control unit is used to generate a second energy storage control command based on the portion of the energy allocation ratio allocated to the flywheel energy storage unit, so as to execute the flywheel energy storage operation.

[0157] Based on the above device, optionally, a capacitor energy storage control unit is used to control the bidirectional DC / DC converter connected to the supercapacitor unit to operate in buck mode; determine the required charging voltage for the supercapacitor unit based on the current terminal voltage of the supercapacitor unit; dynamically determine the current maximum allowable charging current of the supercapacitor unit based on the real-time state of charge and temperature of the supercapacitor unit; and dynamically adjust the switching duty cycle of the bidirectional DC / DC converter through closed-loop proportional-integral-derivative control with the maximum allowable charging current as the set target to achieve constant current charging of the supercapacitor unit.

[0158] Based on the above device, optionally, a flywheel energy storage control unit is used to control the bidirectional power converter and permanent magnet synchronous motor connected to the flywheel energy storage unit, so that the bidirectional power converter and permanent magnet synchronous motor operate in constant power mode; according to the second energy storage control command, the permanent magnet synchronous motor is controlled to accelerate at constant power so as to convert the input electrical energy into kinetic energy and store it in the flywheel energy storage unit.

[0159] The energy recovery control device for multi-axis robots provided in this application embodiment can execute the energy recovery control method for multi-axis robots provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the method execution.

[0160] It is worth noting that the various units and modules included in the above system are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of this application.

[0161] Example 4

[0162] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 6 A block diagram is shown of an exemplary electronic device 40 suitable for implementing embodiments of the present application. Figure 6 The electronic device 40 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0163] like Figure 6 As shown, electronic device 40 is represented in the form of a general-purpose computing device. The components of electronic device 40 may include, but are not limited to: one or more processors or processing units 401, system memory 402, and bus 403 connecting different system components (including system memory 402 and processing unit 401).

[0164] Bus 403 represents one or more of several bus architectures, including memory buses or memory electronics, peripheral buses, graphics acceleration ports, processors, or local buses using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0165] Electronic device 40 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 40, including volatile and non-volatile media, removable and non-removable media.

[0166] System memory 402 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 404 and / or cache memory 405. Electronic device 40 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 406 may be used to read and write non-removable, non-volatile magnetic media (… Figure 6 Not shown (usually referred to as a hard drive). Although Figure 6As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a floppy disk) and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 403 via one or more data media interfaces. Memory 402 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0167] A program / utility 408 having a set (at least one) of program modules 407 may be stored, for example, in memory 402. Such program modules 407 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 407 typically perform the functions and / or methods described in the embodiments of this application.

[0168] Electronic device 40 can also communicate with one or more external devices 409 (e.g., keyboard, pointing device, display 410, etc.), and with one or more devices that enable a user to interact with electronic device 40, and / or with any device that enables electronic device 40 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 411. Furthermore, electronic device 40 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 412. As shown, network adapter 412 communicates with other modules of electronic device 40 via bus 403. It should be understood that, although... Figure 6 Not shown, other hardware and / or software modules may be used in conjunction with electronic device 40, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0169] The processing unit 401 executes various functional applications and page processing by running programs stored in the system memory 402, such as implementing the energy recovery control method for multi-axis robots provided in the embodiments of this application.

[0170] Example 5

[0171] This application embodiment also provides a storage medium containing computer-executable instructions. When executed by a computer processor, the computer-executable instructions are used to execute an energy recovery control method applied to a multi-axis robot. The multi-axis robot has a large arm that carries the main load and is capable of pitch movement. The large arm is driven by at least one drive joint. The multi-axis robot is equipped with a flywheel energy storage unit and a supercapacitor unit for recovering and storing energy. The method includes:

[0172] When the multi-axis robot meets the energy recovery conditions based on the real-time collected joint motion state data and arm motion state data, the total output energy to be recovered and the total energy required for the operation of the multi-axis robot are determined.

[0173] Based on the total output energy to be recovered, the total energy demand, the current available storage capacity and health status of the flywheel energy storage unit and the supercapacitor unit, a multi-objective optimization decision is performed to determine the energy allocation ratio between the flywheel energy storage unit and the supercapacitor unit.

[0174] Based on the energy allocation ratio, the energy to be recovered, corresponding to the total output energy to be recovered, is stored in the flywheel energy storage unit and the supercapacitor unit, respectively.

[0175] The computer storage medium in this application embodiment can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0176] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0177] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0178] Computer program code for performing the operations of the embodiments of this application can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as C or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0179] Note that the above description is merely a preferred embodiment and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the concept of this application, and the scope of this application is determined by the scope of the appended claims.

Claims

1. An energy recovery control method for a multi-axis robot, the multi-axis robot having a large arm that bears the main load and is capable of pitching motion, the large arm being driven by at least one drive joint, the multi-axis robot being equipped with a flywheel energy storage unit and a supercapacitor unit for energy recovery and storage, characterized in that, include: When the multi-axis robot meets the energy recovery conditions based on the real-time collected joint motion state data and arm motion state data, the total output energy to be recovered and the total energy required for the operation of the multi-axis robot are determined. Based on the total output energy to be recovered, the total energy demand, the current available storage capacity and health status of the flywheel energy storage unit and the supercapacitor unit, a multi-objective optimization decision is performed to determine the energy allocation ratio between the flywheel energy storage unit and the supercapacitor unit. Based on the energy allocation ratio, the energy to be recovered, corresponding to the total output energy to be recovered, is stored in the flywheel energy storage unit and the supercapacitor unit, respectively.

2. The method according to claim 1, characterized in that, The method further includes: Based on real-time collected joint motion state data and arm motion state data, it is determined whether the multi-axis robot meets the energy recovery conditions. The joint motion state data includes joint rotation speed data and joint output torque data; the arm motion state data includes upper arm pitch angle data and upper arm acceleration data; the method of detecting whether the multi-axis robot meets the energy recovery conditions based on real-time acquired joint motion state data and arm motion state data includes: Based on the real-time collected joint rotation speed data and joint output torque data, it is determined whether at least one drive joint is in a braking and power generation state. Based on the real-time collected data on the pitch angle of the upper arm and the acceleration data of the upper arm, it is determined whether the upper arm is in a state of gravitational potential energy release. When at least one drive joint is in a braking and power generation state, and / or the upper arm is in a gravitational potential energy release state, the multi-axis robot is determined to meet the energy recovery conditions.

3. The method according to claim 1 or 2, characterized in that, The determination of the total output energy to be recovered includes: In response to the detection that at least one drive joint is in a braking power generation state, the braking regenerative power of each drive joint in the braking power generation state is determined based on the output torque and rotational speed of the drive joint. In response to the detection that the upper arm is in a state of gravitational potential energy release, the gravitational potential energy release power of the upper arm is determined based on the attitude angle, motion acceleration and real-time mass parameters of the upper arm and the load. The total output energy to be recovered is determined based on the braking regeneration power and / or the gravitational potential energy release power.

4. The method according to claim 1 or 2, characterized in that, Determining the total energy required for the operation of the multi-axis robot includes: Based on the real-time collected joint rotation speed data and joint output torque data, the first drive joint in an energy consumption state is identified; For each of the first drive joints, the required power corresponding to the current first drive joint is determined based on the output torque and rotational speed of the current first drive joint. The total energy demand is determined based on the sum of the power demands of each of the aforementioned demands.

5. The method according to claim 1, characterized in that, The process of performing multi-objective optimization decision-making based on the total output energy to be recovered, the total energy demand, and the current available storage capacity and health status of each of the flywheel energy storage unit and the supercapacitor unit to determine the energy allocation ratio between the flywheel energy storage unit and the supercapacitor unit includes: Obtain a pre-constructed comprehensive objective function; wherein the comprehensive objective function includes at least an efficiency function term for improving overall recovery efficiency, a health function term for balancing the lifespan degradation of the flywheel energy storage unit and the supercapacitor unit, and a response speed function term for shortening energy distribution delay; Based on the real-time rate of change of the total output energy to be recovered, the health status of the flywheel energy storage unit, and the current load power of the multi-axis robot, a pre-constraint rule for the energy allocation ratio is generated. Under the premise of satisfying the pre-constraint rules, the comprehensive objective function is optimized by taking the total energy demand, the current available storage capacity and health status of the flywheel energy storage unit and the supercapacitor unit as inputs, and the energy allocation ratio of the flywheel energy storage unit and the supercapacitor unit is obtained by solving.

6. The method according to claim 5, characterized in that, The pre-constraint rules for the energy allocation ratio are generated based on the real-time rate of change of the total output energy to be recovered, the health status of the flywheel energy storage unit, and the current load power of the multi-axis robot, including: When the rate of change of the total output energy to be recovered exceeds the first preset threshold, a lower limit for the energy allocation ratio of the supercapacitor unit is set. When the rate of change of the total output energy to be recovered is lower than the second preset threshold, a lower limit for the energy allocation ratio is set for the flywheel energy storage unit. When the health status of the flywheel energy storage unit is lower than a preset health threshold, the upper limit of the energy distribution ratio of the flywheel energy storage unit is limited. When the current load power of the multi-axis robot exceeds the rated power ratio threshold, the minimum energy allocation ratio is set for the supercapacitor unit.

7. The method according to claim 1, characterized in that, Based on the energy allocation ratio, the energy to be recovered, corresponding to the total output energy to be recovered, is stored in the flywheel energy storage unit and the supercapacitor unit, respectively, including: Based on the portion of the energy allocated to the supercapacitor unit in the energy allocation ratio, a first energy storage control command is generated to execute the supercapacitor charging operation; Based on the portion of the energy allocated to the flywheel energy storage unit in the energy distribution ratio, a second energy storage control command is generated to execute the flywheel energy storage operation.

8. The method according to claim 7, characterized in that, The supercapacitor charging operation includes: The bidirectional DC / DC converter connected to the supercapacitor unit is controlled to operate in buck mode; Based on the current terminal voltage of the supercapacitor unit, determine the required charging voltage for the supercapacitor unit; Based on the real-time state of charge and temperature of the supercapacitor unit, the current maximum allowable charging current of the supercapacitor unit is dynamically determined. Using the maximum allowable charging current as the target, the switching duty cycle of the bidirectional DC / DC converter is dynamically adjusted through closed-loop proportional-integral-derivative control to achieve constant current charging of the supercapacitor unit.

9. The method according to claim 7, characterized in that, The execution of flywheel energy storage operations includes: Control the bidirectional power converter and permanent magnet synchronous motor connected to the flywheel energy storage unit, so that the bidirectional power converter and permanent magnet synchronous motor operate in constant power mode; According to the second energy storage control command, the permanent magnet synchronous motor is controlled to accelerate at constant power so as to convert the input electrical energy into kinetic energy and store it in the flywheel energy storage unit.

10. An energy recovery control device for a multi-axis robot, the multi-axis robot having a large arm that bears the main load and is capable of pitching motion, the large arm being driven by at least one drive joint, the multi-axis robot being equipped with a flywheel energy storage unit and a supercapacitor unit for recovering and storing energy, characterized in that, The device includes: The energy recovery determination module is used to determine the total output energy to be recovered and the total energy required for the operation of the multi-axis robot when the multi-axis robot is detected to meet the energy recovery conditions based on real-time collected joint motion state data and arm motion state data. The energy allocation determination module is used to perform multi-objective optimization decision-making based on the total output energy to be recovered, the total energy demand, the current available storage capacity and health status of the flywheel energy storage unit and the supercapacitor unit, and to determine the energy allocation ratio between the flywheel energy storage unit and the supercapacitor unit. The energy distribution and recovery module is used to store the energy to be recovered, corresponding to the total output energy to be recovered, into the flywheel energy storage unit and the supercapacitor unit, respectively, based on the energy distribution ratio.

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