Optimization Method and System for Servo System Based on Algorithm Control

Abstract

The present invention provides a servo system optimization method and system based on algorithm control, which relates to motor control technology. First, a real-time dynamic response parameter set is obtained when the servo device is dynamically running, covering inertia delay, torque fluctuation, position deviation parameters, and a corresponding optimization requirement set, including optimization requirements for each parameter. Then, according to the optimization requirement priority, a multi-level control signal is generated for the real-time dynamic response parameter set, and a matching control signal sequence set such as the first, second, and third compensation signals is generated. Then, the control signal sequence set is input into an actuator to drive it to dynamically respond according to an updated trajectory. Finally, according to the feedback signal set generated by the actuator, the control signal sequence set is optimized, so that the servo device operating parameters can be fully considered, control signals can be generated in a targeted manner, dynamic adjustment and optimization can be achieved, and the servo system performance can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and in particular, to an optimization method and system for a servo system based on algorithm control. Background Art

[0002] In the fields of industrial automation and precision control, as a key actuator, the servo system is widely used in various high-precision motion control scenarios, such as numerical control machine tool machining, robot operation, aerospace equipment manufacturing, etc. The quality of its performance directly affects the quality of products, production efficiency, and the stability and reliability of equipment.

[0003] The existing servo system control methods lack systematicness and pertinence. They often adopt fixed control strategies without considering the optimization requirement differences of different parameters and cannot flexibly adjust control signals according to the actual situation. In terms of driving the actuator to move, it is difficult for the existing technology to achieve real-time dynamic adjustment of the actuator's motion trajectory. The actuator usually operates according to a preset fixed trajectory. When encountering external interference or working condition changes, it cannot update the motion trajectory in a timely manner according to actual needs, resulting in a decline in operating accuracy and inability to meet the requirements of complex and changeable work tasks.

[0004] Moreover, most of the existing control methods adopt open-loop control or simple feedback control methods. The open-loop control method cannot adjust control parameters according to the actual operating conditions of the system. Once interference or parameter changes occur, the system performance will decline sharply. Although simple feedback control can correct system deviations to a certain extent, it lacks a comprehensive optimization mechanism and cannot deeply optimize control signals according to the comprehensive influence of different parameters, making it difficult to ensure that the servo system is always in the best operating state. Summary of the Invention

[0005] In view of the above-mentioned problems, in combination with the first aspect of the present invention, embodiments of the present invention provide an optimization method for a servo system based on algorithm control, and the method includes:

[0006] Obtain a set of real-time dynamic response parameters generated during the dynamic operation of the servo device, and a set of optimization requirements corresponding to the servo device; wherein, the set of real-time dynamic response parameters includes an inertia delay parameter, a torque fluctuation parameter, and a position deviation parameter generated by the servo device when executing a target motion trajectory, and the set of optimization requirements includes a first optimization requirement for the inertia delay parameter, a second optimization requirement for the torque fluctuation parameter, and a third optimization requirement for the position deviation parameter;

[0007] According to the demand priority corresponding to each optimization demand in the optimization demand set, multi-level control signal generation is performed on the real-time dynamic response parameter set to obtain a control signal sequence set matching each optimization demand; wherein the multi-level control signal generation includes generating a first compensation signal for the inertia delay parameter, generating a second compensation signal for the torque fluctuation parameter, and generating a third compensation signal for the position deviation parameter;

[0008] Inputting the control signal sequence set to the actuator of the servo device to drive the actuator to dynamically respond according to the updated motion trajectory;

[0009] The control signal sequence set is optimized based on the feedback signal set generated by the actuator during the dynamic response process.

[0010] On the other hand, an embodiment of the present invention also provides a servo system optimization system based on algorithm control, including a processor and a machine-readable storage medium, wherein the machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0011] Based on the above aspects, the embodiment of the present application can generate matching control signals in a targeted manner according to the importance of different parameters by accurately obtaining the real-time dynamic response parameter set and the corresponding optimization requirement set generated by the servo device during dynamic operation, and operating according to the requirement priority corresponding to each optimization requirement. Specifically, the first compensation signal, the second compensation signal and the third compensation signal are generated for the inertia delay parameter, the torque fluctuation parameter and the position deviation parameter, respectively, which effectively and accurately solves the problems caused by different parameters, and greatly improves the flexibility and effectiveness of control compared with the traditional method. Traditional methods often adopt a unified control method, which cannot be customized according to the characteristics and priorities of different parameters, while the multi-level control signal generation method of this method can more finely adjust the operation of the servo system. Then, the control signal sequence set is input into the actuator of the servo device, driving it to respond dynamically according to the updated motion trajectory, realizing the precise control and dynamic adjustment of the motion trajectory of the servo device, so that the servo device can quickly adapt to various complex working scenes and task requirements, greatly expanding the application scope of the servo system. Finally, by optimizing the control signal sequence set based on the feedback signal set generated by the actuator during the dynamic response process, the control signal can be continuously optimized according to the actual operating conditions, continuously improving the performance of the servo system and ensuring that it is always in the best operating state. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1It is a schematic flowchart of the execution process of the servo system optimization method based on algorithm control provided by an embodiment of the present invention.

[0013] Figure 2 It is a schematic diagram of the hardware architecture of the servo system optimization system based on algorithm control provided by an embodiment of the present invention. Detailed implementation manners

[0014] The present invention will be specifically described below with reference to the accompanying drawings of the specification. Figure 1 It is a schematic flowchart of the servo system optimization method based on algorithm control provided by an embodiment of the present invention. The servo system optimization method based on algorithm control will be introduced in detail below.

[0015] Step S110: Obtain the set of real-time dynamic response parameters generated during the dynamic operation of the servo device, and the set of optimization requirements corresponding to the servo device. Among them, the set of real-time dynamic response parameters includes the inertia delay parameter, torque fluctuation parameter, and position deviation parameter generated when the servo device executes the target motion trajectory, and the set of optimization requirements includes the first optimization requirement for the inertia delay parameter, the second optimization requirement for the torque fluctuation parameter, and the third optimization requirement for the position deviation parameter.

[0016] Taking a quadruped robot as an example, during the operation of the quadruped robot, the motion modes of the quadruped robot are complex and diverse, such as walking, running, crossing obstacles, etc. The above actions all require the servo device to precisely control the movement of each joint.

[0017] When the quadruped robot executes the target motion trajectory (such as crossing an obstacle with a specific height and width), the servo device will generate real-time dynamic response parameters.

[0018] For example, in terms of the inertia delay parameter, the leg joints of the quadruped robot are driven by the servo device. When the leg needs to be quickly lifted to cross an obstacle, due to the inertia of the mechanical structure itself and the response characteristics of the servo device, an inertia delay will occur. For example, when the control instruction of the robot requires the leg joint to accelerate from a stationary state to a specific angular velocity within 0.1 second to smoothly lift, but due to the inertia delay, it may actually take 0.15 second to reach. This 0.05-second delay is part of the inertia delay parameter.

[0019] For another example, in terms of torque fluctuation parameters, when a quadruped robot walks, different terrains will impose different loads on the leg joints. When the robot walks from flat ground to a slope, the leg joints require greater torque to support the weight of the robot and push it forward. In order to adapt to this load change, the servo device will adjust the output torque, but torque fluctuations may occur in this process. For example, when walking on normal flat ground, the torque of the leg joints is stable near a value, but when walking on a slope, the torque may fluctuate up and down from this stable value in a short period of time, and the fluctuation range may reach ±10%. This fluctuation value is the torque fluctuation parameter.

[0020] For another example, in terms of position deviation parameters, each joint of a quadruped robot has its predetermined motion position to ensure the overall balance and stable motion of the robot. However, in actual motion, position deviation may occur due to various factors. For example, when the robot turns, due to the uneven distribution of inertia and ground friction, the joint of a leg may not reach the theoretically accurate position, and the deviation may reach several millimeters. This deviation is the position deviation parameter.

[0021] At the same time, there is a corresponding set of optimization requirements for these dynamic response parameters. For example, for the first optimization requirement (for the inertia delay parameter): in the fast movement scenario of the quadruped robot, such as running, it is required to minimize the inertia delay. Because a large inertia delay may cause the robot's movements to be uncoordinated, affecting the speed and stability of the running. For example, if the robot wants to change direction quickly, the inertia delay of the leg joints is too large, and it will not be able to respond to the control command in time, which may cause the robot to fall.

[0022] For another example, regarding the second optimization requirement (for the torque fluctuation parameter): when a quadruped robot crosses different terrains (such as from hard ground to soft sand), a stable torque output is required to ensure that the robot's joints are not damaged due to excessive torque fluctuations and that it can move smoothly on different terrains.

[0023] For another example, regarding the third optimization requirement (position deviation parameter): when the quadruped robot performs precise movements (such as standing still or performing delicate operations), the position deviation is required to be controlled within an extremely small range to ensure the overall balance of the robot and the accuracy of the movement.

[0024] Step S120, according to the demand priority corresponding to each optimization demand in the optimization demand set, multi-level control signal generation is performed on the real-time dynamic response parameter set to obtain a control signal sequence set matching each optimization demand. The multi-level control signal generation includes generating a first compensation signal for the inertia delay parameter, generating a second compensation signal for the torque fluctuation parameter, and generating a third compensation signal for the position deviation parameter.

[0025] In this embodiment, the optimization requirements of the servo device of the quadruped robot have different priorities. Suppose when the robot is performing a task of crossing complex terrains (including slopes, potholes, etc.) and needs to move quickly, the priority of the first optimization requirement (reducing inertial delay) is the highest because only a quick response can ensure the robot moves stably and quickly on complex terrains; the priority of the second optimization requirement (stabilizing torque fluctuations) is the second, mainly to protect the joints and ensure the smoothness of movement; the priority of the third optimization requirement (reducing position deviation) is relatively low because in such a complex and fast-moving scenario, a certain range of position deviation is acceptable.

[0026] Generation of the first compensation signal for the inertial delay parameter: According to the priority of the first optimization requirement and the actual situation of the inertial delay parameter, determine the direction and magnitude of the compensation. For example, if the inertial delay causes the response time of the leg joint to be extended by 0.05 seconds, and the acceptable delay range (according to the robot's design and task requirements) is no more than 0.03 seconds, then a compensation signal needs to be generated, which can accelerate the response speed of the servo device to the command, so that when the leg joint moves next time, the inertial delay can be reduced to the acceptable range. Some parameters in the control algorithm can be adjusted, such as increasing the pre-driving current of the motor, so that the motor can overcome inertia and start rotating faster.

[0027] Generation of the second compensation signal for the torque fluctuation parameter: Since the torque fluctuation parameter is affected by factors such as terrain, when determining the compensation signal, the current terrain situation and the robot's motion state need to be considered. For example, when the robot moves from hard ground to soft sand, the torque fluctuation is large. According to the pre-set allowable range of torque fluctuation (such as ±10%), if the actual fluctuation reaches ±15%, a compensation signal needs to be generated to stabilize the torque output. This compensation signal may be to adjust the gain parameter in the torque control loop of the servo device to make the torque output smoother and reduce the fluctuation.

[0028] Generation of the third compensation signal for the position deviation parameter: When the position deviation exceeds the acceptable range (such as a few millimeters), a compensation signal needs to be generated. For example, when the robot stands still but due to uneven ground or slight deformation of its own structure, the position deviation of a certain joint reaches 5 millimeters, while the acceptable deviation range is 2 millimeters. The compensation signal at this time may be to adjust the proportional-integral-derivative (PID) parameters in the position control algorithm, so that the servo device can finely adjust the joint position and reduce the deviation to the acceptable range.

[0029] Through the above operations, a set of control signal sequences matching each optimization requirement is obtained.

[0030] Step S130: Input the set of control signal sequences into the actuator of the servo device, and drive the actuator to perform dynamic response according to the updated motion trajectory.

[0031] In this embodiment, the generated set of control signal sequences is input into the actuator of the servo device of the quadruped robot.

[0032] First, analyze the execution timestamp and execution intensity value corresponding to each control signal in the set of control signal sequences. For example, for the signal to control the leg joint to lift, the execution timestamp may be 0.5 seconds after the robot starts the obstacle-crossing action, and the execution intensity value determines the speed and force of the joint lift.

[0033] Then, divide the set of control signal sequences into multiple subsets of control signals according to the execution timestamps. For a quadruped robot, each motion stage (such as preparing to lift the leg, lifting the leg, crossing the leg, lowering the leg, etc.) has a corresponding subset of control signals. At the starting moment of the motion stage of lifting the leg, load the corresponding subset of control signals into the signal buffer queue of the actuator. And amplify the power of the control signals in the signal buffer queue according to the execution intensity value. For example, if the execution intensity value requires the leg joint to lift with greater force, then the control signal will be amplified accordingly to ensure that the leg can overcome gravity and inertia and lift smoothly.

[0034] Convert the amplified control signal into a physical drive instruction through the drive circuit of the actuator. In this process, obtain the voltage amplitude, current frequency, and pulse width modulation (PWM) parameters in the amplified control signal. For example, assume the voltage amplitude is 12V, the current frequency is 100Hz, and the duty cycle in the PWM parameters is 50%. Calculate the target output voltage and target output current of the drive circuit according to the voltage amplitude and current frequency, and then generate a pulse waveform sequence matching the target output voltage and target output current based on the PWM parameters. Input the pulse waveform sequence into the power switch device of the drive circuit, and control the on-time and off-time of the power switch device. Through the alternating on and off operations of the power switch device, convert the pulse waveform sequence into a physical drive instruction with a predetermined duty cycle and frequency, so as to adjust the motor speed, output torque, and position feedback accuracy of the actuator.

[0035] During this process, it is also necessary to monitor the real-time motion state of the actuator during the motion phase. For example, during the leg lifting process, parameters such as the angle and speed of the leg joints are monitored through sensors. If an abnormal motion state is detected, such as a sudden decrease in the speed of the leg joints or the angle exceeding the normal range, the execution of the current control signal subset will be interrupted and an abnormal recovery mechanism will be triggered. This abnormal recovery mechanism may include readjusting the control signal, reducing the motion speed, or making some emergency attitude adjustments to ensure the stable operation of the quadruped robot.

[0036] Step S140, optimize the control signal sequence set based on the feedback signal set generated by the actuator during the dynamic response process.

[0037] During the motion of the quadruped robot, optimize the control signal sequence set based on the feedback signal set generated by the actuator.

[0038] First, collect the feedback signal set generated by the actuator during the dynamic response process. The acceleration feedback signal and angular velocity feedback signal of the actuator (leg joints) are collected through the inertial measurement unit in the servo device of the quadruped robot. For example, when the leg joint accelerates to lift, the inertial measurement unit can measure the changes in the acceleration and angular velocity of the joint. The real-time torque feedback signal of the actuator is collected through the torque sensor to understand the torque magnitude borne by the leg joint during the motion. The actual position feedback signal of the actuator is collected through the position encoder to determine whether the leg joint has reached the predetermined position.

[0039] Perform noise filtering processing on these feedback signals. For the acceleration feedback signal, identify the high-frequency vibration noise components therein, such as the high-frequency noise generated by mechanical vibration during the robot's motion, and use a low-pass filter to attenuate these high-frequency vibration noise components. For the angular velocity feedback signal, detect the instantaneous pulse interference components therein, such as the pulse interference generated when the joint is suddenly impacted by an external force, and use a sliding window mean filtering algorithm to smooth the instantaneous pulse interference components. For the real-time torque feedback signal, extract the periodic fluctuation noise therein, such as the periodic fluctuation generated by the electromagnetic characteristics of the motor, and apply an adaptive notch filter to eliminate the periodic fluctuation noise. For the actual position feedback signal, analyze the quantization error components therein and compensate and correct the quantization error components through an interpolation algorithm.

[0040] Convert the signals after noise filtering processing into actual dynamic performance indicators. Convert the denoised acceleration data into actual inertial delay parameters, combine the denoised angular velocity data and position data to generate actual position deviation parameters, and convert the denoised torque data into actual torque fluctuation parameters.

[0041] According to the degree of difference between these actual dynamic performance indicators and the preset performance thresholds, dynamic weight adjustment is performed on each control signal in the set of control signal sequences. For example, the preset inertia delay threshold is 0.03 seconds. If the actual inertia delay reaches 0.04 seconds, the absolute value of the difference between the calculated inertia delay deviation value and the preset inertia delay threshold is 0.01 seconds. Based on this absolute value of the difference, the weight adjustment coefficient corresponding to each control signal is determined. Since the larger the absolute value of the difference, the greater the degree of adjustment required for the control signal, the weight adjustment coefficient is positively correlated with the absolute value of the difference. Based on this weight adjustment coefficient, the intensity values of each control signal in the set of control signal sequences are proportionally scaled to generate an intermediate control signal sequence.

[0042] Then, offset compensation is performed on the execution timestamps of each control signal in the intermediate control signal sequence. The phase detection signal of the quadruped robot in the current motion cycle is obtained, and the zero-crossing points and peak points therein are parsed. Based on the zero-crossing points and peak points, the dynamic response phase angle of the quadruped robot is determined, and the theoretical optimal execution moment of each control signal is calculated based on the dynamic response phase angle. The time offset between the original execution timestamp of each control signal in the intermediate control signal sequence and the theoretical optimal execution moment is compared. If the time offset is greater than the preset offset threshold, for example, the preset offset threshold is 0.01 seconds and the actual time offset is 0.02 seconds, then the original execution timestamp is corrected according to the theoretical optimal execution moment; if the time offset is less than or equal to the preset offset threshold, the original execution timestamp remains unchanged. The control signal sequence after intensity scaling and timestamp offset is used as the optimized set of control signal sequences and is iteratively input to the actuator, thereby continuously optimizing the motion performance of the quadruped robot.

[0043] Based on the above steps, the embodiments of the present application can accurately obtain the real-time dynamic response parameter set and the corresponding optimization requirement set generated during the dynamic operation of the servo device, and operate according to the requirement priority corresponding to each optimization requirement. It can generate matching control signals in a targeted manner according to the importance of different parameters, effectively and accurately solve the problems brought by different parameters, and greatly improve the flexibility and effectiveness of control compared with traditional methods. Traditional methods often adopt a unified control method and cannot perform customized processing according to the characteristics and priorities of different parameters, while the multi-level control signal generation method of this method can more finely adjust the operation of the servo system. Then, the control signal sequence set is input into the actuator of the servo device to drive it to perform dynamic response according to the updated motion trajectory, realizing precise control and dynamic adjustment of the motion trajectory of the servo device, enabling the servo device to quickly adapt to various complex working scenarios and task requirements, and greatly expanding the application scope of the servo system. Finally, based on the feedback signal set generated during the dynamic response of the actuator, the control signal sequence set is optimized, which can continuously optimize the control signal according to the actual operation situation, continuously improve the performance of the servo system, and ensure that it is always in the best operating state.

[0044] In a possible implementation manner, step S140 includes:

[0045] Step S141, collecting the feedback signal set generated during the dynamic response of the actuator, and determining the current dynamic performance index of the servo device based on the actual inertia delay parameter, actual torque fluctuation parameter, and actual position deviation parameter in the feedback signal set.

[0046] For example, through the inertial measurement unit in the servo device, the acceleration feedback signal and angular velocity feedback signal during the movement of the actuator (leg joint) can be accurately collected. For example, when a quadruped robot performs a rapid turning action, complex acceleration and angular velocity changes will occur in the leg joints, and the inertial measurement unit will record these data in real time. The torque sensor is responsible for collecting the real-time torque feedback signal of the actuator. When a quadruped robot walks on different terrains, such as walking from a flat ground onto a slope with a certain gradient, the torque borne by the leg joints will change significantly, and the torque sensor can accurately capture this change. The position encoder can collect the actual position feedback signal of the actuator. When a quadruped robot performs actions such as crossing obstacles or precise step adjustment, whether the leg joints accurately reach the predetermined position, the position encoder can give corresponding feedback.

[0047] Suppose a quadruped robot is executing a complex walking path, which includes actions such as acceleration, deceleration, turning, and crossing small obstacles. During this process, if the actual inertia delay parameter shows that the delay time from receiving the command to the start of the action of the leg joint is longer than expected by a certain value. For example, the expected inertia delay should not exceed 0.03 seconds for a certain action, but the actual measurement is 0.05 seconds; the actual torque fluctuation parameter indicates that when switching between different terrains, the torque fluctuation amplitude exceeds the designed allowable range. For example, the normal allowable torque fluctuation range is ±10%, but the actual fluctuation reaches ±15%; the actual position deviation parameter shows that during some key actions, the position of the leg joint deviates from the predetermined position by more than the acceptable deviation. For example, the acceptable position deviation is 2 mm, but the actual deviation reaches 3 mm. The above data combined reflects that the current dynamic performance index of the servo device is not good, which may affect the overall motion performance of the quadruped robot, such as walking stability, action accuracy, and adaptability to different terrains.

[0048] Step S142, according to the degree of difference between the current dynamic performance index and the preset performance threshold, dynamically adjust the weights of each control signal in the control signal sequence set to generate an optimized control signal sequence set, and iteratively input the optimized control signal sequence set into the actuator.

[0049] Taking the inertia delay parameter as an example, if the preset inertia delay threshold is 0.03 seconds and the actual measurement is 0.05 seconds, the absolute value of the difference between the two is 0.02 seconds. This difference indicates that the control signal needs to be adjusted. The larger the difference, the larger the corresponding weight adjustment coefficient. For the torque fluctuation parameter, if the preset fluctuation range is ±10% and the actual fluctuation reaches ±15%, the weight adjustment coefficient is also determined according to the degree of exceeding the range. The same is true for the position deviation parameter. Based on these weight adjustment coefficients, the intensity values of each control signal in the control signal sequence set are scaled proportionally to generate an intermediate control signal sequence. For example, the original intensity value of a certain control signal is to make the leg joint move at a certain speed. If it needs to be adjusted due to a large inertia delay, the intensity value may be increased according to the weight adjustment coefficient to accelerate the response speed of the joint.

[0050] When offset compensation is performed on the execution timestamps of each control signal in the intermediate control signal sequence, during the current motion cycle of the quadruped robot, its phase detection signal contains important information. Then, the zero-crossing points and peak points in the phase detection signal are parsed. For example, in the motion cycle of the robot's leg joints, the zero-crossing points and peak points respectively correspond to specific state transition points of the joint motion. Based on these zero-crossing points and peak points, the dynamic response phase angle of the quadruped robot is determined, and the theoretical optimal execution moment of each control signal is calculated based on this dynamic response phase angle. The time offset between the original execution timestamp of each control signal in the intermediate control signal sequence and the theoretical optimal execution moment is compared. If the time offset is greater than the preset offset threshold, for example, the preset offset threshold is 0.01 seconds and the actual time offset is 0.02 seconds, the original execution timestamp is corrected according to the theoretical optimal execution moment. If the time offset is less than or equal to the preset offset threshold, the original execution timestamp remains unchanged. The control signal sequence after such intensity scaling and timestamp offset becomes the optimized control signal sequence set, and then this optimized control signal sequence set is iteratively input into the actuator. In this way, during subsequent motion, the quadruped robot can make more accurate and efficient actions according to the optimized control signals, continuously improving its motion performance in complex environments.

[0051] In a possible implementation manner, each optimization requirement in the optimization requirement set includes a requirement type identifier and requirement constraint conditions, and step S120 includes:

[0052] Step S121, for each optimization requirement in the optimization requirement set, determine the target dynamic response parameter corresponding to the optimization requirement according to the requirement type identifier. Among them, the requirement type identifier includes an inertia delay optimization identifier, a torque fluctuation optimization identifier, and a position deviation optimization identifier.

[0053] Step S122, according to the parameter adjustment range defined in the requirement constraint conditions, perform constraint boundary extraction on the target dynamic response parameter to obtain the allowable fluctuation range of the target dynamic response parameter.

[0054] Step S123, based on the allowable fluctuation range and the requirement priority, construct a parameter adjustment function corresponding to the target dynamic response parameter, and call the parameter adjustment function to calculate the compensation signal for the target dynamic response parameter. Among them, the compensation signal calculation includes calculating the reverse compensation amount for the parameter deviation value exceeding the allowable fluctuation range.

[0055] Step S124, map the reverse compensation amount to the corresponding control signal strength value, and sort the control signal strength values according to the requirement priority to generate the control signal sequence set.

[0056] In a possible implementation manner, the requirement constraint conditions include time constraint conditions and space constraint conditions, and step S122 includes:

[0057] Step S1221, analyze the maximum allowable delay time and the minimum allowable response time in the time constraint conditions, and determine the first time constraint interval corresponding to the inertial delay parameter.

[0058] Step S1222, analyze the maximum allowable position deviation distance and the minimum allowable torque fluctuation amplitude in the space constraint conditions, and determine the first space constraint interval corresponding to the position deviation parameter and the second space constraint interval corresponding to the torque fluctuation parameter.

[0059] Step S1223, respectively construct a first allowable fluctuation interval of the inertial delay parameter, a second allowable fluctuation interval of the position deviation parameter, and a third allowable fluctuation interval of the torque fluctuation parameter according to the first time constraint interval, the first space constraint interval, and the second space constraint interval. Among them, the first allowable fluctuation interval is used to limit the time fluctuation range of the inertial delay parameter, the second allowable fluctuation interval is used to limit the spatial offset range of the position deviation parameter, and the third allowable fluctuation interval is used to limit the amplitude change range of the torque fluctuation parameter.

[0060] And step S123 includes:

[0061] Step S1231, determine the compensation weight coefficient corresponding to each allowable fluctuation interval according to the requirement priority, where the compensation weight coefficient has a positive correlation with the requirement priority.

[0062] Step S1232, input the upper limit value and the lower limit value of the allowable fluctuation interval into the interval mapping function to generate a reference parameter curve corresponding to the target dynamic response parameter.

[0063] Step S1233, construct a linear compensation term and a non - linear compensation term in the parameter adjustment function based on the reference parameter curve and the compensation weight coefficient. Among them, the linear compensation term is used to perform proportional compensation on the steady - state deviation of the target dynamic response parameter, and the non - linear compensation term is used to perform differential compensation on the transient fluctuation of the target dynamic response parameter.

[0064] Step S1234, superimpose the linear compensation term and the non - linear compensation term to generate the parameter adjustment function, and use the parameter adjustment function to calculate the deviation amount of the real - time value of the target dynamic response parameter.

[0065] In this embodiment, during the movement of the quadruped robot, each optimization requirement in its optimization requirement set includes a requirement type identifier and a requirement constraint condition. For each optimization requirement, first determine the target dynamic response parameter corresponding to the optimization requirement according to the requirement type identifier. Taking the complex scenario where the quadruped robot crosses a series of different terrains (such as flat ground, slopes, potholed terrains, etc.) and simultaneously performs a rapid turning action as an example, when the requirement type identifier is the inertia delay optimization identifier, the corresponding target dynamic response parameter is the inertia delay parameter. In this scenario, the leg joints of the quadruped robot need to respond quickly and accurately to control instructions, and the magnitude of the inertia delay directly affects the movement flexibility and accuracy of the robot. If the inertia delay is too large, the leg joints cannot change the motion state in time after receiving the turning instruction, which may cause the robot to lose balance or fail to turn according to the predetermined trajectory. When the requirement type identifier is the torque fluctuation optimization identifier, the target dynamic response parameter is the torque fluctuation parameter. When the quadruped robot moves on different terrains, the load borne by the leg joints constantly changes, which will cause torque fluctuations. For example, when climbing a slope, a greater torque is required to push the robot forward, and when going from the slope to flat ground, the torque requirement decreases. If the torque fluctuation is too large, it will affect the movement smoothness of the robot and may even cause damage to the joints and drive system. When the requirement type identifier is the position deviation optimization identifier, the target dynamic response parameter is the position deviation parameter. When the quadruped robot is walking or performing a specific action, each leg joint has a predetermined position. If the position deviation is too large, it will affect the overall posture and stability of the robot. For example, when crossing an obstacle, the leg joints need to accurately reach a specific position to ensure that the robot can pass smoothly, otherwise it may cause the robot to trip.

[0066] Next, according to the parameter adjustment range defined in the requirement constraint conditions, the constraint boundaries of the target dynamic response parameters are extracted to obtain the allowable fluctuation interval. The requirement constraint conditions include time constraint conditions and space constraint conditions. For the inertial delay parameter, the maximum allowable delay time and the minimum allowable response time in the time constraint conditions are parsed to determine the first time constraint interval. For example, when a quadruped robot performs a rapid turning motion, according to the design specifications and motion performance requirements of the robot, the maximum allowable delay time is set to 0.05 seconds, and the minimum allowable response time is 0.01 seconds. This determines that the first time constraint interval corresponding to the inertial delay parameter is [0.01, 0.05] seconds, and this first time constraint interval limits the time fluctuation range of the inertial delay parameter. For the position deviation parameter, the maximum allowable position deviation distance in the space constraint conditions is parsed to determine the first space constraint interval. Suppose that during the normal walking of a quadruped robot, according to the structure and motion stability requirements of the robot, the maximum allowable position deviation distance is 3 millimeters. Then the first space constraint interval corresponding to the position deviation parameter is [-3, 3] millimeters, and this first space constraint interval limits the spatial offset range of the position deviation parameter. For the torque fluctuation parameter, similarly, the minimum allowable torque fluctuation amplitude in the space constraint conditions is parsed to determine the second space constraint interval. For example, considering the bearing capacity of the robot joints and drive system and the requirement of motion smoothness, the minimum allowable torque fluctuation amplitude is set to ±5%. Then the second space constraint interval corresponding to the torque fluctuation parameter is [-5%, 5%], and this second space constraint interval limits the amplitude change range of the torque fluctuation parameter. According to these first time constraint intervals, first space constraint intervals, and second space constraint intervals, the first allowable fluctuation interval of the inertial delay parameter, the second allowable fluctuation interval of the position deviation parameter, and the third allowable fluctuation interval of the torque fluctuation parameter are constructed respectively.

[0067] Construct a parameter adjustment function corresponding to the target dynamic response parameter based on the allowed fluctuation range and demand priority. Taking the task scenario of a quadruped robot performing tasks including complex terrain crossing and rapid movements as an example, assume that in this task, the demand priority for inertial delay optimization is the highest, followed by torque fluctuation optimization, and the position deviation optimization has the lowest priority. Determine the compensation weight coefficient corresponding to each allowed fluctuation range according to the demand priority. Since the compensation weight coefficient is positively correlated with the demand priority, the compensation weight coefficient corresponding to the allowed fluctuation range of the inertial delay parameter is the largest, followed by that of the torque fluctuation parameter, and the smallest for the position deviation parameter. Taking the inertial delay parameter as an example, input the upper and lower limit values of its allowed fluctuation range into the interval mapping function to generate the reference parameter curve corresponding to the target dynamic response parameter. Assume that the first allowed fluctuation range of the inertial delay parameter is [0.01, 0.05] seconds, and a reference parameter curve reflecting the ideal change trend of the inertial delay parameter within this first allowed fluctuation range is obtained through the interval mapping function. Based on this reference parameter curve and the compensation weight coefficient, construct the linear compensation term and the non-linear compensation term in the parameter adjustment function. The linear compensation term is used to perform proportional compensation on the steady-state deviation of the target dynamic response parameter. For example, if the inertial delay parameter continuously and stably exceeds the middle value of the allowed fluctuation range, the linear compensation term will compensate for this deviation according to the proportional relationship to prompt the inertial delay parameter to approach the ideal value. The non-linear compensation term is used to perform differential compensation on the transient fluctuation of the target dynamic response parameter. When the inertial delay parameter undergoes a sudden change (such as a sudden increase in delay due to a sudden increase in inertia during a rapid turn), the non-linear compensation term will compensate according to the change rate to suppress the fluctuation of the inertial delay parameter. Superimpose the linear compensation term and the non-linear compensation term to generate the parameter adjustment function, and use this parameter adjustment function to calculate the deviation amount of the real-time value of the target dynamic response parameter. For example, during the actual movement of the quadruped robot, the measured real-time inertial delay parameter is 0.06 seconds, and the deviation amount from the upper limit value of the allowed fluctuation range of 0.05 seconds is calculated through the parameter adjustment function. This deviation amount includes the deviation caused by the steady-state deviation and the transient fluctuation.

[0068] For the torque fluctuation parameter and the position deviation parameter, construct the parameter adjustment function and calculate the deviation amount in the same way as above. When the quadruped robot climbs a slope, the torque fluctuation may exceed the allowed fluctuation range, and the deviation amount is calculated through the corresponding parameter adjustment function. This deviation amount reflects the degree of deviation of the actual torque fluctuation from the ideal fluctuation range. For the position deviation parameter, when the quadruped robot crosses an obstacle, if the position deviation of a certain leg joint exceeds the allowed fluctuation range, the deviation amount can also be accurately calculated through the parameter adjustment function.

[0069] After the deviation amount is calculated, a compensation signal is calculated, which includes calculating the reverse compensation amount for the parameter deviation value that exceeds the allowable fluctuation range. Taking the inertia delay parameter as an example, if the calculated deviation amount is positive (i.e., the actual inertia delay is greater than the upper limit value of the allowable fluctuation range), then the reverse compensation amount is a value that can reduce the inertia delay. This reverse compensation amount is calculated according to the parameter adjustment function and the actual deviation situation, aiming to pull the inertia delay parameter back into the allowable fluctuation range. The same applies to the torque fluctuation parameter and the position deviation parameter. When the torque fluctuation exceeds the allowable fluctuation range, the corresponding reverse compensation amount is calculated to stabilize the torque fluctuation; when the position deviation exceeds the allowable fluctuation range, the reverse compensation amount is calculated to reduce the position deviation.

[0070] Finally, map the reverse compensation amount to the corresponding control signal strength value, and sort the control signal strength values according to the demand priority to generate a set of control signal sequences. For the reverse compensation amount calculated for the inertia delay parameter, it is converted into a control signal strength value according to a certain mapping rule, and this control signal strength value determines the size of the control signal when compensating for the inertia delay. Since the demand priority for inertia delay optimization is the highest, this control signal strength value will be in a relatively important position in the sorting. Similarly, the reverse compensation amounts calculated for the torque fluctuation parameter and the position deviation parameter are also mapped to control signal strength values and sorted according to their respective demand priorities. For example, in the set of control signal sequences of a quadruped robot, first is the control signal strength value corresponding to inertia delay optimization, followed by the control signal strength value corresponding to torque fluctuation optimization, and finally the control signal strength value corresponding to position deviation optimization. The generated set of control signal sequences can accurately control the servo device of the quadruped robot according to the optimization requirements and the actual situation of the target dynamic response parameters, thereby improving the performance of the quadruped robot in complex motion scenarios and ensuring the accuracy, stability, and efficiency of its motion.

[0071] In a possible implementation manner, step S130 includes:

[0072] Step S131, parsing the execution timestamp and execution strength value corresponding to each control signal in the set of control signal sequences.

[0073] Taking the walking process of a quadruped robot as an example, when the quadruped robot takes a step, it involves the coordinated movement of multiple joints, and the movement of each joint is controlled by corresponding control signals. For example, for the joint motor responsible for lifting the leg, the execution timestamp of its control signal may be set to 0.2 seconds after the robot starts moving, and this timestamp precisely determines when the control signal starts to act on the joint. The execution intensity value determines characteristics such as the strength and speed of the joint movement. If the execution intensity value is high, it means the joint will lift with greater force and faster speed, which is necessary for crossing higher obstacles or moving quickly on complex terrains.

[0074] Step S132: Divide the set of control signal sequences into multiple control signal subsets according to the execution timestamps, where each control signal subset corresponds to a motion stage of the servo device.

[0075] A complete walking cycle of a quadruped robot can be divided into multiple motion stages, such as the leg-lifting preparation stage, the leg-lifting stage, the leg-swinging stage, the leg-lowering stage, etc. For the leg-lifting preparation stage, the corresponding control signal subset contains signals that control the leg joints to start preparing for movement from a stationary state, and these signals have specific execution timestamp ranges and execution intensity value ranges. In the leg-lifting stage, its control signal subset focuses on making the leg joints lift at an appropriate speed and force, and the control signals in this control signal subset are different from those in the leg-lifting preparation stage in terms of execution timestamp and execution intensity value. The control signal subset for each motion stage is carefully designed according to the motion logic and mechanical principles of the quadruped robot to ensure the coordinated movement of each joint in different stages.

[0076] Step S133: At the start moment of each motion stage, load the corresponding control signal subset into the signal buffer queue of the actuator, and amplify the power of the control signals in the signal buffer queue according to the execution intensity value.

[0077] For example, when entering the leg-lifting stage, at the start moment of this leg-lifting stage, the relevant control signal subset is loaded into the signal buffer queue of the actuator. Suppose there is a control signal in this control signal subset whose execution intensity value requires the leg joint to lift with greater force. To meet this requirement, it is necessary to amplify the power of this control signal in the signal buffer queue according to this execution intensity value. This is like in a mechanical transmission system, in order to make a certain component move at a predetermined force and speed, sufficient energy input needs to be given, and power amplification is to ensure that the control signal can drive the actuator to generate sufficient power.

[0078] Step S134: Convert the amplified control signal into a physical drive instruction through the drive circuit of the actuator, and adjust the motor speed, output torque, and position feedback accuracy of the actuator based on the physical drive instruction.

[0079] In a possible implementation, step S134 includes:

[0080] Step S1341: Obtain the voltage amplitude, current frequency, and pulse width modulation parameters in the amplified control signal.

[0081] Step S1342: Calculate the target output voltage and target output current of the drive circuit according to the voltage amplitude and current frequency.

[0082] Step S1343: Generate a pulse waveform sequence matching the target output voltage and target output current based on the pulse width modulation parameters.

[0083] Step S1344: Input the pulse waveform sequence into the power switch device of the drive circuit to control the on-time and off-time of the power switch device.

[0084] Step S1345: Convert the pulse waveform sequence into a physical drive instruction with a predetermined duty cycle and frequency through the alternating on and off operations of the power switch device.

[0085] For example, after the control signal in the leg-lifting stage is power-amplified, its voltage amplitude may reach 24 volts, the current frequency is 100 Hz, and the duty cycle in the pulse width modulation parameters is 60%. The target output voltage and target output current of the drive circuit are calculated based on the voltage amplitude and current frequency. In this example, the target output voltage and target output current values ​​suitable for the drive circuit are calculated based on the voltage amplitude of 24 volts and the current frequency of 100 Hz through a specific circuit calculation formula. These values ​​are key parameters to ensure the normal operation of the motor. Then, a pulse waveform sequence matching the target output voltage and target output current is generated based on the pulse width modulation parameters. The pulse waveform sequence contains various information for controlling the movement of the motor. The pulse waveform sequence is input into the power switch device of the drive circuit to control the on time and off time of the power switch device. For example, when a pulse in the pulse waveform sequence arrives, the power switch device is turned on and the current passes through the motor. When the pulse disappears, the power switch device is turned off. Through the alternating on and off operation of the power switch device, the pulse waveform sequence is converted into a physical drive instruction with a predetermined duty cycle and frequency. The physical drive instruction directly acts on the motor. Depending on the duty cycle and frequency, the motor speed, output torque and position feedback accuracy will change accordingly. For example, a higher duty cycle may increase the motor speed, thereby lifting the leg joint faster, while also affecting the output torque and position feedback accuracy, ensuring that the leg joint can accurately reach the predetermined position and provide sufficient support.

[0086] Step S135 , monitoring the real-time motion state of the actuator in the motion phase, and when detecting an abnormal motion state, interrupting the execution of the current control signal subset and triggering an abnormal recovery mechanism.

[0087] For example, during the leg-lifting phase of the quadruped robot, if the motor speed of the leg joint is detected to suddenly drop below the normal range of motion speed, this may be due to motor failure, sudden increase in load (such as the leg encountering an unexpected obstacle) or other reasons. Once this abnormal motion state is detected, the execution of the control signal subset of the current leg-lifting phase will be immediately interrupted. For example, if there are subsequent control signals in the control signal subset that have not yet been executed, these control signals will be suspended to prevent further erroneous operations. At the same time, the abnormal recovery mechanism is triggered, which may include re-evaluating the current motion state, adjusting the control signal parameters, reducing the motion speed or taking other measures to ensure the stability of the quadruped robot. If the abnormality is caused by a sudden increase in load, the execution intensity value of the control signal may be adjusted to increase the output torque to overcome the additional load so that the leg joint can continue to move normally. If it is a motor failure, it may switch to a spare motor or start a fault diagnosis program to restore the normal operation of the quadruped robot as soon as possible.

[0088] For the process of converting the amplified control signal into a physical drive command through the drive circuit of the actuator, the leg joint movement during the walking of a quadruped robot is taken as an example for further elaboration. When the amplified control signal enters the drive circuit, it is crucial to obtain the voltage amplitude, current frequency, and pulse width modulation parameters therein. Assume that during the leg-swinging phase of the quadruped robot, the voltage amplitude of the amplified control signal is 36 volts, the current frequency is 80 hertz, and the duty cycle in the pulse width modulation parameters is 50%. First, calculate the target output voltage and target output current of the drive circuit based on the voltage amplitude and current frequency. Through electrical calculation formulas, based on the voltage amplitude of 36 volts and the current frequency of 80 hertz, determine the target voltage and target current values that the drive circuit should output. These calculated target values are to match the operating characteristics of the motor to ensure that the motor can operate under suitable electrical conditions.

[0089] Generate a pulse waveform sequence that matches the target output voltage and target output current based on the pulse width modulation parameters. The generation of this pulse waveform sequence is based on the principle of pulse width modulation technology. According to parameters such as the duty cycle and frequency, construct a specific pulse sequence. Each pulse in this pulse sequence contains information for controlling the motor. For example, the width of the pulse determines the conduction time of the power switch device, thereby affecting the input energy and operating state of the motor. Then, input this pulse waveform sequence into the power switch device of the drive circuit to control the conduction time and turn-off time of the power switch device. For example, when a high-level pulse in the pulse waveform sequence arrives, the power switch device conducts, and the current flows from the power supply to the motor, and the motor starts to rotate. When the pulse becomes low level, the power switch device turns off, the current stops flowing, and the motor stops rotating or starts to decelerate. Through the alternating conduction and turn-off operations of the power switch device, convert the pulse waveform sequence into a physical drive command with a predetermined duty cycle and frequency. This physical drive command directly acts on the motor, enabling the motor to adjust its own rotational speed, output torque, and position feedback accuracy according to the command. For example, if the duty cycle in the physical drive command increases, the average input voltage of the motor will increase, resulting in an increase in the rotational speed of the motor. At the same time, the output torque will also increase accordingly, and the position feedback accuracy also needs to be adjusted accordingly to ensure that the actual position of the leg joint matches the expected position and guarantee the stable walking of the quadruped robot. In this process, any problem in any link may lead to abnormal movement of the quadruped robot. Therefore, precise control and monitoring are the keys to ensuring the normal operation of the quadruped robot.

[0090] In a possible implementation manner, step S141 includes:

[0091] Step S1411, collect the acceleration feedback signal and angular velocity feedback signal of the actuator through the inertial measurement unit in the servo device.

[0092] During the movement of the quadruped robot, various sensors in the servo device ensure that the feedback signals of the actuator (i.e., the joint motors of the quadruped robot and their related transmission components, etc.) can be accurately collected during the dynamic response process. First, the acceleration feedback signal and the angular velocity feedback signal of the actuator are collected through the inertial measurement unit in the servo device. During the walking process of the quadruped robot, when the leg joint starts to move, whether it is from a stationary state to lifting, moving, or lowering, there will be changes in acceleration and angular velocity. The inertial measurement unit can accurately sense these changes and generate corresponding feedback signals. For example, when the leg of the quadruped robot starts to accelerate and lift from the stationary state when standing to step over a small obstacle, the inertial measurement unit will detect the acceleration value and angular velocity value during this acceleration process. The acceleration feedback signal reflects the magnitude and direction of the leg joint acceleration, and the angular velocity feedback signal reflects the speed change of the joint rotation.

[0093] Step S1412, collect the real-time torque feedback signal of the actuator through the torque sensor in the servo device.

[0094] During each movement stage of the quadruped robot, the torque borne by the leg joint is constantly changing. For example, when the quadruped robot walks on a slope, in order to overcome gravity and push the robot forward, the leg joint requires a greater torque. The torque sensor can measure the magnitude of this torque in real time and generate a feedback signal. This real-time torque feedback signal is crucial for evaluating the load condition of the joint and the power distribution of the entire robot.

[0095] Step S1413, collect the actual position feedback signal of the actuator through the position encoder in the servo device.

[0096] When the quadruped robot is in motion, each leg joint has its predetermined motion trajectory and target position. The position encoder can accurately measure the actual position of the joint and generate a feedback signal. For example, during the process of the quadruped robot completing a full walking cycle, the position encoder continuously monitors whether the leg joint accurately reaches the predetermined lifting height, moving distance, and lowering position, etc.

[0097] Step S1414, perform noise filtering processing on the acceleration feedback signal, angular velocity feedback signal, real-time torque feedback signal, and actual position feedback signal to obtain the denoised acceleration data, angular velocity data, torque data, and position data.

[0098] However, after collecting these acceleration feedback signals, angular velocity feedback signals, real-time torque feedback signals, and actual position feedback signals, due to various interference factors in the actual environment, these signals inevitably contain noise components, and the noise components need to be filtered to obtain accurate signal data.

[0099] Among them, step S1414 includes:

[0100] Step S1414-1, identifying the high-frequency vibration noise components in the acceleration feedback signal, and attenuating the high-frequency vibration noise components using a low-pass filter.

[0101] During the movement of the quadruped robot, due to the friction between mechanical components, electromagnetic vibration of the motor, etc., high-frequency vibration noise may be mixed into the acceleration feedback signal. For example, when the motor of the leg joint rotates, the electromagnetic force inside the motor may cause some small high-frequency vibrations, and these vibrations will be reflected in the acceleration feedback signal. The low-pass filter allows only signal components below the set cut-off frequency to pass through by setting an appropriate cut-off frequency, thereby effectively attenuating the high-frequency vibration noise components. For example, assuming that the useful signal frequency in the acceleration feedback signal mainly concentrates in the range of 0-100Hz, and the frequency of the high-frequency vibration noise components is above 500Hz, setting the cut-off frequency of the low-pass filter to 150Hz can attenuate most of the high-frequency vibration noise components and obtain a relatively pure acceleration feedback signal.

[0102] Step S1414-2, detecting the instantaneous pulse interference components in the angular velocity feedback signal, and smoothing the instantaneous pulse interference components using a sliding window mean filtering algorithm.

[0103] During the movement of the quadruped robot, some sudden situations may cause instantaneous pulse interference components to appear in the angular velocity feedback signal. For example, when the leg joint is suddenly subjected to a short external force impact during movement, such as accidentally hitting an object next to it, a large pulse will be generated instantaneously in the angular velocity feedback signal. The sliding window mean filtering algorithm calculates the average value of the data within the sliding window to replace the data at the center of the window by setting an appropriate window size. Assuming that the size of the sliding window is 5 data points, when an instantaneous pulse interference is detected, the average value of the other normal data points within the window will be used to replace the data point containing the pulse interference, thereby realizing the smoothing of the instantaneous pulse interference components and obtaining a more stable angular velocity feedback signal.

[0104] Step S1414-3, extracting the periodic fluctuation noise in the real-time torque feedback signal, and eliminating the periodic fluctuation noise using an adaptive notch filter.

[0105] During the movement of a quadruped robot, due to some inherent characteristics of the motors or periodic disturbances in the external environment, the real-time torque feedback signal may contain periodic fluctuation noise. For example, there may be periodic fluctuations in the electromagnetic torque of the motors at a certain frequency, or when the quadruped robot walks on a regular terrain (such as periodic bumps or depressions), the periodic changes in the external load will also cause periodic fluctuations in the torque feedback signal. The adaptive notch filter can automatically detect and adapt to the frequency of this periodic fluctuation noise and then eliminate it. Assuming that the frequency of the periodic fluctuation noise is 100 Hz, the adaptive notch filter can accurately locate this frequency component and, by adjusting the parameters of the filter, remove the signal component at 100 Hz from the real-time torque feedback signal to obtain a pure torque feedback signal.

[0106] Step S1414-4, analyze the quantization error component in the actual position feedback signal and compensate and correct the quantization error component through an interpolation algorithm.

[0107] During the position measurement of a quadruped robot, due to the limited resolution of the position encoder itself, quantization errors will occur. For example, the position encoder may discretize the actual continuous position change into certain quantization units, and quantization errors will occur when the actual position change is between two quantization units. The interpolation algorithm estimates and compensates the quantization error component by using the known position data points according to a certain mathematical model. For example, if the quantization unit of the position encoder is 1 mm, when the actual position is at 1.2 mm but the position encoder can only feedback 1 mm or 2 mm, the interpolation algorithm can compensate and correct this quantization error according to the relationship of the surrounding position data points to obtain a more accurate actual position feedback signal.

[0108] Step S1414-5, use the signal data after filtering, smoothing, eliminating, and correcting as the denoised acceleration data, angular velocity data, torque data, and position data.

[0109] Step S1415, convert the denoised acceleration data into actual inertial delay parameters, combine the denoised angular velocity data with the position data to generate actual position deviation parameters, and convert the denoised torque data into actual torque fluctuation parameters.

[0110] In this embodiment, the denoised acceleration data is converted into actual inertial delay parameters, and this conversion process is based on the kinematic and dynamic models of the quadruped robot. For example, according to the mass, moment of inertia of the leg joints, and the actual acceleration data, the actual inertial delay parameters are obtained through specific calculation formulas. The denoised angular velocity data is combined with the position data to generate actual position deviation parameters because the change in angular velocity will affect the motion trajectory of the leg joints, and combining the actual position data can accurately calculate the deviation of the joint relative to the predetermined position. For example, the theoretical position that the joint should reach within a certain time is calculated based on the angular velocity data and compared with the actual position data to obtain the position deviation. The denoised torque data is converted into actual torque fluctuation parameters, and this conversion reflects the fluctuation of the actual torque relative to the ideal stable torque, which is of great significance for evaluating the load stability and dynamic performance of the joints. Through these accurately collected and processed feedback signal parameters, the actual dynamic performance of the actuator of the quadruped robot can be accurately evaluated.

[0111] In a possible implementation manner, step S142 includes:

[0112] Step S1421, calculate the absolute values of the differences between the inertial delay deviation value, torque fluctuation deviation value, and position deviation value in the current dynamic performance index and the corresponding inertial delay threshold, torque fluctuation threshold, and position deviation threshold in the preset performance threshold.

[0113] In this embodiment, during the movement of the quadruped robot, in order to ensure its precise motion control and good performance, it is necessary to dynamically adjust the weights of each control signal in the control signal sequence set according to the difference degree between the actual dynamic performance index and the preset performance threshold. First, calculate the absolute values of the differences between the inertial delay deviation value, torque fluctuation deviation value, and position deviation value in the current dynamic performance index and the corresponding inertial delay threshold, torque fluctuation threshold, and position deviation threshold in the preset performance threshold. For example, when the quadruped robot performs tasks of crossing complex terrains (including slopes with different gradients, potholes of different sizes, etc.) and complex actions (such as rapid turning, jumping, etc.), the preset inertial delay threshold is set to 0.03 seconds, and the actual inertial delay value in the current dynamic performance index is 0.05 seconds. Then the absolute value of the difference between the inertial delay deviation value and the preset inertial delay threshold is 0.02 seconds. For torque fluctuation, assume that the preset torque fluctuation threshold is ±10%, and the actually measured torque fluctuation value is ±15%. Then the absolute value of the difference between the torque fluctuation deviation value and the preset torque fluctuation threshold is 5%. For position deviation, if the preset position deviation threshold is 2 millimeters and the actually measured position deviation value is 3 millimeters, then the absolute value of the difference between the position deviation deviation value and the preset position deviation threshold is 1 millimeter.

[0114] Step S1422: Determine the weight adjustment coefficient corresponding to each control signal according to the absolute value of the difference, where the weight adjustment coefficient has a positive correlation with the absolute value of the difference.

[0115] Since the weight adjustment coefficient has a positive correlation with the absolute value of the difference, that is, the larger the absolute value of the difference, the larger the weight adjustment coefficient. Taking inertial delay as an example, if the absolute value of the difference between the inertial delay deviation value and the preset threshold is 0.02 seconds, this indicates that the actual inertial delay exceeds the preset range by a large margin, then the corresponding weight adjustment coefficient will be large, meaning that a greater degree of adjustment needs to be made to the control signal related to inertial delay. The same principle applies to torque fluctuation and position deviation. If the absolute value of the difference between the torque fluctuation deviation value and the preset threshold is large, or the absolute value of the difference between the position deviation deviation value and the preset threshold is large, then their corresponding weight adjustment coefficients will also increase accordingly.

[0116] Step S1423: Scale the intensity value of each control signal in the control signal sequence set based on the weight adjustment coefficient to generate an intermediate control signal sequence.

[0117] In the control signal system of a quadruped robot, each control signal has its specific intensity value, and these intensity values determine the motion characteristics of the actuators (such as leg joint motors), such as speed, force, etc. For example, for the control signal that controls the lifting speed of the leg joint, its original intensity value may be set to make the joint lift at a certain angular velocity. If the weight adjustment coefficient related to inertial delay is large, scale the intensity value of this control signal according to this weight adjustment coefficient, which may increase this intensity value to accelerate the lifting speed of the leg joint, thereby reducing inertial delay. Similarly, for the control signals related to torque fluctuation and position deviation, scale their intensity values according to their respective weight adjustment coefficients to generate an intermediate control signal sequence. Each control signal intensity value in this intermediate control signal sequence has been adjusted to adapt to the difference between the actual dynamic performance and the preset performance threshold, aiming to optimize the motion performance of the quadruped robot.

[0118] Step S1424: Perform offset compensation on the execution timestamp of each control signal in the intermediate control signal sequence so that the execution moment of the scaled control signal matches the dynamic response phase of the servo device.

[0119] In a possible implementation, step S1424 includes:

[0120] Step S1424-1: Obtain the phase detection signal of the servo device in the current motion cycle and parse the zero-crossing points and peak points in the phase detection signal.

[0121] During the leg joint motion cycle of a quadruped robot, the phase detection signal reflects the periodic changes in joint motion. For example, when the leg joint starts to lift upward from the lowest position, the phase detection signal starts a new cycle, within which there are specific zero-crossing points and peak points. The zero-crossing points may correspond to the change in the joint motion direction or the transition of the force balance state, and the peak points may correspond to the maximum point of the joint motion speed or the maximum point of the torque requirement, etc.

[0122] Step S1424-2: Determine the dynamic response phase angle of the servo device based on the zero-crossing points and peak points, and calculate the theoretical optimal execution time of each control signal based on the dynamic response phase angle.

[0123] For example, for a control signal that fine-tunes the leg joint during the upward movement, according to the motion cycle, speed curve of the leg joint, and the current phase angle, it is calculated that the theoretical optimal execution time of this control signal should be when the joint rises to a certain height and the speed starts to slow down. This theoretical optimal execution time can most effectively fine-tune the joint position while avoiding negative impacts on the stability of joint motion.

[0124] Step S1424-3: Compare the time offset between the original execution timestamp of each control signal in the intermediate control signal sequence and the theoretical optimal execution time.

[0125] Step S1424-4: If the time offset is greater than the preset offset threshold, correct the original execution timestamp according to the theoretical optimal execution time.

[0126] Step S1424-5: If the time offset is less than or equal to the preset offset threshold, keep the original execution timestamp unchanged.

[0127] For example, the original execution timestamp of a certain control signal is set to execute at 0.3 seconds after the leg joint starts to lift, but the calculated theoretical optimal execution time is 0.25 seconds. Then the time offset is 0.05 seconds. If this time offset is greater than the preset offset threshold (assuming the preset offset threshold is 0.03 seconds), correct the original execution timestamp according to the theoretical optimal execution time. This is done to ensure that the control signal is executed at the most appropriate moment to optimize the motion effect of the quadruped robot. If the time offset is less than or equal to the preset offset threshold, such as the time offset is 0.02 seconds, then keep the original execution timestamp unchanged because in this case, the deviation between the original execution timestamp and the theoretical optimal execution time is small and will not have an obvious adverse impact on the motion of the quadruped robot.

[0128] Step S1425, use the control signal sequence after intensity scaling and timestamp offset as the optimized control signal sequence set.

[0129] In this embodiment, the optimized control signal sequence set comprehensively considers the difference between the current dynamic performance index and the preset performance threshold. Through dynamic weight adjustment (including proportional scaling of intensity values and offset compensation of execution timestamps), it can more accurately control the servo device of the quadruped robot and improve its performance in complex motion scenarios. For example, when the quadruped robot performs tasks of crossing complex terrains and complex actions again, the optimized control signal sequence set can make the movement of the leg joints more coordinated and precise, reducing problems such as inertial delay, torque fluctuation, and position deviation, thereby improving performance indicators such as the overall movement stability, speed, and accuracy of the quadruped robot.

[0130] Figure 2 FIG. shows a schematic diagram of exemplary hardware and software components of an algorithm-based servo system optimization system 100 that can implement the idea of the present application provided by some embodiments of the present application. For example, the processor 120 can be used on the algorithm-based servo system optimization system 100 and is used to execute the functions in the present application.

[0131] The algorithm-based servo system optimization system 100 can be a general-purpose server or a special-purpose server, both of which can be used to implement the algorithm-based servo system optimization method of the present application. Although only one server is shown in the present application, for convenience, the functions described in the present application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0132] For example, the algorithm-based servo system optimization system 100 can include a network port 110 connected to the network, one or more processors 120 for executing program instructions, a communication bus 130, and different forms of storage media 140, such as disks, ROM, or RAM, or any combination thereof. Exemplarily, the algorithm-based servo system optimization system 100 can also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application can be implemented according to these program instructions. The algorithm-based servo system optimization system 100 also includes an input / output (I / O) interface 150 between the computer and other input / output devices.

[0133] For ease of explanation, only one processor is described in the algorithm-based servo system optimization system 100. However, it should be noted that the algorithm-based servo system optimization system 100 in this application may also include multiple processors. Therefore, the steps performed by one processor described in this application may also be jointly performed or separately performed by multiple processors. For example, if the processor of the algorithm-based servo system optimization system 100 performs step A and step B, it should be understood that step A and step B may also be jointly performed by two different processors or separately performed in one processor. For example, the first processor performs step A, the second processor performs step B, or the first processor and the second processor jointly perform steps A and B.

[0134] In addition, an embodiment of the present invention further provides a readable storage medium, in which computer-executable instructions are preset. When the processor executes the computer-executable instructions, the above algorithm-based servo system optimization method is implemented.

[0135] It should be noted that, in order to simplify the description of the present invention disclosure and thus help the understanding of one or more embodiments of the present invention, in the previous description of the embodiments of the present invention, sometimes multiple features are merged into one embodiment, drawing, or description thereof.

Claims

1. A servo system optimization method based on algorithm control, characterized in that: The method comprises: Acquire a real-time dynamic response parameter set generated by the servo device during dynamic operation, and an optimization requirement set corresponding to the servo device; wherein the real-time dynamic response parameter set includes an inertia delay parameter, a torque fluctuation parameter, and a position deviation parameter generated by the servo device when executing a target motion trajectory, and the optimization requirement set includes a first optimization requirement for the inertia delay parameter, a second optimization requirement for the torque fluctuation parameter, and a third optimization requirement for the position deviation parameter; According to the demand priority corresponding to each optimization demand in the optimization demand set, multi-level control signal generation is performed on the real-time dynamic response parameter set to obtain a control signal sequence set matching each optimization demand; wherein the multi-level control signal generation includes generating a first compensation signal for the inertia delay parameter, generating a second compensation signal for the torque fluctuation parameter, and generating a third compensation signal for the position deviation parameter; Inputting the control signal sequence set to the actuator of the servo device to drive the actuator to dynamically respond according to the updated motion trajectory; Performing optimization operations on the control signal sequence set based on the feedback signal set generated by the actuator during the dynamic response process; Each optimization requirement in the optimization requirement set includes a requirement type identifier and a requirement constraint condition. The multi-level control signal generation is performed on the real-time dynamic response parameter set according to the requirement priority corresponding to each optimization requirement in the optimization requirement set to obtain a control signal sequence set matching each optimization requirement, including: For each optimization requirement in the optimization requirement set, determining a target dynamic response parameter corresponding to the optimization requirement according to the requirement type identifier; wherein the requirement type identifier includes an inertia delay optimization identifier, a torque fluctuation optimization identifier, and a position deviation optimization identifier; According to the parameter adjustment range defined in the demand constraint condition, the constraint boundary of the target dynamic response parameter is extracted to obtain the allowable fluctuation range of the target dynamic response parameter; Based on the allowable fluctuation range and the demand priority, construct a parameter adjustment function corresponding to the target dynamic response parameter, and call the parameter adjustment function to perform compensation signal calculation on the target dynamic response parameter; wherein the compensation signal calculation includes calculating a reverse compensation amount for a parameter deviation value that exceeds the allowable fluctuation range; The reverse compensation amount is mapped to a corresponding control signal strength value, and the control signal strength values ​​are sorted according to the demand priority to generate the control signal sequence set.

2. The servo system optimization method based on algorithm control according to claim 1, characterized in that: The optimizing operation on the control signal sequence set based on the feedback signal set generated by the actuator in the dynamic response process includes: Collecting a feedback signal set generated by the actuator during the dynamic response process, and determining a current dynamic performance index of the servo device based on an actual inertia delay parameter, an actual torque fluctuation parameter, and an actual position deviation parameter in the feedback signal set; According to the difference between the current dynamic performance index and the preset performance threshold, each control signal in the control signal sequence set is dynamically weighted to generate an optimized control signal sequence set, and the optimized control signal sequence set is iteratively input to the actuator.

3. The servo system optimization method based on algorithm control according to claim 1, characterized in that: The demand constraint condition includes a time constraint condition and a space constraint condition. The step of extracting the constraint boundary of the target dynamic response parameter according to the parameter adjustment range defined in the demand constraint condition to obtain the allowable fluctuation range of the target dynamic response parameter includes: Analyze the maximum allowable delay time and the minimum allowable response time in the time constraint condition, and determine a first time constraint interval corresponding to the inertia delay parameter; Analyze the maximum allowable position deviation distance and the minimum allowable torque fluctuation amplitude in the spatial constraint condition, and determine a first spatial constraint interval corresponding to the position deviation parameter and a second spatial constraint interval corresponding to the torque fluctuation parameter; According to the first time constraint interval, the first space constraint interval and the second space constraint interval, a first allowable fluctuation interval of the inertia delay parameter, a second allowable fluctuation interval of the position deviation parameter and a third allowable fluctuation interval of the torque fluctuation parameter are respectively constructed; wherein the first allowable fluctuation interval is used to limit the time fluctuation range of the inertia delay parameter, the second allowable fluctuation interval is used to limit the spatial offset range of the position deviation parameter, and the third allowable fluctuation interval is used to limit the amplitude variation range of the torque fluctuation parameter; And, constructing a parameter adjustment function corresponding to the target dynamic response parameter based on the allowable fluctuation range and the demand priority includes: Determine a compensation weight coefficient corresponding to each allowable fluctuation interval according to the demand priority, wherein the compensation weight coefficient is positively correlated with the demand priority; Inputting the upper limit value and the lower limit value of the allowable fluctuation range into an interval mapping function to generate a reference parameter curve corresponding to the target dynamic response parameter; Based on the reference parameter curve and the compensation weight coefficient, a linear compensation term and a nonlinear compensation term in the parameter adjustment function are constructed; wherein the linear compensation term is used to perform proportional compensation on the steady-state deviation of the target dynamic response parameter, and the nonlinear compensation term is used to perform differential compensation on the transient fluctuation of the target dynamic response parameter; The linear compensation term and the nonlinear compensation term are superimposed to generate the parameter adjustment function, and the parameter adjustment function is used to calculate the deviation of the real-time value of the target dynamic response parameter.

4. The servo system optimization method based on algorithm control according to claim 1, characterized in that: The step of inputting the control signal sequence set to the actuator of the servo device to drive the actuator to dynamically respond according to the updated motion trajectory includes: Parsing the execution timestamp and execution strength value corresponding to each control signal in the control signal sequence set; Dividing the control signal sequence set into a plurality of control signal subsets according to the execution timestamp, wherein each control signal subset corresponds to a motion phase of the servo device; At the start of each motion phase, a corresponding subset of control signals is loaded into a signal buffer queue of the actuator, and power amplification of the control signals in the signal buffer queue is performed according to the execution intensity value; The amplified control signal is converted into a physical drive instruction by the drive circuit of the actuator, and the motor speed, output torque and position feedback accuracy of the actuator are adjusted based on the physical drive instruction; The real-time motion state of the actuator in the motion phase is monitored, and when an abnormal motion state is detected, the execution of the current control signal subset is interrupted and an abnormal recovery mechanism is triggered.

5. The servo system optimization method based on algorithm control according to claim 4 is characterized in that: The step of converting the amplified control signal into a physical drive instruction through the drive circuit of the actuator includes: Obtaining voltage amplitude, current frequency and pulse width modulation parameters in the amplified control signal; Calculating a target output voltage and a target output current of the drive circuit according to the voltage amplitude and the current frequency; generating a pulse waveform sequence matching the target output voltage and the target output current based on the pulse width modulation parameters; Inputting the pulse waveform sequence into the power switch device of the drive circuit to control the on time and off time of the power switch device; The pulse waveform sequence is converted into a physical driving instruction with a predetermined duty cycle and frequency through the alternating on and off operations of the power switch device.

6. The servo system optimization method based on algorithm control according to claim 2, characterized in that: The collecting of the feedback signal set generated by the actuator during the dynamic response process includes: Collecting the acceleration feedback signal and angular velocity feedback signal of the actuator through the inertial measurement unit in the servo device; Collecting a real-time torque feedback signal of the actuator through a torque sensor in the servo device; Acquiring an actual position feedback signal of the actuator through a position encoder in the servo device; Performing noise filtering on the acceleration feedback signal, angular velocity feedback signal, real-time torque feedback signal and actual position feedback signal to obtain denoised acceleration data, angular velocity data, torque data and position data; converting the denoised acceleration data into actual inertial delay parameters, combining the denoised angular velocity data with the position data to generate actual position deviation parameters, and converting the denoised torque data into actual torque fluctuation parameters; The noise filtering process of the acceleration feedback signal, the angular velocity feedback signal, the real-time torque feedback signal and the actual position feedback signal includes: Identifying high-frequency vibration noise components in the acceleration feedback signal, and attenuating the high-frequency vibration noise components using a low-pass filter; Detecting the instantaneous pulse interference component in the angular velocity feedback signal, and smoothing the instantaneous pulse interference component using a sliding window mean filtering algorithm; Extracting periodic fluctuation noise from the real-time torque feedback signal, and applying an adaptive notch filter to eliminate the periodic fluctuation noise; Analyzing the quantization error component in the actual position feedback signal, and compensating and correcting the quantization error component through an interpolation algorithm; The signal data after filtering, smoothing, elimination and correction are used as denoised acceleration data, angular velocity data, torque data and position data.

7. The servo system optimization method based on algorithm control according to claim 2, characterized in that: The dynamically adjusting the weight of each control signal in the control signal sequence set according to the difference between the current dynamic performance indicator and the preset performance threshold comprises: Calculating the absolute value of the difference between the inertia delay deviation value, the torque fluctuation deviation value and the position deviation value in the current dynamic performance index and the inertia delay threshold value, the torque fluctuation threshold value and the position deviation threshold value corresponding to the preset performance threshold value; Determining a weight adjustment coefficient corresponding to each control signal according to the absolute value of the difference, wherein the weight adjustment coefficient is positively correlated with the absolute value of the difference; Scaling the strength value of each control signal in the control signal sequence set based on the weight adjustment coefficient to generate an intermediate control signal sequence; Performing offset compensation on the execution timestamp of each control signal in the intermediate control signal sequence so that the execution time of the scaled control signal matches the dynamic response phase of the servo device; The control signal sequence after intensity scaling and time stamp shifting is used as the optimized control signal sequence set.

8. The servo system optimization method based on algorithm control according to claim 7, characterized in that: The offset compensation of the execution timestamp of each control signal in the intermediate control signal sequence includes: Acquire a phase detection signal of the servo device in a current motion cycle, and analyze a zero-crossing point and a peak point in the phase detection signal; Determine the dynamic response phase angle of the servo device according to the zero-crossing point and the peak point, and calculate the theoretical optimal execution time of each control signal based on the dynamic response phase angle; Comparing the time offset between the original execution timestamp of each control signal in the intermediate control signal sequence and the theoretical optimal execution time; If the time offset is greater than a preset offset threshold, the original execution timestamp is corrected according to the theoretical optimal execution time; If the time offset is less than or equal to the preset offset threshold, the original execution timestamp is kept unchanged.

9. A servo system optimization system based on algorithm control, characterized in that: The algorithm-controlled servo system optimization system includes a processor and a memory, wherein the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the algorithm-controlled servo system optimization method described in any one of claims 1 to 8.

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