A robot joint motor current prediction control method and system

Abstract

The application belongs to the technical field of robot joint servo control, and discloses a robot joint motor current prediction control method and system, which comprises the following steps: step S1, a state energy modulation adaptive sliding mode controller for robot joint permanent magnet synchronous motor control is designed; step S2, a robot joint servo current prediction control system is constructed by using the state energy modulation adaptive sliding mode controller; and step S3, robot joint permanent magnet synchronous motor control is realized by using the robot joint servo current prediction control system. The robot joint motor current prediction control method and system are used to effectively improve the current control precision of the robot joint permanent magnet synchronous motor, significantly improve the dynamic response speed, positioning accuracy and anti-disturbance ability of the joint servo system, suppress the sliding mode chattering and torque pulsation, and optimize the full working condition operation performance of the robot joint.

Description

Technical Field

[0001] This invention relates to the field of robot joint servo control technology, and in particular to a method and system for predictive control of robot joint motor current. Background Technology

[0002] Industrial robots and collaborative robots are core equipment in intelligent manufacturing, and joint servo systems are the core execution components of robots. Their dynamic response speed, positioning accuracy, anti-interference ability, and operational stability directly determine the overall performance of the robot and represent a core technological bottleneck for the localization of high-end robots. Permanent magnet synchronous motors (SPMSMs), with their advantages of high power density, large torque-to-inertia ratio, fast response speed, high control precision, and low loss, perfectly meet the core requirements of robot joint miniaturization, lightweighting, frequent acceleration and deceleration, and high-precision servoing, and have become the mainstream execution motor for robot joint servo systems.

[0003] With the increasing demands on the dynamic performance, steady-state accuracy, and disturbance rejection capabilities of robot joint servo systems in high-end manufacturing, high-performance control strategies for permanent magnet synchronous motors (PMSMs) have become a research hotspot in the industry. Among numerous control methods, model predictive current control (MMDC) is widely used in high-performance speed control systems for PMSMs due to its advantages such as fast dynamic response, simple control structure, ease of handling multivariable constraints, and high current tracking accuracy. MMDC utilizes a discretized mathematical model of the motor to predict the current behavior at future moments, and selects the optimal voltage vector by optimizing the cost function, thereby achieving rapid tracking and control of the stator current.

[0004] In traditional model predictive current control frameworks, the speed loop typically employs a proportional-integral (PI) controller. PI controllers have become the mainstream solution for industrial applications due to their simple structure and relatively easy parameter tuning. However, in actual operation, robot joints often face complex conditions with strong nonlinearity and strong coupling, including joint friction nonlinearity, reducer backlash, large-scale abrupt changes in load inertia (inertia can change by tens of times when the end effector grasps different workpieces), and external impact disturbances (external force impacts during grinding and assembly). Traditional PI speed controllers are essentially error feedback control based on linear models, and have inherent limitations in dealing with the aforementioned strong nonlinearity, strong coupling, and parameter uncertainties. Specifically, this manifests in two ways: First, the output of a PI controller relies on a fixed gain, making it difficult to simultaneously achieve both dynamic response speed and steady-state accuracy across the entire operating range. This can easily lead to large overshoot and response lag during start-up and shutdown, directly resulting in decreased joint positioning accuracy. Second, when faced with sudden loads, external shocks, or parameter perturbations, the PI controller exhibits insufficient robustness and limited anti-interference capabilities, leading to significant speed fluctuations and torque pulsations. This causes end effector jitter, affecting operational consistency and even accelerating wear on joint reducers, thus reducing equipment lifespan. These shortcomings limit the application potential of traditional model predictive current control schemes in high-end robotic scenarios.

[0005] In summary, existing model predictive current control methods for permanent magnet synchronous motors in robot joints based on PI velocity loops still need improvement in terms of dynamic response speed, positioning accuracy, load disturbance resistance, and operational stability. Therefore, it is necessary to investigate a novel control strategy that can improve control performance in order to further enhance the overall operational quality of robot joint servo systems. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for predictive control of robot joint motor current, which improves the current control accuracy of permanent magnet synchronous motors used in robot joints, enhances the dynamic response speed, positioning accuracy and anti-disturbance capability of joint servo systems, suppresses sliding mode chattering and torque pulsation, and optimizes the all-condition operation performance of robot joints.

[0007] To achieve the above objectives, the present invention provides a method for predictive control of robot joint motor current, comprising the following steps: Step S1: Design a state energy modulation adaptive sliding mode controller for robot joint permanent magnet synchronous motor control; Step S2: Construct a robot joint servo current prediction and control system using a state energy modulation adaptive sliding mode controller; Step S3: Use the robot joint servo current prediction and control system to realize the control of the robot joint permanent magnet synchronous motor.

[0008] Preferably, based on the principle of model predictive control, a model predictive current control system for permanent magnet synchronous motors used in robot joints is designed, and the specific process is as follows: Input given speed The actual speed of the motor is Then there is an electrical angular velocity error. for: (1); Due to speed error The reference value of q-axis torque current is obtained from the controller. Set the reference value for the d-axis excitation current. The stator current excitation component and torque component reference values ​​are obtained by rotating coordinate transformation to obtain the stator current setpoint in the αβ coordinate system. , The three-phase stator current of the motor is transformed by Clark to obtain the stator current in the two-phase stationary coordinate system. , The stator current in the dq coordinate system is then obtained through Park transformation. , The stator voltage in the two-phase stationary coordinate system is obtained by voltage reconstruction from the inverter switching state. Construct a discrete current prediction model for the system, substitute the corresponding variables, and obtain the predicted stator current value. and The cost function is designed as follows: (2); in, , These are the stator currents. The reference values ​​for the axis components are respectively derived from the inverse Park transform based on... , Give; Provided by the controller, ; and The predicted value of the stator current αβ axis component at the next moment, as predicted by the prediction model; Calculate separately The cost function corresponding to each voltage vector in The value of is selected to minimize the cost function and applied to the inverter, thereby driving the permanent magnet synchronous motor of the robot joint.

[0009] Preferably, based on the sliding mode theory, a state energy modulation adaptive sliding mode controller is designed, and the specific process is as follows: The mechanical motion equations of the permanent magnet synchronous motors for the robot joints are constructed as follows: (3); The electromagnetic torque equation of a permanent magnet synchronous motor is shown below: (4); in, The electromagnetic torque of the permanent magnet synchronous motor; This refers to the load torque on the robot joints. This represents the total rotational inertia of the robot's joint system. This represents the number of pole pairs of the motor. t is the mechanical angular velocity of the motor; t is time. For permanent magnet flux linkage in motors; Let q be the q-axis torque component of the motor stator current in the dq synchronous rotating coordinate system; The first state variable and the second state variable are determined as follows: (5); in, This is the first state variable; It is the second state variable; Give the motor a mechanical angular velocity; This is the derivative of the actual motor speed; Differentiating the expressions for the first and second state variables and combining them with the mechanical motion equations of the permanent magnet synchronous motor, considering that the load torque is constant during the control cycle, we obtain: (6); in, This is the second derivative of the actual speed of the motor; This is the electromagnetic torque derivative of the permanent magnet synchronous motor; Select a first-order sliding surface s As shown below: (7); Where c is the sliding mode coefficient. ; The exponential law of convergence is as follows: (8); in, The derivative of the first-order sliding surface; For the gain of the constant velocity arrival term, , .

[0010] A preferred design improvement of the adaptive exponential reaching law is as follows: (9); Among them, α, β, δ, and λ are all positive constants; Choose a very small positive number to avoid singularities; For the gain of the constant velocity arrival term, ; e is the base of the natural number; It is the 2-norm of the currently selected state variable. t time; functions and The function is as follows: (10); (11); The 2-norm of the selected state variable is shown below: (12).

[0011] Preferably, according to Lyapunov stability theory, the Lyapunov function is expressed as: (13); Based on equations (9) and (13), when α, β, δ, and λ are all positive constants, then: (14); in, Let be the derivative of the Lyapunov function; from the above equation, it can be seen that the system is asymptotically stable and meets the Lyapunov stability condition; When using the above-mentioned robot joint servo current prediction and control system to implement motor control, the reference value of the stator current q-axis component of the permanent magnet synchronous motor is determined based on equations (6), (7) and (9), as shown below: (15); Based on the above formula, the given torque current is calculated to realize the predictive current control of the permanent magnet synchronous motor model for robot joints based on state energy modulation adaptive sliding mode control.

[0012] A robot joint motor current prediction and control system is provided to implement the above-mentioned robot joint motor current prediction and control method. The system includes: a control framework construction module and a motor control module. A control framework building module is used to design a state energy modulation adaptive sliding mode controller for robot joint permanent magnet synchronous motor control; the state energy modulation adaptive sliding mode controller is used to replace the PI controller in the model predictive current control framework for robot joint permanent magnet synchronous motor control, resulting in a robot joint servo current predictive control system. The motor control module is used to control the permanent magnet synchronous motor of the robot joint by utilizing the robot joint servo current prediction and control system.

[0013] An electronic device includes a memory and a processor; the memory stores a computer program, and the processor executes the computer program to implement the aforementioned method for predictive control of robot joint motor current.

[0014] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for predictive control of robot joint motor current.

[0015] Therefore, this invention adopts the above-mentioned robot joint motor current prediction control method and system. Based on the model predictive control principle, a novel state energy modulation adaptive sliding mode control law is proposed, and a state energy modulation adaptive sliding mode controller is designed based on this law to replace the traditional PI as the speed outer loop controller of the robot joint servo system. This not only effectively improves the current control accuracy of the permanent magnet synchronous motor, but also significantly enhances the dynamic response speed, positioning accuracy, and anti-disturbance capability of the robot joint servo system. At the same time, the adaptive approach law of state energy modulation effectively suppresses sliding mode chattering and torque pulsation, solving the industry pain point that the traditional PI controller cannot take into account both dynamic performance and steady-state accuracy under the strong nonlinearity and strong disturbance conditions of robot joints, and greatly optimizing the all-condition operation quality of the robot joint servo system.

[0016] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the implementation process of a robot joint motor current prediction and control method according to the present invention. Figure 2 This is a block diagram of the state energy modulation adaptive sliding mode controller according to an embodiment of the present invention; Figure 3 This is a block diagram of a robot joint servo current prediction and control system based on a PI controller according to an embodiment of the present invention. Figure 4 This is a block diagram of a robot joint servo current prediction and control system based on a state energy modulation adaptive sliding mode controller according to an embodiment of the present invention. Figure 5 This is a system block diagram of the electronic device provided in the embodiments of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] like Figure 1 As shown, the present invention provides a method for predictive control of robot joint motor current, comprising the following steps: Step S1: Design a state energy modulation adaptive sliding mode controller for robot joint permanent magnet synchronous motor control; Step S2: Replace the PI controller in the model predictive current control framework used for robot joint permanent magnet synchronous motor control with a state energy modulation adaptive sliding mode controller to build a robot joint servo current predictive control system. Step S3: Use the robot joint servo current prediction and control system to realize the control of the robot joint permanent magnet synchronous motor.

[0020] Example 1 This embodiment provides a method for predictive control of robot joint motor current, and details the implementation process of the proposed method.

[0021] Based on the principle of model predictive control, a model predictive current control system for permanent magnet synchronous motors used in robot joints is designed. The principle is as follows: Input given speed The actual speed of the motor is Then there is an electrical angular velocity error. for: (1); Due to speed error The reference value of the q-axis torque current is obtained based on the controller (in this embodiment, a self-constructed state energy modulation adaptive sliding mode controller). Set the reference value for the d-axis excitation current. (The mainstream control strategy in the permanent magnet synchronous motor industry) obtains the stator current setpoint in the αβ coordinate system by transforming the reference values ​​of the stator current excitation component and torque component through a rotating coordinate transformation. , The three-phase stator current of the motor is transformed by Clark to obtain the stator current in the two-phase stationary coordinate system. , The stator current in the dq coordinate system is then obtained through Park transformation. , The stator voltage in the two-phase stationary coordinate system is obtained by voltage reconstruction from the inverter switching state. Then, a discrete current prediction model for the system is constructed, and the corresponding variables are substituted to obtain the predicted stator current value. and The cost function is designed as follows: (2); in, , These are the stator currents. The reference values ​​for the axis components are respectively derived from the inverse Park transform based on... , Give; The state-energy modulation adaptive sliding mode controller proposed in this embodiment provides the following: ; and This is the predicted value of the αβ axis component of the stator current at the next moment, as predicted by the prediction model.

[0022] Calculate separately Cost functions corresponding to the 8 voltage vectors The value of is selected to minimize the cost function and applied to the inverter, thereby driving the permanent magnet synchronous motor of the robot joint.

[0023] Based on the sliding mode theory, a state energy modulation adaptive sliding mode controller is designed to enhance the control accuracy, response speed, and robustness of the robot joint servo system.

[0024] Neglecting the viscous friction coefficient and Coulomb friction torque of the robot joint system, the mechanical motion equation of the permanent magnet synchronous motor of the robot joint can be expressed as: (3); The electromagnetic torque equation of the permanent magnet synchronous motor is expressed as: (4); in, The electromagnetic torque of the permanent magnet synchronous motor; This refers to the load torque on the robot joints. This represents the total rotational inertia of the robot's joint system. This represents the number of pole pairs of the motor. t is the mechanical angular velocity of the motor; t is time. For permanent magnet flux linkage in motors; Let q be the torque component of the motor stator current in the dq synchronous rotating coordinate system.

[0025] The first state variable and the second state variable are determined as follows: (5); in, This is the first state variable; It is the second state variable; Give the motor a mechanical angular velocity; This is the derivative of the actual motor speed.

[0026] Differentiating the expressions for the first and second state variables and combining them with the mechanical motion equations of the permanent magnet synchronous motor, considering that the load torque is constant during the control cycle, we obtain: (6); in, This is the second derivative of the actual speed of the motor; This is the electromagnetic torque derivative of the permanent magnet synchronous motor.

[0027] Select a first-order sliding surface s As shown below: (7); Where c is the sliding mode coefficient. .

[0028] Commonly used exponential reaching laws are shown below: (8); in, The derivative of the first-order sliding surface; For the gain of the constant velocity arrival term, , .

[0029] To address the chattering problem and poor dynamic performance of traditional sliding mode control, and to improve the robustness of robot joints to load disturbances and sudden changes in inertia, this embodiment designs an improved adaptive exponential reaching law, as shown below: (9); Where α, β, δ, and λ are all positive constants. For example, a set of parameters can be taken as α=5, β=15, δ=1, and λ=0.15. These parameters can be obtained by some optimization algorithms (such as Bayesian optimization and genetic algorithms) or by manual tuning, which will not be elaborated here. Choose a very small positive number to avoid singularities; For the gain of the constant velocity arrival term, ; e is the base of the natural number; It is the 2-norm of the currently selected state variable. t time.

[0030] functions and The function can be represented as follows: (10); (11); The 2-norm of the selected state variable is expressed as follows: (12); It should be noted that the square of the Euclidean norm of the state vector is usually used to represent the generalized energy of the system. Here, the adaptive gain of the modulation reaching law is applied based on the system energy. This allows the system state variables to have a large reaching velocity while avoiding overshoot when moving away from the sliding surface. When approaching the sliding surface and sliding towards the equilibrium point on the sliding surface, the reaching velocity decreases rapidly, and chattering is suppressed. During the control process, the gain will approach a stable constant after the system stabilizes. This results in a stronger disturbance rejection control effect when the system is running stably. Consequently, it has a faster response speed and smaller speed fluctuations and torque ripples when facing disturbances such as changes in robot joint load, sudden changes in inertia, or changes in reference speed.

[0031] Therefore, a state energy modulation adaptive sliding mode controller is constructed, such as Figure 2 As shown, where It is an integral operator, and it is used to replace the PI controller in the traditional model predictive current control framework, such as... Figure 3 As shown, a novel robot joint servo current prediction and control system is obtained.

[0032] According to Lyapunov stability theory, the Lyapunov function is expressed as: (13); Based on equations (9) and (13), when α, β, δ, and λ are all positive constants, then: (14); in, Let be the derivative of the Lyapunov function; from the above equation, it can be seen that the system is asymptotically stable and meets the Lyapunov stability condition.

[0033] When using the above-mentioned robot joint servo current prediction and control system to implement motor control, the reference value of the stator current q-axis component of the permanent magnet synchronous motor is determined based on equations (6), (7) and (9), as shown below: (15); According to equation (15), the given torque current is calculated to realize the robot joint based on state energy modulation adaptive sliding mode control. The current control is predicted using a permanent magnet synchronous motor model, such as... Figure 4 As shown.

[0034] Example 2 This embodiment provides a robot joint motor current prediction and control system, including a control framework construction module and a motor control module.

[0035] A control framework building module is used to design a state energy modulation adaptive sliding mode controller for the control of permanent magnet synchronous motors in robot joints. The state energy modulation adaptive sliding mode controller is used to replace the PI controller in the model predictive current control framework for the control of permanent magnet synchronous motors in robot joints, resulting in a robot joint servo current predictive control system.

[0036] The motor control module is used to control the permanent magnet synchronous motor of the robot joint by utilizing the robot joint servo current prediction and control system.

[0037] It should be noted that the robot joint permanent magnet synchronous motor current prediction control system of this embodiment corresponds to the control method of the first embodiment above; the functions implemented by each functional module in the system of this embodiment correspond one-to-one with the process steps in the method of the first embodiment above; these will not be repeated here.

[0038] Example 3 This embodiment provides an electronic device, such as... Figure 5 As shown, the electronic device includes a processor and a memory; wherein the processor and the memory can be connected via a communication bus; the memory stores at least one instruction, which is loaded and executed by the processor to implement the method of the first embodiment described above. Furthermore, the electronic device may also include a transceiver, the processor and the transceiver can be connected via a communication bus, and the transceiver is used to communicate with other devices.

[0039] Combination Figure 5 A detailed introduction to each component of this electronic device is provided below: The processor is the control center of the electronic device. The electronic device may include multiple processors, each of which can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The term "processor" can refer to a single processor or a collective term for multiple processing elements. For example, a processor can be one or more central processing units (CPUs), other general-purpose processors, application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), one or more field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor can perform various functions of the electronic device by running or executing software programs stored in memory and by calling data stored in memory.

[0040] In a specific implementation, as one example, the processor may include one or more CPUs, such as... Figure 5 The CPU0 and CPU1 shown are, of course, merely illustrative examples. The memory is used to store the software program executing the present invention, and its execution is controlled by the processor. Specific implementation methods can be found in the above-described method embodiments, and will not be repeated here.

[0041] Optionally, the memory may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory may be integrated with the processor or may exist independently and be coupled to the processor through the interface circuit of the electronic device; this embodiment of the invention does not specifically limit this.

[0042] The transceiver may include a receiver and a transmitter. The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function. The transceiver may be integrated with the processor or may exist independently and be coupled to the processor through the interface circuit of the electronic device; this embodiment of the invention does not specifically limit this.

[0043] It should be noted that, Figure 5 The structure of the electronic device shown is not intended to limit the device. Actual devices may include more or fewer components than shown, or combine certain components, or have different component arrangements. Furthermore, the technical effects achieved by this electronic device when performing the method of the first embodiment described above can be referenced to the technical effects described in the first embodiment; therefore, they will not be repeated here.

[0044] Example 4 This embodiment provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the method of the first embodiment described above. The computer-readable storage medium may be a ROM, random access memory, CD-ROM, magnetic tape, floppy disk, or optical data storage device, etc. The instruction stored therein can be loaded and executed by a processor in a terminal.

[0045] Furthermore, it should be noted that the present invention can be provided as a method, apparatus, or computer program product. Therefore, embodiments of the present invention can take the form of a completely or partially hardware embodiment, a completely or partially software embodiment, or an embodiment combining software and hardware aspects. Moreover, when implemented in software, embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any usable medium accessible to a computer or a data storage device such as a server or data center containing one or more sets of usable media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive (SSD).

[0046] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0047] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0048] It should also be noted that, in this invention, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element. Furthermore, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Additionally, the character " / " in this text generally indicates an "or" relationship between the preceding and following objects, but it can also indicate an "AND / OR" relationship. Please refer to the context for specific interpretations. "At least one" refers to one or more items, while "more than" refers to two or more items. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can be represented as: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0049] Furthermore, it is understood that in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0050] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0051] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of functional modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs. Additionally, the functional units in the various embodiments of this invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0052] If the method is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0053] It is worth noting that all contents not described in detail in this invention are existing technologies and are well known to those skilled in the art.

[0054] Therefore, this invention adopts the above-mentioned robot joint motor current prediction control method and system. Based on the model predictive control principle, a novel state energy modulation adaptive sliding mode control law is proposed. Based on this law, a state energy modulation adaptive sliding mode controller is designed to replace the traditional PI controller as the speed outer loop controller. This not only effectively improves the current control accuracy of the permanent magnet synchronous motor, but also significantly enhances the dynamic response speed, positioning accuracy, and anti-disturbance capability of the robot joint servo system. At the same time, it effectively suppresses sliding mode chattering and torque pulsation, solves the inherent defects of the traditional PI controller under the strong nonlinearity and strong disturbance conditions of the robot joint, and greatly optimizes the all-condition operation quality of the robot joint servo system.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for predictive control of robot joint motor current, characterized in that, Includes the following steps: Step S1: Design a state energy modulation adaptive sliding mode controller for robot joint permanent magnet synchronous motor control; Step S2: Construct a robot joint servo current prediction and control system using a state energy modulation adaptive sliding mode controller; Step S3: Use the robot joint servo current prediction and control system to realize the control of the robot joint permanent magnet synchronous motor.

2. The method for predictive control of robot joint motor current according to claim 1, characterized in that, Based on the principle of model predictive control, a model predictive current control system for permanent magnet synchronous motors used in robot joints is designed. The specific process is as follows: Input given speed The actual speed of the motor is Then there is an electrical angular velocity error. for: （1）； Due to speed error The reference value of q-axis torque current is obtained from the controller. ; Set the d-axis excitation current reference value The stator current excitation component and torque component reference values ​​are obtained by rotating coordinate transformation to obtain the stator current setpoint in the αβ coordinate system. , The three-phase stator current of the motor is transformed by Clark to obtain the stator current in the two-phase stationary coordinate system. , The stator current in the dq coordinate system is then obtained through Park transformation. , The stator voltage in the two-phase stationary coordinate system is obtained by voltage reconstruction from the inverter switching state. ; Construct a discrete current prediction model for the system, substitute the corresponding variables, and obtain the predicted stator current value. and The cost function is designed as follows: （2）； in, , These are the stator currents. The reference values ​​for the axis components are respectively derived from the inverse Park transform based on... , Give; Provided by the controller, ; and The predicted value of the stator current αβ axis component at the next moment, as predicted by the prediction model; Calculate separately The cost function corresponding to each voltage vector in The value of is selected to minimize the cost function and applied to the inverter, thereby driving the permanent magnet synchronous motor of the robot joint.

3. The method for predictive control of robot joint motor current according to claim 2, characterized in that, Based on the sliding mode theory, a state energy modulation adaptive sliding mode controller is designed. The specific process is as follows: The mechanical motion equations of the permanent magnet synchronous motors for the robot joints are constructed as follows: （3）； The electromagnetic torque equation of a permanent magnet synchronous motor is shown below: （4）； in, The electromagnetic torque of the permanent magnet synchronous motor; This refers to the load torque of the robot joints; This represents the total moment of inertia of the robot's joint system. This represents the number of pole pairs of the motor. t is the mechanical angular velocity of the motor; t is time. For permanent magnet flux linkage in motors; Let q be the q-axis torque component of the motor stator current in the dq synchronous rotating coordinate system; The first state variable and the second state variable are determined as follows: （5）； in, This is the first state variable; It is the second state variable; Give the motor a mechanical angular velocity; This is the derivative of the actual motor speed; Differentiating the expressions for the first and second state variables and combining them with the mechanical motion equations of the permanent magnet synchronous motor, considering that the load torque is constant during the control cycle, we obtain: （6）； in, This is the second derivative of the actual speed of the motor; This is the electromagnetic torque derivative of the permanent magnet synchronous motor; Select a first-order sliding surface s As shown below: （7）； Where c is the sliding mode coefficient. ; The exponential law of convergence is as follows: （8）； in, The derivative of the first-order sliding surface; For the gain of the constant velocity arrival term, , .

4. The method for predictive control of robot joint motor current according to claim 3, characterized in that, The improved adaptive exponential reaching law is designed as follows: （9）； Among them, α, β, δ, and λ are all positive constants; Choose a very small positive number to avoid singularities; For the gain of the constant velocity arrival term, ; e is the base of the natural number; It is the 2-norm of the currently selected state variable. t time; functions and The function is as follows: （10）； （11）； The 2-norm of the selected state variable is shown below: （12）。 5. The method for predictive control of robot joint motor current according to claim 4, characterized in that, According to Lyapunov stability theory, the Lyapunov function is expressed as: （13）； Based on equations (9) and (13), when α, β, δ, and λ are all positive constants, then: （14）； in, Let be the derivative of the Lyapunov function; from the above equation, it can be seen that the system is asymptotically stable and meets the Lyapunov stability condition; When using the above-mentioned robot joint servo current prediction and control system to implement motor control, the reference value of the stator current q-axis component of the permanent magnet synchronous motor is determined based on equations (6), (7) and (9), as shown below: （15）； Based on the above formula, the given torque current is calculated to realize the predictive current control of the permanent magnet synchronous motor model for robot joints based on state energy modulation adaptive sliding mode control.

6. A robot joint motor current prediction and control system, used to implement the robot joint motor current prediction and control method according to any one of claims 1-5, characterized in that, The system includes: a control framework construction module and a motor control module; A control framework building module is used to design a state energy modulation adaptive sliding mode controller for robot joint permanent magnet synchronous motor control; the state energy modulation adaptive sliding mode controller is used to replace the PI controller in the model predictive current control framework for robot joint permanent magnet synchronous motor control, resulting in a robot joint servo current predictive control system. The motor control module is used to control the permanent magnet synchronous motor of the robot joint by utilizing the robot joint servo current prediction and control system.

7. An electronic device, characterized in that, It includes a memory and a processor; the memory stores a computer program, and the processor executes the computer program to implement the robot joint motor current prediction control method according to any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the robot joint motor current prediction control method according to any one of claims 1-5.

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