A multi-motor cooperative predictive control method and system based on adaptive sliding mode

Abstract

This invention discloses a multi-motor cooperative predictive control method and system based on adaptive sliding mode, belonging to the field of multi-axis drive cooperative control technology for robots. The invention collects the given speed and actual speed of each motor, calculates the tracking error and cooperative error; the speed tracking controller generates a reference torque; the adaptive sliding mode cooperative controller uses a novel reaching law containing a soft-sign function and an inverse hyperbolic sine function to generate torque adjustment commands based on the cooperative error; the reference torque and adjustment commands are fused to obtain the final given torque; based on the predictive model, the electromagnetic torque and stator flux prediction values ​​are calculated, and the optimal voltage vector is selected through a cost function and applied to the corresponding motor. This invention effectively suppresses sliding mode chattering, improves the system's robustness to parameter changes and load disturbances, and significantly improves the multi-motor cooperative control accuracy and operational stability of robots under complex conditions such as high speed, heavy load, and variable load.

Description

Technical Field

[0001] This invention relates to the field of multi-axis drive cooperative control technology for robots, and in particular to a multi-motor cooperative predictive control method and system based on adaptive sliding mode. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs), especially surface-mounted permanent magnet synchronous motors (SPMSMs), have become the preferred power source for modern robot drive systems due to their high torque density, low moment of inertia, fast dynamic response, and high-precision speed control characteristics. In robot systems with multiple motors working collaboratively, the synchronization accuracy of the speed and position of each motor directly determines the robot's end effector accuracy, motion smoothness, and operational reliability. For example, trajectory tracking in industrial robotic arms requires strict kinematic synchronization among all joints, differential drive in AGVs requires precise matching of the speeds of the left and right wheels, and multi-arm collaborative handling requires multiple actuators to maintain force-position synchronization.

[0003] Existing multi-motor cooperative control strategies mainly include master-slave control, cross-coupling control, and virtual spindle control. While these can meet basic cooperative requirements under normal working conditions, they have significant limitations under complex dynamic conditions of robots: First, factors such as real-time changes in robot joint load with posture, inertial disturbances generated by high-speed motion, and frictional nonlinearity can lead to a decrease in the speed tracking accuracy of a single motor. Second, the parameter dispersion of different motors and differences in drive circuit characteristics can cause the accumulation of synchronization errors among multiple motors. Third, the chattering problem in traditional sliding mode control can affect the stability of robot motion and even induce mechanical resonance. Therefore, there is an urgent need for a multi-motor cooperative control scheme that can combine high-precision tracking, strong robustness, and low chattering under complex working conditions. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-motor cooperative predictive control method and system based on adaptive sliding mode, which solves the problems of cooperative control accuracy and operational stability in existing multi-motor cooperative control strategies under complex working conditions such as high speed, heavy load, and variable load of robots.

[0005] To achieve the above objectives, the present invention provides a multi-motor cooperative predictive control method based on adaptive sliding mode, comprising the following steps: S1: Collect the given speed and actual speed of multiple permanent magnet synchronous motors in the robot system, and calculate the tracking error and coordination error; S2: The speed tracking controller generates a reference torque based on the tracking error; S3: The adaptive sliding mode cooperative controller generates torque adjustment commands based on cooperative errors; S4: Combine the reference torque with the torque adjustment command to obtain the final given torque; S5: Based on the preset prediction model, calculate the predicted electromagnetic torque and stator flux linkage of each permanent magnet synchronous motor. S6: Combining the final given torque, the predicted electromagnetic torque, the predicted stator flux linkage, and the reference stator flux linkage, the optimal voltage vector is selected through a cost function; S7: Apply the optimal voltage vector to the corresponding permanent magnet synchronous motor to achieve multi-motor coordinated control.

[0006] Preferably, S1 specifically includes: S101: Obtain the given speed of each permanent magnet synchronous motor through the robot motion planning module, and determine the proportional relationship between the given speeds.

[0007] S102: Collect the actual speed of each permanent magnet synchronous motor through the encoder, and calculate the tracking error based on the given speed and actual speed of each permanent magnet synchronous motor.

[0008] S103: Calculate the coordination error based on the actual speed of each permanent magnet synchronous motor and the coordination reference speed.

[0009] Preferably, the speed tracking controller in S2 is a PI controller, with the tracking error as the input and the reference torque required for independent operation of a single motor as the output.

[0010] Preferably, S3 specifically includes: S301: Define the system state variables and establish the state equations based on the kinematic equations of the permanent magnet synchronous motor.

[0011] S302: Design a first-order sliding surface.

[0012] S303: Design Adaptive Approach Law.

[0013] S304: Based on the system state equation, sliding surface equation, and adaptive approach law, the adaptive sliding mode control law is derived to obtain the torque adjustment command.

[0014] Preferably, S5 specifically includes: S501: Converts three-phase stator current into stator current in a two-phase stationary coordinate system using Clark transformation.

[0015] S502: Based on the inverter switching state and DC bus voltage, voltage reconstruction is performed to obtain the stator voltage in a two-phase stationary coordinate system.

[0016] S503: Perform stator flux estimation based on the voltage model method to obtain the stator flux estimate at the current moment.

[0017] S504: Substitute into the discrete prediction model to calculate the predicted values ​​of electromagnetic torque and stator flux linkage at the next moment.

[0018] Preferably, S6 specifically includes: S601: Construct the cost function.

[0019] S602: Traverse all the basic voltage vectors of the inverter and calculate the cost value corresponding to each vector.

[0020] S603: Select the voltage vector with the lowest cost value as the optimal voltage vector.

[0021] Preferably, step S7 converts the optimal voltage vector into an inverter drive signal and applies it to the corresponding permanent magnet synchronous motor to achieve multi-motor coordinated control.

[0022] Secondly, the present invention provides a cooperative predictive control system for multiple permanent magnet synchronous motors based on adaptive sliding mode, characterized in that it includes: processor; The memory stores computer-readable instructions, which, when executed by the processor, implement the aforementioned cooperative predictive control method for multiple permanent magnet synchronous motors based on adaptive sliding mode.

[0023] Thirdly, the present invention also provides a computer-readable storage medium, characterized in that the readable storage medium stores a program or instructions, which, when executed by a processor, implement the steps of the above-described adaptive sliding mode-based multi-permanent magnet synchronous motor cooperative predictive control method.

[0024] Therefore, the multi-motor cooperative predictive control method and system based on adaptive sliding mode using the above-described structure of the present invention has the following beneficial effects: The adaptive sliding mode cooperative controller used in this invention can effectively suppress control chattering, while retaining the strong robustness of sliding mode control to parameter uncertainties and external disturbances. It can also adaptively compensate for the effects of factors such as differences in motor parameters, changes in load inertia and frictional nonlinearity, thereby significantly improving the cooperative control accuracy and operational stability of the robot under complex working conditions such as high speed, heavy load and variable load.

[0025] 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

[0026] Figure 1 A flowchart illustrating a cooperative predictive control method for multiple permanent magnet synchronous motors based on adaptive sliding mode, provided in an embodiment of the present invention; Figure 2A block diagram of a cooperative predictive control system for multiple permanent magnet synchronous motors based on adaptive sliding mode is provided in an embodiment of the present invention. Figure 3 A schematic diagram of a cooperative predictive control system for multiple permanent magnet synchronous motors based on adaptive sliding mode provided in an embodiment of the present invention; Figure Labels 20. A cooperative predictive control system for multiple permanent magnet synchronous motors based on adaptive sliding mode; 201. Processor; 202. Memory. Detailed Implementation

[0027] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0028] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0029] Example Reference manual attached Figure 1 The diagram shows a flowchart of a cooperative predictive control method for multiple permanent magnet synchronous motors based on adaptive sliding mode, provided by an embodiment of the present invention.

[0030] This invention provides a cooperative predictive control method for multiple permanent magnet synchronous motors (PMSMs) based on adaptive sliding mode. This method can be implemented by a cooperative predictive control device for multiple PMSMs based on adaptive sliding mode, which can be a terminal or a server. The processing flow of the cooperative predictive control method for multiple PMSMs based on adaptive sliding mode may include the following steps: S1: Collect the given speed and actual speed of multiple permanent magnet synchronous motors in the robot system, and calculate the tracking error and coordination error.

[0031] In one possible implementation, S1 specifically includes sub-steps S101, S102, and S103: S101: Obtain the given speed of each permanent magnet synchronous motor through the robot motion planning module, and determine the proportional relationship between the given speeds.

[0032] The given speed is the target speed value set by the system. The proportional relationship between the given speeds is determined according to the following formula: ; in, This indicates the given speed of the first permanent magnet synchronous motor. This indicates the given speed of the second permanent magnet synchronous motor. Indicates the first n The given speed of the permanent magnet synchronous motor. k 1 represents the proportional coefficient corresponding to the first permanent magnet synchronous motor. k 2 represents the proportional coefficient corresponding to the second permanent magnet synchronous motor. k n Indicates the first n The proportional coefficient corresponding to the permanent magnet synchronous motor. m This indicates the serial number of the permanent magnet synchronous motor. n This indicates the total number of permanent magnet synchronous motors.

[0033] S102: Collect the actual speed of each permanent magnet synchronous motor through the encoder, and calculate the tracking error based on the given speed and actual speed of each permanent magnet synchronous motor.

[0034] The tracking error is the difference between the given electrical angular velocity and the actual electrical angular velocity.

[0035] The given electrical angular velocity is calculated using the following formula: ; in: For the first i The given electrical angular velocity of the permanent magnet synchronous motor is the first... i The number of pole pairs of a permanent magnet synchronous motor. No. i The given speed of the permanent magnet synchronous motor. i This indicates the serial number of the permanent magnet synchronous motor.

[0036] The actual electrical angular velocity is calculated using the following formula: ; in: For the first i The actual electrical angular velocity of the Taiwan permanent magnet synchronous motor No. i The actual speed of the permanent magnet synchronous motor.

[0037] Furthermore, the error is calculated according to the following formula: ; in, e i Indicates the first i Electrical angular velocity tracking error of a permanent magnet synchronous motor.

[0038] S103: Calculate the coordination error based on the actual speed of each permanent magnet synchronous motor.

[0039] The reference coordinated electrical angular velocity can be calculated using the following formula: ； in, ∑ represents the reference coordinated electrical angular velocity of multiple permanent magnet synchronous motors, and ∑ represents the summation symbol.

[0040] Furthermore, the cooperative error is calculated according to the following formula: ; in, Indicates the first i The coordination error of the permanent magnet synchronous motor.

[0041] S2: The speed tracking controller generates a reference torque based on the tracking error.

[0042] The speed tracking controller uses a PI controller, with the tracking error as the input and the reference torque required for independent operation of a single motor as the output.

[0043] S3: The adaptive sliding mode cooperative controller generates torque adjustment commands based on the cooperative error.

[0044] Among them, the cooperative controller is built based on the adaptive sliding mode control theory and is used to output torque adjustment commands according to the cooperative error in order to achieve precise cooperative operation of multiple motors.

[0045] Specifically, when constructing a cooperative controller based on the principle of model predictive control, the basic equations are first established based on the kinematic characteristics of the permanent magnet synchronous motor.

[0046] Furthermore, neglecting the viscous friction coefficient and Coulomb friction torque of the robot joint system, the kinematic equation of the permanent magnet synchronous motor is: ; in, T ei Indicates the first i The electromagnetic torque of the permanent magnet synchronous motor T Li Indicates the first i The load torque of the permanent magnet synchronous motor J iIndicates the first i The moment of inertia of a permanent magnet synchronous motor. t Represents a time variable. d The derivative symbol is used.

[0047] It should be noted that, in order to facilitate the design of the controller, the system state variables need to be defined.

[0048] Specifically, the system's state variables are defined as follows: ; in, x 1i Indicates the first i The first state variable of the permanent magnet synchronous motor. x 2i Indicates the first i The second state variable of the Taiwan permanent magnet synchronous motor. The derivative of the reference co-electrical angular velocity, Indicates the first i The derivative of the actual electrical angular velocity of the permanent magnet synchronous motor.

[0049] Furthermore, based on the above state variables and kinematic equations, the system's state equations are derived: ; in, Indicates the first i The derivative of the first state variable of the permanent magnet synchronous motor. Indicates the first i The derivative of the second state variable of the permanent magnet synchronous motor. Indicates the first i The derivative of the cooperative torque of the permanent magnet synchronous motor. Indicates the first i The derivative of the electromagnetic torque of a permanent magnet synchronous motor.

[0050] Specifically, to ensure stable convergence of the system, a first-order sliding surface is selected for the design of the control law.

[0051] The commonly used first-order sliding surface is: ; in, Indicates the first i Sliding surface variables of an adaptive sliding mode cooperative controller Indicates the first i The sliding mode coefficients of an adaptive sliding mode cooperative controller.

[0052] For example, a commonly used exponential law of convergence is: ; in, Indicates the first i The derivative of the sliding surface of an adaptive sliding mode cooperative controller. Indicates the first i The switching gain coefficient in the reaching law of an adaptive sliding mode cooperative controller sgn (·) denotes a sign function. Indicates the first i The exponential coefficient in the exponential reaching law of an adaptive sliding mode cooperative controller.

[0053] It should be noted that, in order to resolve the contradiction between sliding mode control and chattering, this invention designs an improved reaching law.

[0054] Furthermore, the improved reaching law is as follows: ; ; in, Indicates the first i The power exponent parameter in the approach law of an adaptive sliding mode cooperative controller. Indicates the first i The shape adjustment parameter in the saturation function of an adaptive sliding mode cooperative controller. e Represents the natural constant. Indicates the first i The exponential decay coefficient of an adaptive sliding mode cooperative controller, Softsign Represents soft sign functions, sinh -1 Represents the inverse hyperbolic sine function. Indicates the first i The gain coefficient in the inverse hyperbolic sine function of an adaptive sliding mode cooperative controller represents the absolute value.

[0055] Specifically, Softsign The function can be represented as: ; in, x Indicates the input variables of the function.

[0056] Specifically, sinh -1 The (·) function can be represented as: ; in, ln ( ) represents the natural constant. e Logarithmic operations with base 0.

[0057] Furthermore, by adopting an improved approach law, the system state variables can have a larger approach velocity when they are far away from the sliding surface, while the approach velocity decreases rapidly when they approach the sliding surface and slide towards the equilibrium point on the sliding surface, thereby significantly suppressing chattering in sliding mode control.

[0058] It should be noted that, according to Lyapunov stability theory, the Lyapunov function can be expressed as: ; in, Let Lyapunov function be represented.

[0059] Furthermore, the specific formula for solving the derivative of the Lyapunov function with respect to time is: ; in, This represents the time derivative of the Lyapunov function.

[0060] Furthermore, based on the definition of state variables, state equations, and improved reaching law, the torque adjustment command can be obtained: ; in, T ci Indicates the first i Torque adjustment command for a permanent magnet synchronous motor. ∫ This represents the integration operator. t Represents a time variable.

[0061] Reference manual attached Figure 2 The diagram shows a block diagram of a cooperative predictive control system for multiple permanent magnet synchronous motors based on adaptive sliding mode, provided by an embodiment of the present invention.

[0062] Figure 2 middle , ,…, Provide an electrical angular velocity for each permanent magnet synchronous motor. ω r1 , ω r2 ,…, ω rn The actual electrical angular velocity of each permanent magnet synchronous motor. e 1, e 2,…, e n For the tracking error of each permanent magnet synchronous motor, T e1-ref , T e2-ref ,…, T en-ref This serves as the reference torque for each permanent magnet synchronous motor.T c1 , T c2 ,…, T cn These are torque adjustment commands for each permanent magnet synchronous motor. , ,…, To provide the final torque for each permanent magnet synchronous motor, S 1, S 2,…, S n The bridge states of each permanent magnet synchronous motor are shown. V dc1 , V dc2 ,…, V dcn The DC bus voltage of each inverter i s1 ( k ), i s2 ( k ),…, i sn ( k The data represents the stator current in the two-phase stationary coordinate system of each permanent magnet synchronous motor. u s1 ( k ), u s2 ( k ),…, u sn ( k The data represents the stator voltage in the two-phase stationary coordinate system of each permanent magnet synchronous motor. ψ s1 ( k ), ψ s2 ( k ),…, ψ sn ( k ( ) represents the estimated stator flux linkage for each permanent magnet synchronous motor. ψ s1 ( k +1), ψ s2 ( k +1),…, ψ sn ( k +1) represents the predicted stator flux linkage value for each permanent magnet synchronous motor. i s1 ( k +1), i s2 ( k +1),…,i sn ( k +1) represents the predicted stator current values ​​for each permanent magnet synchronous motor. For the reference coordinated electrical angular velocity of multiple permanent magnet synchronous motors, , ,…, This refers to the coordination error of each permanent magnet synchronous motor.

[0063] Furthermore, in terms of integration, the system integrates modules such as a speed tracking controller, an adaptive sliding mode co-controller, a cost function calculation and optimal voltage vector selection module, an inverter, a permanent magnet synchronous motor (PMSM), Clark transformation, voltage reconstruction, flux estimation, and torque and flux prediction. In terms of connectivity, the given speed and actual speed within each channel are used to calculate and generate a tracking error. This tracking error is input to the speed tracking controller to generate a reference torque. Simultaneously, the actual speed is calculated to generate a co-operational error, which is input to the adaptive sliding mode co-controller to generate a torque adjustment command. The reference torque and the torque adjustment command are combined to generate the final given torque, which is then input to the cost function calculation and optimal voltage vector selection module. The output current of the PMSM undergoes Clark transformation and voltage reconstruction to generate stator current and voltage data. Flux estimation and torque and flux prediction then generate feedback parameters to the cost function module. Finally, the optimal voltage vector is output and used by the inverter to drive the PMSM. The actual speeds of multiple motors are aggregated and used for collaborative calculation.

[0064] It should be noted that, Figure 2 By combining adaptive sliding mode cooperative controller with model predictive control, closed-loop cooperative operation of multiple permanent magnet synchronous motors is achieved, effectively balancing the speed of each motor, suppressing system chattering, and significantly improving the cooperative control accuracy and operational stability of the multiple permanent magnet synchronous motor system under complex working conditions.

[0065] In this embodiment of the invention, through a cooperative controller (preferably an adaptive sliding mode cooperative controller) and specific sub-steps, combined with kinematic equations, state variables, sliding surfaces and improved approach laws, torque adjustment commands are accurately generated, which can adaptively adjust control parameters, suppress chattering, and improve the accuracy and stability of multi-motor cooperative control.

[0066] S4: Combine the reference torque with the torque adjustment command to obtain the final given torque.

[0067] The final given torque is the target torque value that is finally input into the prediction model.

[0068] Furthermore, the given torque data is calculated according to the following formula: ; in, Indicates the first i The final torque of the permanent magnet synchronous motor.T ei-ref Indicates the first i The reference torque of the permanent magnet synchronous motor. T ci Indicates the first i Torque adjustment command for a permanent magnet synchronous motor.

[0069] In this embodiment of the invention, the final given torque is determined by combining the reference torque and the torque adjustment command. By calculating with a clear formula, the two types of data are reasonably integrated to ensure that the given torque accurately matches the requirements of the prediction model, laying the foundation for subsequent cost function calculation and optimal voltage vector selection.

[0070] S5: Based on the preset prediction model, calculate the predicted values ​​of electromagnetic torque and stator flux linkage for each permanent magnet synchronous motor.

[0071] S501: Converts three-phase stator current into stator current in a two-phase stationary coordinate system using Clark transformation.

[0072] The coordinate transformation is a method for converting the current in the three-phase stationary coordinate system into the current in the two-phase stationary coordinate system. The stator current data is the current value in the two-phase stationary coordinate system.

[0073] Specifically, the three-phase stator current of each permanent magnet synchronous motor ( i Ai , i Bi , i Ci After Clark transformation, the stator current in the two-phase stationary coordinate system is obtained, and the formula is expressed as: ; in, i si ( k ) indicates the first i Taiwan permanent magnet synchronous motor k Stator current in a two-phase stationary coordinate system at a given time. Clark Indicates the Clark transform operation, ( i Ai , i Bi , i Ci ) respectively represent the first i The three-phase stator current of a permanent magnet synchronous motor.

[0074] S502: Based on the inverter switching state and DC bus voltage, voltage reconstruction is performed to obtain the stator voltage in a two-phase stationary coordinate system.

[0075] Among them, the bridge state refers to the on / off state of the inverter switching transistors, voltage reconstruction is the method of reconstructing the actual voltage value based on the bridge state, and stator voltage data is the voltage value in a two-phase stationary coordinate system.

[0076] S503: Perform stator flux estimation based on the voltage model method to obtain the stator flux estimate at the current moment.

[0077] Among them, stator flux estimation is the process of calculating stator flux through current and voltage data, and the estimated value of stator flux is the calculated stator flux value.

[0078] S504: Substitute into the discrete prediction model to calculate the predicted values ​​of electromagnetic torque and stator flux linkage at the next moment.

[0079] The prediction model is used to predict the torque and flux linkage values ​​at future moments based on the current state. The electromagnetic torque prediction value is the predicted electromagnetic torque value, and the stator flux linkage prediction value is the predicted stator flux linkage value.

[0080] S6: Combining the final given torque, the predicted electromagnetic torque, the predicted stator flux linkage, and the reference stator flux linkage, the optimal voltage vector is selected through a cost function.

[0081] S601: Construct the cost function.

[0082] Among them, the reference stator flux linkage is the target stator flux linkage value set by the system, the cost function is used to evaluate the control effect of different voltage vectors, and the optimal voltage vector is the voltage vector that minimizes the cost function value.

[0083] S602: Traverse all the basic voltage vectors of the inverter and calculate the cost value corresponding to each vector.

[0084] The preset filtering rule is to select the voltage vector with the lowest cost value.

[0085] Specifically, the available voltage vectors are traversed, and the vector with the lowest cost value is selected as the optimal voltage vector.

[0086] In this embodiment of the invention, by combining the reference stator flux linkage, electromagnetic torque, predicted stator flux linkage, and final given torque, the cost value is calculated through a cost function and the optimal voltage vector is selected. The selection rules are clear, and the voltage vector that matches the control requirements can be accurately selected, ensuring the rationality of subsequent motor drive.

[0087] S603: Select the voltage vector with the lowest cost value as the optimal voltage vector.

[0088] S7: Apply the optimal voltage vector to the corresponding permanent magnet synchronous motor to achieve multi-motor coordinated control.

[0089] The optimal voltage vector is converted into an inverter drive signal and applied to the corresponding permanent magnet synchronous motor to achieve multi-motor coordinated control.

[0090] Reference manual attached Figure 3 The diagram shows a structural schematic of a cooperative predictive control system for multiple permanent magnet synchronous motors based on adaptive sliding mode provided by the present invention.

[0091] The present invention also provides a cooperative predictive control system 20 for multiple permanent magnet synchronous motors based on adaptive sliding mode, applied to the above-mentioned cooperative predictive control method for multiple permanent magnet synchronous motors based on adaptive sliding mode, comprising: Processor 201.

[0092] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, they implement the adaptive sliding mode-based cooperative predictive control method for multiple permanent magnet synchronous motors as described in the method embodiment.

[0093] The adaptive sliding mode-based multi-permanent magnet synchronous motor cooperative predictive control system 20 provided by the present invention can execute the above-mentioned adaptive sliding mode-based multi-permanent magnet synchronous motor cooperative predictive control method and achieve the same or similar technical effects. To avoid repetition, the present invention will not elaborate further.

[0094] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0095] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0096] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs 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 available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available 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.

[0097] It should be understood that the term "and / or" in this article 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 existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0098] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0099] It should be 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.

[0100] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein 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.

[0101] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0102] 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 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 coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0103] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, 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.

[0104] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0105] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this 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 this 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.

[0106] This invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the cooperative predictive control method for multiple permanent magnet synchronous motors based on adaptive sliding mode as described in the method embodiment.

[0107] The present invention provides a computer-readable storage medium that can implement the steps and effects of the adaptive sliding mode-based multi-permanent magnet synchronous motor cooperative predictive control method of the above-described method embodiments. To avoid repetition, the present invention will not elaborate further.

[0108] In summary, this invention effectively suppresses chattering by introducing an improved adaptive reaching law sliding mode cooperative controller, and achieves high-precision coordination of multi-motor systems by combining model predictive control, making it particularly suitable for complex working conditions such as multi-axis robot drives.

[0109] 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 cooperative predictive control method for multiple permanent magnet synchronous motors based on adaptive sliding mode, characterized in that, include: S1: Collect the given speed and actual speed of multiple permanent magnet synchronous motors in the robot system, and calculate the tracking error and coordination error; S2: The speed tracking controller generates a reference torque based on the tracking error; S3: The adaptive sliding mode cooperative controller generates torque adjustment commands based on cooperative errors; S4: Combine the reference torque with the torque adjustment command to obtain the final given torque; S5: Based on the preset prediction model, calculate the predicted values ​​of electromagnetic torque and stator flux linkage for each permanent magnet synchronous motor. S6: Combining the final given torque, the predicted electromagnetic torque, the predicted stator flux linkage, and the reference stator flux linkage, the optimal voltage vector is selected through a cost function; S7: Apply the optimal voltage vector to the corresponding permanent magnet synchronous motor to achieve multi-motor coordinated control.

2. The cooperative predictive control method for multiple permanent magnet synchronous motors based on adaptive sliding mode according to claim 1, characterized in that, S1 specifically includes: S101: Obtain the given speed of each permanent magnet synchronous motor through the robot motion planning module, and determine the proportional relationship between the given speeds, wherein the proportional relationship is based on the formula... ； Sure, For the first The given speed of the motor, This is the proportionality coefficient; S102: The actual speed of each permanent magnet synchronous motor is acquired through the encoder. Based on the given speed and actual speed of each permanent magnet synchronous motor, the tracking error is calculated. The given electrical angular velocity is calculated according to the formula... The actual electrical angular velocity is calculated using the formula. The tracking error is calculated using the formula. calculate, For extreme logarithms, This refers to the actual rotational speed; S103: Calculate the coordination error based on the actual speed of each permanent magnet synchronous motor, where the reference coordination electrical angular velocity is calculated using the formula... The cooperative error is calculated using the formula. calculate.

3. The cooperative predictive control method for multiple permanent magnet synchronous motors based on adaptive sliding mode according to claim 1, characterized in that, The speed tracking controller in step S2 is a PI controller, with the tracking error as the input and the reference torque required for the independent operation of a single motor as the output.

4. The cooperative predictive control method for multiple permanent magnet synchronous motors based on adaptive sliding mode according to claim 1, characterized in that, Step S3 specifically includes: S301: Define the system state variables and establish the state equations based on the kinematic equations of the permanent magnet synchronous motor, where the kinematic equations are: ； State variables are defined as ； The state equation is ； S302: Design a first-order sliding surface as follows ; S303: Design Adaptive Approach Law ； in , ; S304: Based on the system state equation, sliding surface equation, and adaptive reaching law, the adaptive sliding mode control law is derived, resulting in the torque adjustment command. 。 5. The cooperative predictive control method for multiple permanent magnet synchronous motors based on adaptive sliding mode according to claim 1, characterized in that, Step S5 specifically includes: S501: The Clark transformation converts the three-phase stator current into the stator current in a two-phase stationary coordinate system. The transformation formula is as follows: ; S502: Based on the inverter switching state and DC bus voltage, voltage reconstruction is performed to obtain the stator voltage in a two-phase stationary coordinate system; S503: Perform stator flux estimation based on the voltage model method to obtain the estimated stator flux value at the current moment; S504: Substitute into the discrete prediction model to calculate the predicted values ​​of electromagnetic torque and stator flux linkage at the next moment.

6. The cooperative predictive control method for multiple permanent magnet synchronous motors based on adaptive sliding mode according to claim 1, characterized in that, Step S6 specifically includes: S601: Construct the cost function; S602: Traverse all the basic voltage vectors of the inverter and calculate the cost value corresponding to each vector; S603: Select the voltage vector with the lowest cost value as the optimal voltage vector.

7. The cooperative predictive control method for multiple permanent magnet synchronous motors based on adaptive sliding mode according to claim 1, characterized in that, Step S7 converts the optimal voltage vector into an inverter drive signal and applies it to the corresponding permanent magnet synchronous motor to achieve multi-motor coordinated control.

8. The cooperative predictive control method for multiple permanent magnet synchronous motors based on adaptive sliding mode according to claim 1, characterized in that, The formula for obtaining the final given torque in step S4 is as follows: ,in For the final given torque, As a reference torque, This is a torque adjustment command.

9. A cooperative predictive control system for multiple permanent magnet synchronous motors based on adaptive sliding mode, characterized in that, include: processor; A memory storing computer-readable instructions, which, when executed by the processor, implement the cooperative predictive control method for multiple permanent magnet synchronous motors based on adaptive sliding mode as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the adaptive sliding mode-based cooperative predictive control method for multiple permanent magnet synchronous motors as described in any one of claims 1-8.

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