Transfer robot stability control method based on reference self-adaption of active-disturbance-rejection model

Abstract

The invention discloses an auto-disturbance rejection model reference adaptive transfer robot stability control method, relates to the technical field of transfer robots, and provides a control equation transfer function under the condition of considering disturbance; through a control equation transfer function of a transfer robot control system, a disturbance model of an active-disturbance-rejection model reference self-adaptive rotating speed ring is obtained, and the disturbance model of the active-disturbance-rejection model reference self-adaptive rotating speed ring is discretized; according to the tracking differentiator and the nonlinear extended state observer after the disturbance model of the active-disturbance-rejection model reference adaptive rotating speed loop is discretized, constructing nonlinear state error feedback control laws of the active-disturbance-rejection model reference adaptive rotating speed loop and the active-disturbance-rejection model reference adaptive flux linkage loop; and a time domain signal is selected, so that the actual working condition output value of the transfer robot control system can track the output value of the control system reference model. The method can effectively and intelligently realize stable control of the transfer robot, and has good robustness and superiority.

Description

Technical Field

[0001] The invention relates to the technical field of handling robots, in particular to a self-adaptive reference control method for a handling robot. Background Art

[0002] Handling robots are automated products that use robot motion trajectories to replace manual handling. With their high efficiency, intelligence, and high flexibility, they are widely used in warehousing, manufacturing, ports, special industries, and other fields, and are beginning to show economic benefits. Since handling robots require multiple sets of different motor drives, their stability has attracted much attention. Currently, the most commonly used methods are model reference adaptive control, PID linear control method, fuzzy extended Kalman, and robust model adaptive control methods with state feedback.

[0003] After searching relevant patents and literature at home and abroad, regarding the stable control method of the handling robot, there is a paper such as "Optimization of Guidance Control of Binocular-Multi-Motion Sub-Omnidirectional Configuration Handling Robot", which designs a guidance control method for a binocular-multi-motion sub-omnidirectional configuration handling robot, and conducts a comprehensive analysis of collaborative guidance control. The results show that the convergence speed to the steady-state value after reinforcement learning optimization on a straight path is faster, and the steady-state deviation is effectively reduced, but the control performance of this method will be reduced in the case of load disturbance. In addition, the patent application with application number 202210167218.0 and name "An Intelligent Warehousing and Handling Robot System with Automatic Load Balancing and Stability" discloses an intelligent warehouse handling robot system with automatic load balancing and stability. When the robot body is in emergency braking during driving, the stability of the robot body and the cargo can be guaranteed, and the robot body and the cargo can be effectively prevented from tipping over. The stabilization mechanism can further improve the stability of the robot body during emergency braking, and further prevent the robot body and the cargo from tipping over. However, the control parameters of the patent application are uncertain, which affects the control performance of the system. For example, the patent application with application number 202410839377.X and name "A robot handling method and system with composite control of driving wheels and joints" discloses a robot handling method and system with composite control of driving wheels and joints. The control module calculates the path curvature and the curvature change rate according to the real-time coordinate information and the preset coordinate information, obtains the joint rotation angle, adjusts the speed of the drive motor, and realizes speed control of the handling path. It operates efficiently and stably, and improves the handling and production efficiency. However, this patent application requires the establishment of an accurate mathematical model of the driving wheels and joints to achieve effective control.

[0004] Although there are many studies on the control methods of handling robots in the prior art, handling robots have high requirements on the accuracy of mathematical models and there is uncertainty in the control system parameters. In addition, it is difficult to establish an optimal model caused by load disturbances, and the uncertainty of control parameters affects the control performance of the system, making it difficult to achieve stable control of the handling robot. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a stable control method for a handling robot with self-adaptive model reference. The self-adaptive model reference adaptive control method adopted in this method can decouple the torque current and excitation current of the handling robot control system, thereby compensating for the coupling part in the closed-loop control and improving the accuracy of interference estimation; it eliminates the problem of difficulty in establishing an optimal model caused by load disturbances in the operation of the handling robot control system, as well as the influence of the uncertainty of control parameters on the control performance of the system; at the same time, taking into account the limiting conditions such as the matching degree of the noise adjustment factor and the variance, it solves the problems of many factors affecting the prediction process, mutual interference of parameter sensitivity, and difficulty in establishing an accurate optimization model.

[0006] The present invention adopts the following technical solutions to solve the above technical problems:

[0007] In a first aspect, a transport robot stabilization control method based on self-disturbance rejection model reference and self-adaptation is provided according to the present invention, comprising:

[0008] According to the actual working conditions of the handling robot control system and the characteristics of the control system reference model, the control equation transfer function considering the disturbance is given;

[0009] The handling robot control system is decomposed into an auto-disturbance rejection model reference adaptive speed loop and an auto-disturbance rejection model reference adaptive flux loop. The disturbance model of the auto-disturbance rejection model reference adaptive speed loop is obtained through the control equation transfer function of the handling robot control system. The disturbance model of the auto-disturbance rejection model reference adaptive speed loop is discretized to obtain a discretized tracking differentiator and a nonlinear extended state observer.

[0010] According to the tracking differentiator and nonlinear extended state observer after discretization of the disturbance model of the ADRC reference adaptive speed loop, the nonlinear state error feedback control law of the ADRC reference adaptive speed loop and the ADRC reference adaptive flux loop is constructed;

[0011] According to the nonlinear state error feedback control law, the time domain signal is selected so that the actual working condition output value of the handling robot control system can track the output value of the control system reference model.

[0012] As a further optimization scheme of the self-disturbance rejection model reference adaptive handling robot stable control method described in the present invention, the control equation transfer function is:

[0013]

[0014] Among them, u is the control input of the handling robot control system, y is the system output of the handling robot control system, x0, x1, and x2 are the coefficients under the unknown disturbance, is the first derivative of y, is the second-order derivative of y, is the perturbation model.

[0015] As a further optimization scheme of the stable control method of a handling robot with reference to an adaptive self-disturbance rejection model described in the present invention, the disturbance model of the self-disturbance rejection model reference adaptive speed loop for:

[0016]

[0017] Among them, ζ1 represents the uncertain disturbance, ΔL m represents the change in mutual inductance of the handling robot control system, Δψ represents the change in magnetic flux of the handling robot control system, ΔJ1, ΔJ2, and ΔJ3 are the moment of inertia of the robot itself, the moment of inertia of the transmission components, and the moment of inertia of the external load, respectively. sq represents the current, n p represents the number of pole pairs, T L is the load, L r is the rotor inductance.

[0018] As a further optimization scheme of the stable control method of the transport robot with reference adaptive self-disturbance rejection model described in the present invention, the disturbance model of the reference adaptive speed loop of the self-disturbance rejection model is discretized, as follows:

[0019]

[0020] in, Set the speed of the handling robot system at time k, ω r (k) is the speed of the differentiator tracked at time k, is the speed differential of the tracking differentiator at time k, ω r (k+1) is the speed of the differentiator tracked at time k+1, is the speed differential of the differentiator at time k+1, T s is a discrete control cycle.

[0021] As a further optimization scheme of the self-disturbance rejection model reference adaptive handling robot stable control method described in the present invention, the self-disturbance rejection model reference adaptive speed loop nonlinear state error feedback control law Constructed as,

[0022]

[0023] Among them, λ v is the reference adaptive speed loop speed regulation parameter of the ADRC model, α(k) is the speed disturbance value at time k, is the speed differential of the differentiator at time k+1, and fal(·) represents a nonlinear function.

[0024] As a further optimization scheme of the self-disturbance rejection model reference adaptive handling robot stable control method described in the present invention, the discrete mathematical model of the self-disturbance rejection model reference adaptive magnetic link is expressed as:

[0025]

[0026] Among them, ψ r (k) is the actual value of magnetic flux at time k, ψ r (k+1) is the actual value of the flux linkage of the differentiator tracked at time k+1, σ is the flux linkage disturbance value, Tracking the differential flux of the differentiator at time k+1;

[0027] Constructing the nonlinear state error feedback control law of the self-disturbance rejection model reference adaptive flux link for,

[0028]

[0029] Wherein, μ(k) is the flux adjustment parameter of the reference adaptive flux loop of the ADAS model, ψ v Reference adaptive flux ring flux adjustment parameters for the self-disturbance rejection model.

[0030] As a further optimization scheme of the self-disturbance rejection model reference adaptive handling robot stable control method described in the present invention, the output value f(t) of the control system reference model is:

[0031]

[0032] Among them, y m is the reference model of y, Yes m , α0 and α1 are the error adjustment parameters of the ADRC reference adaptive control method, and f(t) is the output value of the tracking reference model.

[0033] In a second aspect, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein when the processor executes the computer program, the steps of the self-disturbance rejection model reference adaptive handling robot stabilization control method as described in the first aspect above or any corresponding embodiment thereof are implemented.

[0034] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the self-disturbance rejection model reference adaptive handling robot stabilization control method are implemented as described in the first aspect above or any corresponding embodiment thereof.

[0035] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0036] (1) Compared with the traditional proportional-integral control method, the self-disturbance rejection model reference adaptive method based on the present invention eliminates the problem of difficulty in establishing the optimal model caused by load disturbance during the operation of the handling robot control system, as well as the influence of the control parameter uncertainty on the system control performance; at the same time, considering the limiting conditions such as the matching degree of the noise adjustment factor and the variance, it solves the problem that the prediction process is affected by many factors, the parameter sensitivity interferes with each other, and it is difficult to establish an accurate optimization model.

[0037] (2) Compared with the traditional model prediction current control method, the design method of the self-disturbance rejection model reference adaptive speed loop and the self-disturbance rejection model reference adaptive flux loop adopted in this method can decouple the torque current and excitation current of the handling robot control system, thereby compensating for the coupling part in the closed-loop control and improving the accuracy of interference estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of the present invention;

[0039] Figure 2 This is the effect diagram of the acceleration, constant speed and deceleration control of the handling robot. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0041] like Figure 1 As shown, a self-disturbance rejection model reference adaptive handling robot stability control method includes the following steps:

[0042] According to the actual working conditions of the handling robot control system and the characteristics of the control system reference model, the control equation transfer function considering the disturbance is given;

[0043] The handling robot control system is decomposed into an auto-disturbance rejection model reference adaptive speed loop and an auto-disturbance rejection model reference adaptive flux loop. The disturbance model of the auto-disturbance rejection model reference adaptive speed loop is obtained through the control equation transfer function of the handling robot control system. The disturbance model of the auto-disturbance rejection model reference adaptive speed loop is discretized to obtain a discretized tracking differentiator and a nonlinear extended state observer.

[0044] According to the tracking differentiator and nonlinear extended state observer after discretization of the disturbance model of the ADRC reference adaptive speed loop, the nonlinear state error feedback control law of the ADRC reference adaptive speed loop and the ADRC reference adaptive flux loop is constructed;

[0045] The purpose of the auto-disturbance rejection model reference adaptive control method is to select appropriate time domain signals so that the actual working condition output value of the handling robot control system can track the output value of the control system reference model. The auto-disturbance rejection model reference adaptive control method adopted in this method can decouple the torque current and excitation current of the handling robot control system, thereby compensating for the coupling part in the closed-loop control and improving the accuracy of the disturbance estimation.

[0046] According to the actual working conditions of the handling robot control system, the control equation transfer function under disturbance is considered.

[0047]

[0048] Among them, u is the control input of the handling robot control system, y is the system output of the handling robot control system, x0, x1, and x2 are the coefficients under the unknown disturbance, is the first derivative of y, is the second-order derivative of y, is the perturbation model.

[0049] The handling robot control system includes the design of the self-disturbance rejection model reference adaptive speed loop and the self-disturbance rejection model reference adaptive flux link loop. Through the transfer function of the handling robot system control equation, the disturbance model of the self-disturbance rejection model reference adaptive speed loop can be obtained. for,

[0050]

[0051] Among them, ζ1 represents the uncertain disturbance, ΔL mrepresents the change in mutual inductance of the handling robot control system, Δψ represents the change in magnetic flux of the handling robot control system, ΔJ1, ΔJ2, and J3 are the moment of inertia of the robot itself, the moment of inertia of the transmission components, and the moment of inertia of the external load, respectively. sq represents the current, n p represents the number of pole pairs, T L is the load, L r is the rotor inductance.

[0052] The ADRC model of equation (2) is discretized with reference to the disturbance model of the adaptive speed loop.

[0053]

[0054] in, Set the speed of the handling robot system at time k, ω r (k) is the speed of the differentiator tracked at time k, is the speed differential of the tracking differentiator at time k, ω r (k1) is the speed of the differentiator tracked at time k+1, is the speed differential of the differentiator at time k+1, T s is a discrete control cycle.

[0055] According to the tracking differentiator and nonlinear extended state observer of the ADRC model reference adaptive speed loop, the nonlinear state error feedback control law of the ADRC model reference adaptive speed loop can be constructed as follows:

[0056]

[0057] Among them, λ v is the reference adaptive speed loop speed regulation parameter of the ADRC model, α(k) is the speed disturbance value at time k, is the speed differential of the differentiator at time k+1, and fal(·) represents a nonlinear function.

[0058] The discrete mathematical model of each part of the adaptive flux link controller can be expressed as:

[0059]

[0060] Among them, ψ r (k) is the actual value of magnetic flux at time k, ψ r (k+1) is the actual value of the flux linkage of the differentiator tracked at time k+1, σ is the flux linkage disturbance value, Tracking the differential flux of the differentiator at time k+1;

[0061] Constructing the nonlinear state error feedback control law of the self-disturbance rejection model reference adaptive flux link for,

[0062]

[0063] Wherein, μ(k) is the flux adjustment parameter of the reference adaptive flux loop of the ADAS model, ψ ν Reference adaptive flux ring flux adjustment parameters for the self-disturbance rejection model.

[0064] The purpose of the ADRC model reference adaptive control method is to select appropriate time domain signals so that the actual working condition output of the handling robot control system tracks the output of the reference model.

[0065]

[0066] Among them, y m is the reference model of y, Yes m , α0 and α1 are the error adjustment parameters of the ADRC reference adaptive control method, and f(t) is the output value of the tracking reference model.

[0067] After the introduction of the design of the self-disturbance rejection model reference adaptive speed loop and the self-disturbance rejection model reference adaptive flux link loop, the control system tracking speed converges faster. Figure 2 The three stages of acceleration, uniform speed, and deceleration shown are highly consistent with the reference model output.

[0068] The self-disturbance rejection model reference adaptive control method adopted in this method can decouple the torque current and excitation current of the handling robot control system, thereby compensating for the coupling part in the closed-loop control and improving the accuracy of disturbance estimation.

[0069] An embodiment of the present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein when the processor executes the computer program, the steps of the self-disturbance rejection model reference adaptive handling robot stabilization control method as described in the first aspect above or any corresponding embodiment thereof are implemented.

[0070] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the self-disturbance rejection model reference adaptive handling robot stabilization control method as described in the first aspect above or any corresponding embodiment thereof.

[0071] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0072] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0075] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0076] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A stable control method for a handling robot based on an adaptive self-disturbance rejection model, characterized in that: include: According to the actual working conditions of the handling robot control system and the characteristics of the control system reference model, the control equation transfer function considering the disturbance is given; The handling robot control system is decomposed into an auto-disturbance rejection model reference adaptive speed loop and an auto-disturbance rejection model reference adaptive flux loop. The disturbance model of the auto-disturbance rejection model reference adaptive speed loop is obtained through the control equation transfer function of the handling robot control system. The disturbance model of the auto-disturbance rejection model reference adaptive speed loop is discretized to obtain a discretized tracking differentiator and a nonlinear extended state observer. According to the tracking differentiator and nonlinear extended state observer after discretization of the disturbance model of the ADRC reference adaptive speed loop, the nonlinear state error feedback control law of the ADRC reference adaptive speed loop and the ADRC reference adaptive flux loop is constructed; According to the nonlinear state error feedback control law, the time domain signal is selected so that the actual working condition output value of the handling robot control system can track the output value of the control system reference model.

2. According to claim 1, a transport robot stability control method based on self-disturbance rejection model reference adaptation is characterized in that: The governing equation transfer function: Among them, u is the control input of the handling robot control system, yy is the system output of the handling robot control system, x0, x1, and x2 are the coefficients under the unknown disturbance, is the first derivative of y, is the second-order derivative of y, is the perturbation model.

3. The method for stable control of a handling robot based on an auto-disturbance rejection model reference adaptive control according to claim 2, characterized in that: The disturbance model of the adaptive speed loop is referenced by the ADRC model d(ω r )for: Among them, ζ1 represents the uncertain disturbance, ΔL m represents the change in mutual inductance of the handling robot control system, Δψ represents the change in magnetic flux of the handling robot control system, ΔJ1, ΔJ2, and ΔJ3 are the moment of inertia of the robot itself, the moment of inertia of the transmission components, and the moment of inertia of the external load, respectively. sq represents the current, n p represents the number of pole pairs, T L is the load, L r is the rotor inductance.

4. The method for stable control of a handling robot based on an auto-disturbance rejection model reference adaptation according to claim 3 is characterized in that: The disturbance model of the adaptive speed loop is discretized with reference to the disturbance model of the adaptive speed loop, as follows: in, Set the speed of the handling robot system at time k, ω r (k) is the speed of the differentiator tracked at time k, is the speed differential of the differentiator tracking at time k, ω r (k+1) is the speed of the differentiator tracked at time k+1, is the speed differential of the tracking differentiator at time k+1, T s is a discrete control cycle.

5. The method for stable control of a handling robot based on an auto-disturbance rejection model reference adaptation according to claim 4 is characterized in that: Nonlinear state error feedback control law of adaptive speed loop based on active disturbance rejection model reference Constructed as, Among them, λ v is the reference adaptive speed loop speed regulation parameter of the ADRC model, α(k) is the speed disturbance value at time k, is the speed differential of the differentiator at time k+1, and fal(·) represents a nonlinear function.

6. The method for stable control of a handling robot based on an auto-disturbance rejection model reference adaptation according to claim 5, characterized in that: The discrete mathematical model of the reference adaptive flux link of the self-disturbance rejection model is expressed as: Among them, ψ r (k) is the actual value of magnetic flux at time k, ψ r (k+1) is the actual value of the flux linkage of the differentiator tracked at time k+1, σ is the flux linkage disturbance value, Tracking the differential flux of the differentiator at time k+1; Constructing the nonlinear state error feedback control law of the self-disturbance rejection model reference adaptive flux link for, Wherein, μ(k) is the flux adjustment parameter of the reference adaptive flux loop of the ADAS model, ψ v Reference adaptive flux ring flux adjustment parameters for the self-disturbance rejection model.

7. The method for stable control of a handling robot based on an auto-disturbance rejection model reference adaptation according to claim 6, characterized in that: The output value f(t) of the control system reference model is: Among them, y m is the reference model of y, Yes m , α0 and α1 are the error adjustment parameters of the ADRC reference adaptive control method, and f(t) is the output value of the tracking reference model.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, the steps of the self-disturbance rejection model reference adaptive handling robot stability control method as described in any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the self-disturbance rejection model reference adaptive transport robot stabilization control method as described in any one of claims 1 to 7 are implemented.

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