Nonlinear adaptive control method and system used for motion control of a robot arm

Abstract

The present invention discloses a nonlinear adaptive control method and system for use in the motion control of a robot arm, the method including steps such as inputting the output of an incremental nonlinear dynamic inversion controller to a servo motor driver for driving a robot arm, and the system is used to realize the method. The present invention does not require a robot arm motion load model and a disturbance model for tuning the parameters of the controller, thus solving the technical problems of the conventional robot arm motion control technology, that is, the motion load model is difficult to identify and the controller's time-varying parameters must be tuned. In the current situation where the conventional technology cannot effectively solve the above difficulties and problems, the present invention provides a very simple, effective and high-performance robot arm motion control method and system.

Description

[Technical Field]

[0001] This invention relates to the field of motion control of robot arms, and more specifically to a nonlinear adaptive control method and system used for motion control of robot arms. [Background technology]

[0002] As intelligent technology continues to develop, smart devices and intelligent technologies will be increasingly used in people's lives, work, and learning, improving their quality of life and increasing the efficiency of their learning and work. In the field of robotic arm motion control, robotic arm systems are inherently nonlinear, time-varying, and uncertain systems, and the design of control systems requires the use of nonlinear and adaptive control algorithms.

[0003] Currently, all robot arm motion control systems use conventional PID control algorithms. PID control algorithms are based on linear systems, and the problem when controlling the motion of a nonlinear robot arm with a PID algorithm is how to tune the PID ratios, integrals, and derivatives. Because robot arm systems are nonlinear, time-varying, and uncertain, it is necessary to identify nonlinear or linearized models of the robot arm and its loads and disturbances, either offline or online. The obtained robot arm and load / disturbance models are used to tune the PID parameters in real time or stepwise. In the overall control process, the parameters of conventional PID algorithms are constant. In practice, the overall controlled system cannot be predicted early, and in particular, the motion and environment of a general-purpose robot arm cannot be predicted. Therefore, with fixed PID parameters, high-performance control effects cannot be obtained for the system. While model identification can achieve relatively intuitive control performance, model identification, especially for online model identification, greatly increases the complexity of the system, and it is not possible to ensure that accurate models and model parameters are obtained. Such adaptive control systems are difficult to obtain industrial certification for. Furthermore, other adaptive or intelligent control methods, including fuzzy control, sliding mode control, neural network control, and model-referenced adaptation, have problems in that they cannot guarantee the stability of the algorithm and stable operation in environments with interference from any operational state load. No effective solution has yet been proposed to address the above problem. [Overview of the project]

[0004] Therefore, the object of the present invention is to provide a nonlinear adaptive control method and system for controlling the motion of a robot arm. In the present invention, there is no need for a robot arm motion load model and a disturbance model for tuning the controller parameters; therefore, FollowThis invention solves the technical challenges in conventional robot arm motion control technology, such as the difficulty in identifying motion load models and the need to tune time-varying parameters of the controller. Given that conventional technologies cannot effectively solve the above difficulties and challenges, this invention provides a very simple, effective, and high-performance method for controlling the motion of a robot arm.

[0005] To achieve the above objective, the robot arm motion control method used in the present invention utilizes Lagrangian dynamics modeling to establish a complete nonlinear dynamics model having correspondingly n linked robot arms, acquires measurement data of the joint angle q of the robot arm, and uses numerical differentiation to obtain the second time derivative d of the joint angle q of the robot arm. 2 q / dt 2 Calculating the ideal value of the robot arm's joint angle q based on the robot arm's motion requirements. d To determine the ideal value q of the joint angle of the robot arm. d The difference between the measured value q and the second time derivative d is input to the motion controller, and the output of the motion controller and the second time derivative d are also input. 2 q / dt 2 This includes simultaneously inputting the following to an incremental nonlinear dynamic inversion controller, and inputting the control command output from the incremental nonlinear dynamic inversion controller to a servo motor driver that drives the robot arm.

[0006] In the aforementioned incremental nonlinear dynamic inversion controller, JPEG0007836904000001.jpg1067

[0007] JPEG0007836904000002.jpg31137

[0008] The virtual control variable of the incremental nonlinear dynamic inversion controller is: JPEG0007836904000003.jpg29138

[0009] The present invention further discloses a nonlinear adaptive control system used for motion control of a robot arm, comprising a processor and a memory, wherein computer-readable instructions are stored in the memory, and the processor is used to execute the computer-readable instructions, wherein the nonlinear adaptive control method used for motion control of the robot arm is executed when the computer-readable instructions are activated.

[0010] In the embodiments of the present invention, the second time derivative of the joint angle of the robot arm (angular acceleration of the robot arm joint) is used to replace the necessary robot arm system and load and disturbance models. Furthermore, in dynamic inversion control, the system and load and disturbance models are not required, and therefore, Follow In conventional robot arm motion control technology, the challenge of having to tune time-varying parameters of the controller when it is difficult to identify the robot arm system, load, and disturbance models is addressed.

[0011] According to another embodiment of the present invention, a nonlinear adaptive control system for motion control of a robot arm is further provided, comprising a processor and a memory, wherein computer-readable instructions are stored in the memory, and the processor is used to execute the computer-readable instructions, thereby executing a nonlinear adaptive control method used for motion control of the robot arm when the computer-readable instructions are activated. [Brief explanation of the drawing]

[0012] The drawings described herein are for further understanding of the present invention and constitute part of this application. The schematic embodiments and descriptions thereof are for illustrative purposes only and are not intended to limit the present invention. In the drawings, [Figure 1] This is a flowchart of a nonlinear adaptive control method used for motion control of a robot arm according to an embodiment of the present invention. [Figure 2] This is a block diagram of the configuration of a nonlinear adaptive control system used for motion control of a robot arm according to an embodiment of the present invention. [Modes for carrying out the invention]

[0013] To allow those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be described below clearly and comprehensively, with reference to the drawings of the embodiments. Naturally, the embodiments described are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, those skilled in the art will know that all other embodiments obtained without requiring creative work should be included within the scope of the claims of the present invention.

[0014] It should be noted that terms such as “First,” “Second,” etc., in the specification, claims, and drawings of the present invention are used to distinguish similar subjects and are not intended to describe a specific order or sequence. It should be understood that the data used in this manner may be replaced where appropriate so that the embodiments of the invention described herein are carried out in an order other than that illustrated or described herein. Furthermore, the terms “includes” and “composes” and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to those steps or units explicitly mentioned, and may include other steps or units that are not explicitly mentioned or are specific to those processes, methods, products, or apparatus.

[0015] According to embodiments of the present invention, a nonlinear adaptive control method used for motion control of a robot arm is provided, and it should be noted that the steps shown in the flowchart of the drawings can be executed, for example, in a computer system of a set of computer-executable commands, and although the flowchart shows a logical order, the steps shown or described may, in some cases, be executed in a different order.

[0016] Figure 1 is a flowchart of a non-linear adaptive control method used for motion control of a robotic arm according to an embodiment of the present invention. As shown in Figure 1, the method includes: Step S100 of obtaining measurement data of the joint angle q of the robotic arm; Using the numerical differentiation method to calculate the second-order time derivative coefficient d 2 q / dt 2 of the joint angle q of the robotic arm in step S200; Step S300 of inputting the difference between the ideal value q d of the joint angle of the robotic arm and the measured value q into the motion controller; Step S400 of simultaneously inputting the output of the motion controller and the second-order time derivative coefficient d 2 q / dt 2 into the incremental non-linear dynamic inversion controller; Step S500 of inputting the output of the incremental non-linear dynamic inversion controller into the servo motor driver that drives the robotic arm; Step S600 of calculating the incremental output of the incremental dynamic inversion control; Step S700 of calculating the motion controller, and includes.

[0017] Such a motion control method for a robotic arm uses the Lagrange dynamics modeling method to establish a complete non-linear dynamics model with a corresponding n-link robotic arm, obtain measurement data of the joint angle q of the robotic arm, use the numerical differentiation method to calculate the second-order time derivative coefficient d 2 q / dt 2 of the joint angle q of the robotic arm, obtain the ideal value qd of the joint angle of the robotic arm based on the robotic arm motion requirement, input the difference between the ideal value qd and the measured value q of the joint angle of the robotic arm into the motion controller, and simultaneously input the output of the motion controller and the second-order time derivative coefficient d a q / dt 2 q / dt 2 into the incremental non-linear dynamic inversion controller, and input the control command output from the incremental non-linear dynamic inversion controller into the servo motor driver that drives the robotic arm, and includes.

[0018] In the aforementioned incremental nonlinear dynamic inversion controller, JPEG0007836904000004.jpg40141

[0019] The virtual control variable of the incremental nonlinear dynamic inversion controller is: JPEG0007836904000005.jpg 865 Equation e=qq d This is the difference between the joint angle of the robot arm and the required joint angle of the robot arm, and K p This is a ratio parameter of the motion controller, K I is the integral parameter of the motion controller, and K D This is the differential parameter of the motion controller.

[0020] This incremental dynamic inversion control method is achieved based on the following principle. The robot arm model established using the Lagrangian dynamics modeling method is JPEG0007836904000006.jpg43142

[0021] Equation (1) is, It can also be written as JPEG0007836904000007.jpg10127.

[0022] Simplifying equation (2), The filename becomes JPEG0007836904000008.jpg10109.

[0023] Here, JPEG0007836904000009.jpg20126

[0024] At the sampling point, perform a vector series expansion on equation (4), JPEG0007836904000010.jpg44142

[0025] Here, JPEG0007836904000011.jpg43123

[0026] JPEG0007836904000012.jpg14124 This is also the angular acceleration of the robot arm joint at sampling time k. As can be seen from equation (2), this angular acceleration contains all the information of the system, and the controller uses the angular acceleration to substitute for the system model required for the controller.

[0027] The numbering of the above embodiments of the present invention is for illustrative purposes only and does not indicate any superiority or inferiority among the embodiments.

[0028] In the above embodiments of the present invention, each description of an embodiment has its own emphasis, and parts not described in detail in one embodiment can be referenced to the relevant description in another embodiment.

[0029] It should be understood that, in some embodiments provided herein, the disclosed technical content can be realized in other forms. Herein, the embodiments of the apparatus described above are illustrative only, and for example, the division of the units may be a logical functional division, or may have other divisional forms in actual implementation, for example, multiple units or assemblies may be combined or integrated into other systems, or some features may be ignored or not implemented. On the other hand, the mutual coupling, direct coupling or communication connection disclosed or discussed may be an indirect coupling or communication connection via some interface, unit or module, and may be in an electrical or other form.

[0030] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physically separate, and may be located in one place or distributed among multiple units. Depending on the actual needs, some or all of these units can be selected to achieve the objectives of the solution of this embodiment.

[0031] Furthermore, each functional unit in each embodiment of the present invention may be integrated into a single processing unit, each unit may exist physically independently, or two or more units may be integrated into a single unit. The integrated unit may be implemented in hardware form or in the form of a software functional unit.

[0032] The integrated unit may be implemented in the form of a software function unit and, if sold or used as an independent product, may be stored in a single computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be implemented in the form of a software product, which is stored in a single storage medium and includes several instructions used to cause a single computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The storage medium includes various media capable of storing program code, such as USB flash memory, read-only memory (ROM), random access memory (RAM), removable hard disk, magnetic disk, or optical disk.

[0033] The above description is merely a preferred embodiment of the present invention, and it should be noted that those skilled in the art may make further improvements and modifications as long as they do not deviate from the principles of the present invention, and these improvements and modifications should also be included within the scope of the claims of the present invention.

Claims

1. A nonlinear adaptive control method used for controlling the motion of a robot arm, Using the Lagrangian dynamics modeling method, we aim to establish a complete nonlinear dynamics model with correspondingly n link robot arms. To acquire measurement data of the joint angle q of the robot arm, Using numerical differentiation, find the second time derivative (d) of the joint angle q of the robot arm. ２ q) / dt ２ To calculate, The ideal value of the joint angle q of the robot arm based on the robot arm motion requirements. ｄ To release, Ideal value q for the joint angle of a robot arm ｄ The difference between the measured value q and the input to the motion controller, The output of the motion controller and the second time derivative (d ２ q) / dt ２ The following are simultaneously input to the incremental nonlinear dynamic inversion controller, This includes inputting the output value of an incremental nonlinear dynamic inversion controller to a servo motor driver that drives the robot arm, In the aforementioned incremental nonlinear dynamic inversion controller, In the equation, Δu is the output command of the incremental nonlinear dynamic inversion controller, q is the joint angle of the robot arm, and M(q) is the inertia matrix of the robot arm model. is the joint angular acceleration of the robot arm calculated from the joint angle q of the robot arm, and v is the virtual control quantity of the incremental nonlinear dynamic inversion controller. The virtual control variable of the incremental nonlinear dynamic inversion controller is: where \(e = q - q\) ｄ is the difference between the joint angle of the robotic arm and the required joint angle of the robotic arm, \(K\) ｐ is the ratio parameter of the motion controller, \(K\) Ｉ is the integral parameter of the motion controller, \(K\) Ｄ is the derivative parameter of the motion controller A nonlinear adaptive control method used for motion control of a robot arm, characterized by the following features.

2. A nonlinear adaptive control system used for controlling the motion of a robot arm, A nonlinear adaptive control system for controlling the motion of a robot arm, comprising a processor and memory, wherein computer-readable instructions are stored in the memory, and the processor is used to execute the computer-readable instructions, wherein when the computer-readable instructions are activated, the nonlinear adaptive control method used for controlling the motion of the robot arm as described in claim 1 is executed.

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