Nonlinear adaptive motion control method and system for robotic arm manipulation

Abstract

A nonlinear adaptive control method and system for mechanical arm motion control. The method comprises the steps of inputting the output of an incremental nonlinear inverse dynamics controller into a

Description

Nonlinear adaptive control method and system for robotic arm motion control Technical Field

[0001] The present invention relates to the field of robot arm motion control, and in particular to a nonlinear adaptive control method and system for robot arm motion control. Background Art

[0002] With the continuous development of intelligent technology, people are increasingly using intelligent devices in their lives, work, and studies. The use of intelligent technology has improved the quality of life and increased the efficiency of their learning and work. In the field of robotic arm motion control, the robotic arm system is inherently nonlinear, time-varying, and uncertain. Therefore, the design of the control system requires the use of nonlinear and adaptive control algorithms.

[0003] Currently, all robotic arm motion control systems utilize the traditional PID control algorithm. The PID control algorithm is based on a linear system. When using the PID algorithm to control the motion of a nonlinear robotic arm, the challenge is how to tune the proportional, integral, and differential parameters of the PID. Due to the nonlinearity, time-varying nature, and uncertainty of the robotic arm system, offline or online identification of nonlinear or linearized models of the robotic arm, its load, and external disturbances is essential. The resulting models of the robotic arm, load, and external disturbances are used to tune the PID parameters in real time or in stages. Traditional PID algorithms maintain constant parameters throughout the entire control process. However, in practical applications, the entire controlled system cannot be predicted in advance, especially for general-purpose robotic arms, where the motion and environment are unpredictable. Therefore, fixed PID parameters cannot achieve high-performance control. While model identification can achieve relatively good control performance, it significantly increases system complexity, especially when performed online. Furthermore, model identification cannot guarantee the correct model and model parameters, making it difficult for such adaptive control systems to obtain industrial certification. Similarly, other adaptive or intelligent control methods including fuzzy control, sliding mode control, neural network-based control, model reference adaptation, etc. also have the problem of not being able to guarantee algorithm stability and stable operation under any working load interference environment.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far.

[0005] Summary of the Invention

[0006] Therefore, the present invention aims to provide a nonlinear adaptive control method and system for robotic arm motion control. This method eliminates the need for a robotic arm motion load model and external disturbance model, which are required for controller parameter tuning. This method addresses the difficulties of identifying the motion load model and tuning the time-varying controller parameters in existing robotic arm motion control technologies. Given that existing technologies have not yet effectively addressed these difficulties and issues, the present invention provides a very simple, effective, and high-performance robotic arm motion control method.

[0007] To achieve the above-mentioned object, a method for controlling the motion of a robotic arm of the present invention comprises the following steps: establishing a complete nonlinear dynamic model of a robotic arm having n connecting rods by using the Lagrangian dynamic modeling method; obtaining the measurement data of the joint rotation angle q of the robotic arm; calculating the second-order time derivative d of the joint rotation angle q of the robotic arm by using the digital differential method; 2 q / dt 2 ; According to the motion requirements of the robot arm, the ideal value of the robot arm joint angle q is given d ; Set the ideal value of the robot arm joint angle q d The difference between the measured value q is input to the motion controller; the output of the motion controller is related to the second-order time derivative d 2 q / dt 2 At the same time, the control instructions output by the incremental nonlinear inverse dynamics controller are input to the servo motor driver that drives the robotic arm.

[0008] The incremental nonlinear inverse dynamics controller is described as follows:

[0009]

[0010] Where Δu is the output command of the incremental nonlinear inverse dynamics controller; q is the joint angle of the manipulator; M(q) is the inertia matrix of the manipulator model; is the robot arm joint angular acceleration calculated from the robot arm joint angle q; v is the virtual control variable of the incremental nonlinear inverse dynamics controller.

[0011] The virtual control quantity of the incremental nonlinear inverse dynamics controller is:

[0012]

[0013] where e=qq d K is the difference between the robot arm joint angle and the required robot arm joint angle; p is the motion controller proportional parameter; K I Motion controller integral parameter; K D Motion controller derivative parameters.

[0014] The present invention also discloses a nonlinear adaptive control system for robotic arm motion control, comprising a processor and a memory; the memory stores computer-readable instructions, and the processor is used to run the computer-readable instructions, wherein the computer-readable instructions execute the above-mentioned nonlinear adaptive control method for robotic arm motion control when running.

[0015] In an embodiment of the present invention, the second-order time derivative of the robot arm joint angle (angular acceleration of the robot arm joint) is used to replace the required robot arm system, load and external interference models, and thus the system, load and external interference models are not required in inverse dynamics control, thereby solving the difficulties in identifying the robot arm system, load and external interference models and the technical problems of adjusting the time-varying parameters of the controller in the existing robot arm motion control technology.

[0016] According to another aspect of an embodiment of the present invention, a nonlinear adaptive control system for robotic arm motion control is also provided, comprising a processor and a memory; the memory stores computer-readable instructions, and the processor is used to run the computer-readable instructions, wherein the computer-readable instructions execute a nonlinear adaptive control method for robotic arm motion control when running. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] FIG1 is a flow chart of a nonlinear adaptive control method for manipulator motion control according to an embodiment of the present invention.

[0019] FIG2 is a structural block diagram of a nonlinear adaptive control system for manipulator motion control according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to enable 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 clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] According to an embodiment of the present invention, a nonlinear adaptive control method for robotic arm motion control is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0023] FIG1 is a flow chart of a nonlinear adaptive control method for manipulator motion control according to an embodiment of the present invention. As shown in FIG1 , the method includes the following steps:

[0024] Step S100, obtaining the measurement data of the robot arm joint angle q;

[0025] Step S200, using digital differentiation method to calculate the second-order time derivative d of the robot arm joint angle q 2 q / dt 2 ;

[0026] Step S300: The ideal value of the robot arm joint angle q d The difference between the measured value q is input to the motion controller;

[0027] Step S400: The output of the motion controller is compared with the second-order time derivative d 2 q / dt 2 At the same time, it is input to the incremental nonlinear inverse dynamics controller;

[0028] Step S500 , the output of the incremental nonlinear inverse dynamics controller is input to the servo motor driver driving the robotic arm;

[0029] Step S600 calculates the incremental output of the incremental inverse dynamics control;

[0030] Step S700 calculates the motion controller.

[0031] The robot arm motion control method comprises the following steps: using the Lagrangian dynamics modeling method to establish a complete nonlinear dynamic model corresponding to the robot arm with n links; obtaining the measurement data of the robot arm joint angle q; using the digital differential method to calculate the second-order time derivative d of the robot arm joint angle q 2 q / dt 2 ; According to the motion requirements of the robot arm, the ideal value of the robot arm joint angle q is given d ; Set the ideal value of the robot arm joint angle q d The difference between the measured value q is input to the motion controller; the output of the motion controller is related to the second-order time derivative d 2 q / dt 2 At the same time, the control instructions output by the incremental nonlinear inverse dynamics controller are input to the servo motor driver that drives the robotic arm.

[0032] The incremental nonlinear inverse dynamics controller is described as follows:

[0033]

[0034] Where Δu is the output command of the incremental nonlinear inverse dynamics controller; q is the joint angle of the manipulator; M(q) is the inertia matrix of the manipulator model; is the robot arm joint angular acceleration calculated from the robot arm joint angle q; v is the virtual control variable of the incremental nonlinear inverse dynamics controller.

[0035] The virtual control quantity of the incremental nonlinear inverse dynamics controller is:

[0036]

[0037] where e=qq d K is the difference between the robot arm joint angle and the required robot arm joint angle; p is the motion controller proportional parameter; K I Motion controller integral parameter; K D Motion controller differential parameters

[0038] This incremental inverse dynamics control method is based on the following principles.

[0039] Robotic arm model established using Lagrangian dynamics modeling method

[0040]

[0041] Where q is the joint angle of the robotic arm; is the angular velocity of the robot arm joint; is the angular acceleration of the manipulator joint; M(q) is the inertia matrix of the manipulator system; is the Coriolis force and centrifugal force vector of the system; C v is the joint viscous friction coefficient matrix; G(q) is the gravity vector; F is the external disturbance vector; Γ is the control torque vector.

[0042] Formula (1) can also be written as

[0043]

[0044] Simplified formula (2) is written as

[0045]

[0046] in

[0047]

[0048] u=Γ (5)

[0049] Expand the vector series of equation (4) at the sampling time point

[0050]

[0051] According to the time variation characteristics of each variable, under the condition of the existing sampling time, we can obtain

[0052]

[0053] in

[0054] Δu=(u k -u k-1 ) (8)

[0055]

[0056]

[0057]

[0058] e=qq d (12)

[0059] In formula (7) is the second time derivative of the manipulator joint angle, and is also the angular acceleration of the manipulator joint at sampling time k. From Equation (2), it can be seen that this angular acceleration contains all the information of the system. In the controller, angular acceleration is used to replace the system model required by the controller.

[0060] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0061] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0062] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0063] The units described as separate components may or may not be physically separate, and 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 units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0064] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0065] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can 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 aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc., various media that can store program code.

[0066] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A nonlinear adaptive control method for manipulator motion control, characterized in that: The following steps are involved: The Lagrangian dynamic modeling method is used to establish a complete nonlinear dynamic model of the manipulator with n links. Obtain the measurement data of the robot arm joint angle q; Calculate the second-order time derivative d of the manipulator joint angle q using digital differentiation method 2 q / dt 2 ; According to the motion requirements of the robot arm, the ideal value q of the robot arm joint angle is given d ; The ideal value of the robot arm joint angle q d The difference between the measured value q is input to the motion controller; The output of the motion controller is related to the second-order time derivative d 2 q / dt 2 At the same time, it is input to the incremental nonlinear inverse dynamics controller; The output of the incremental nonlinear inverse dynamics controller is input to the servo motor driver that drives the robotic arm.

2. The nonlinear adaptive control method for manipulator motion control according to claim 1, wherein: The incremental nonlinear inverse dynamics controller is described as follows: Where Δu is the output command of the incremental nonlinear inverse dynamics controller; q is the joint angle of the manipulator; M(q) is the inertia matrix of the manipulator model; is the robot arm joint angular acceleration calculated from the robot arm joint angle q; v is the virtual control variable of the incremental nonlinear inverse dynamics controller.

3. The nonlinear adaptive control method for manipulator motion control according to claim 2, wherein: The virtual control quantity of the incremental nonlinear inverse dynamics controller is: where e=qq d K is the difference between the robot arm joint angle and the required robot arm joint angle; p is the motion controller proportional parameter; K I Motion controller integral parameter; K D Motion controller derivative parameters.

4. A nonlinear adaptive control system for robotic arm motion control, characterized by: It includes a processor and a memory; the memory stores computer-readable instructions, and the processor is used to run the computer-readable instructions, wherein the computer-readable instructions, when running, execute the nonlinear adaptive control method for robotic arm motion control described in any one of claims 1 to 3.

Images