Sliding mode trajectory tracking control of manipulator based on adaptive neural network

doi:10.3785/j.issn.1006-754X.2023.00.050

Chin J Eng Design

2023, Vol. 30

Issue (4): 512-520 DOI: 10.3785/j.issn.1006-754X.2023.00.050

Mechanical Optimization Design

Sliding mode trajectory tracking control of manipulator based on adaptive neural network

Qiqi LI¹(

),Xiangrong XU¹(

),Hui ZHANG^1,²

^1.School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China
^2.School of Metallurgical Engineering, Anhui University of Technology, Maanshan 243032, China

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Abstract

In view of the adverse effects of dynamic modeling errors and uncertain perturbations on the high-precision trajectory tracking control of the end of manipulators, a novel sliding mode control strategy for manipulators based on the adaptive neural network was proposed. The control strategy could be divided into three parts: adaptive neural network compensation term, switching control term and equivalent control term. The introduction of adaptive neural network avoided the influence of modeling error and unknown external disturbance on the manipulator system, and improved the trajectory tracking accuracy. The switching control term could enable the manipulator system performance to quickly approach the sliding mode surface while approaching the equilibrium point at a very small rate, so as to ensure system stability while avoiding excessive chattering. The equivalent control term was used to compensate the gravity term and Coriolis force term of the manipulator dynamics model, which realized the linearization of the model and ensured the system control accuracy. Finally, the stability of the designed control system was proved by constructing the Lyapunov function, and the simulation experiment and comparison experiment were carried out in MATLAB/Simulink environment and robot system toolbox. The results showed that the proposed control algorithm could achieve high-precision trajectory tracking while maintaining the stability of the manipulator, which verified the correctness and superiority of this control algorithm. The adaptive neural network sliding mode control algorithm provides a solution for enhancing the trajectory tracking accuracy of the end of manipulators.

Key words： manipulator high-precision trajectory tracking adaptive neural network

Received: 23 December 2022 Published: 04 September 2023

CLC:

TP 242.2

Corresponding Authors: Xiangrong XU E-mail: 13637099782@163.com;xuxr@ahut.edu.cn

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Cite this article:

Qiqi LI,Xiangrong XU,Hui ZHANG. Sliding mode trajectory tracking control of manipulator based on adaptive neural network. Chin J Eng Design, 2023, 30(4): 512-520.

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https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2023.00.050 OR https://www.zjujournals.com/gcsjxb/Y2023/V30/I4/512

基于自适应神经网络的机械臂滑模轨迹跟踪控制

针对动态建模误差和不确定性扰动对机械臂末端高精度轨迹跟踪控制的不利影响，提出了一种新型的基于自适应神经网络的机械臂滑模控制策略。该控制策略可分为三部分：自适应神经网络补偿项、切换控制项和等效控制项。自适应神经网络的引入，避免了建模误差和外界未知扰动对机械臂系统的影响，提高了轨迹跟踪精度；切换控制项可使机械臂系统性能在迅速趋近滑模面的同时以很小的速率趋近平衡点，既能保证系统稳定，又能避免系统过于抖振；等效控制项用于对机械臂动力学模型的重力项和哥氏力项进行补偿，实现对模型的线性化处理，保证了系统的控制精度。最后，通过构造Lyapunov函数验证了所设计控制系统的稳定性，并在MATLAB/Simulink环境下和机器人系统工具箱中开展仿真实验和对比实验。结果表明，所提出的控制算法能够在保持机械臂稳定性的同时实现高精度的轨迹跟踪，验证了该控制算法的有效性和优越性。自适应神经网络滑模控制算法可为提高机械臂末端轨迹跟踪精度提供一种解决方案。

关键词： 机械臂, 高精度, 轨迹跟踪, 自适应神经网络

Fig.1 Structure of six-degree-of-freedom manipulator system and its D-H coordinate system

Fig.2 RBF neural network structure

Fig.3 Block diagram of manipulator control system

关节

θ i / r a d

α i - 1 /

rad

d i

a i - 1

θ₁

θ₂

θ₃

θ₄

θ₅

θ₆

π/2

0.275 5

-0.009 8

-0.311 0

-0.263 8

0.410 0

Table 1 D-H parameters of manipulator

Fig.4 Simulation model of manipulator system

Fig.5 Expected trajectory of the end of manipulator

Fig 6 End position tracking results of manipulator based on different control algorithms

Fig.7 Angle tracking results of each joint of manipulator based on different control algorithms

Table 2 Comparison of angle tracking error for each joint of manipulator based on different control algorithms

Fig.8 Torque variation curve of each joint of manipulator during operation


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