基于极局部模型的机械臂自适应滑模控制

doi:10.13229/j.cnki.jdxbgxb20190489

摘要/Abstract

摘要：

针对多自由度机械臂在实现轨迹跟踪控制时过于依赖机械臂的精确数学模型和跟踪精度低等问题，提出了一种将自适应神经网络、极局部模型与积分型终端滑模相结合的控制方法。该方法使用一种基于时延估计的极局部模型来近似机械臂的动力学模型，利用自适应神经网络的非线性逼近性能，补偿时延估计产生的误差；对极局部模型设计积分型滑模控制器提高系统的收敛速度和控制精度，实现不依靠动力学模型的机械臂高精度轨迹跟踪。通过李雅普诺夫理论证明系统的稳定性和有限时间收敛性。最后通过实验，验证了该控制方法可以在完全不依赖模型信息的前提下实现机械臂的高速度和高精度跟踪控制。

关键词: 自动控制技术, 机械臂, 极局部模型, 滑模控制, 神经网络, 轨迹跟踪, 有限时间收敛

Abstract:

A model-free control method is proposed for trajectory tracking of multi-degree of freedom robotic manipulator to deal with the problems of relying too much on the precise mathematical model and low tracking accuracy. This method combines the adaptive neural network, the ultra-local model and the integral terminal sliding mode. First, an ultra-local model based on delay estimation is used to approximate the dynamic model of the manipulator. Then, a neural network is used to compensate the errors of delay estimation because of its nonlinear approximation capability. Finally, an integral sliding mode controller is designed for the ultra-local model to improve the convergence speed and control accuracy of the system and realize the high-precision trajectory tracking of the manipulator without relying on the dynamic model. The stability and finite time convergence of closed loop system are proved by Lyapunov theory. Experimental results show that the proposed control method can realize the high precision tracking control of the manipulator without depending on the model information completely.

Key words: automatic control technology, robotic manipulator, ultra-local mode, sliding mode control, neural network, trajectory tracking, finite time convergence

中图分类号:

TP241

吴爱国,韩俊庆,董娜. 基于极局部模型的机械臂自适应滑模控制[J]. 吉林大学学报(工学版), 2020, 50(5): 1905-1912.

Ai-guo WU,Jun-qing HAN,Na DONG. Adaptive sliding mode control based on ultra⁃local model for robotic manipulator[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1905-1912.

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控制算法	关节1	关节2	关节3
本文算法	0.000 16	0.000 18	0.001 10
对比算法1	0.000 83	0.000 38	0.001 70
对比算法2	0.005 00	0.003 70	0.013 00