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一种用于气动肌肉驱动单连杆机器人手臂的新型自适应协同控制设计

A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles.

作者信息

Humaidi Amjad J, Ibraheem Ibraheem Kasim, Azar Ahmad Taher, Sadiq Musaab E

机构信息

Control and Systems Engineering Department, University of Technology, Baghdad 10001, Iraq.

Department of Electrical Engineering, College of Engineering, University of Baghdad, Baghdad 10001, Iraq.

出版信息

Entropy (Basel). 2020 Jun 30;22(7):723. doi: 10.3390/e22070723.

Abstract

This paper suggests a new control design based on the concept of Synergetic Control theory for controlling a one-link robot arm actuated by Pneumatic artificial muscles (PAMs) in opposing bicep/tricep positions. The synergetic control design is first established based on known system parameters. However, in real PAM-actuated systems, the uncertainties are inherited features in their parameters and hence an adaptive synergetic control algorithm is proposed and synthesized for a PAM-actuated robot arm subjected to perturbation in its parameters. The adaptive synergetic laws are developed to estimate the uncertainties and to guarantee the asymptotic stability of the adaptive synergetic controlled PAM-actuated system. The work has also presented an improvement in the performance of proposed synergetic controllers (classical and adaptive) by applying a modern optimization technique based on Particle Swarm Optimization (PSO) to tune their design parameters towards optimal dynamic performance. The effectiveness of the proposed classical and adaptive synergetic controllers has been verified via computer simulation and it has been shown that the adaptive controller could cope with uncertainties and keep the controlled system stable. The proposed optimal Adaptive Synergetic Controller (ASC) has been validated with a previous adaptive controller with the same robot structure and actuation, and it has been shown that the optimal ASC outperforms its opponent in terms of tracking speed and error.

摘要

本文基于协同控制理论的概念,提出了一种新的控制设计方法,用于控制在二头肌/三头肌相对位置由气动人工肌肉(PAM)驱动的单连杆机器人手臂。协同控制设计首先基于已知的系统参数建立。然而,在实际的PAM驱动系统中,不确定性是其参数中固有的特性,因此针对参数受到扰动的PAM驱动机器人手臂,提出并合成了一种自适应协同控制算法。开发了自适应协同律来估计不确定性,并保证自适应协同控制的PAM驱动系统的渐近稳定性。这项工作还通过应用基于粒子群优化(PSO)的现代优化技术来调整其设计参数以实现最佳动态性能,从而提高了所提出的协同控制器(经典和自适应)的性能。通过计算机仿真验证了所提出的经典和自适应协同控制器的有效性,结果表明自适应控制器能够应对不确定性并保持控制系统稳定。所提出的最优自适应协同控制器(ASC)已与具有相同机器人结构和驱动方式的先前自适应控制器进行了验证,结果表明最优ASC在跟踪速度和误差方面优于其对手。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb4/7517262/a8ab33e6d025/entropy-22-00723-g001.jpg

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