Adaptive Iterative Learning Control for Robot Manipulators With Time-Varying Parameters and Arbitrary Initial Errors

Qiuzhen Yan,Yuntao Zhang,Jianping Cai,Zhi Yang

doi:10.1109/access.2021.3136896

Abstract

In this paper, a novel error-tracking adaptive iterative learning control scheme is proposed to solve trajectory-tracking problem for a class of robot manipulators with time-varying parameters and arbitrary initial errors. Firstly, desired error trajectories are constructed for implementing error tracking strategy in the robotic systems, so as to relax the requirement of zero initial errors, which is usually assumed to be met in traditional iterative learning control algorithms. Secondly, with the help of reasonable parameterization to the robotic dynamics, the adaptive iterative control law is designed by using Lyapunov approach. Projection-free combined time-domain and iteration-domain adaptive learning strategy is adopted to estimate the unknown time-invariant parametric uncertainties, and difference learning strategy is adopted to estimate unknown time-varying parametric uncertainties. As the iteration number increases, the system error follows its desired error trajectory over the whole interval. As a result, system state can perfectly track the reference signal in the predetermined part interval. In the end, several numerical simulations are presented to demonstrate the effectiveness of the designed control scheme.

Highlights

Iterative learning control (ILC) technique is effective in dealing with repetitive processes [1]− [4]
The repetitive learning control algorithm for robotic systems with time-varying parameters proposed in [38] can be used in the situation that two following assumptions are satisfied: (1) The reference trajectory is smoothly closed, i.e., the initial state of reference trajectory must be equal to the final state of reference trajectory; (2) The initial state of current cycle must be equal to the final state of previous cycle
The main contributions of this work can be summarized as follows: 1) Error-tracking adaptive ILC is proposed for robot manipulators with time-varying parameters, which can guarantee the performance and overcome the initial position problem of ILC

Summary

INTRODUCTION

Iterative learning control (ILC) technique is effective in dealing with repetitive processes [1]− [4]. In [33], a neural network-based adaptive ILC scheme is developed to solve the trajectory tracking problem for rigid robot manipulators with arbitrary initial errors, where the technique of time-varying boundary layer [37] is used to remove the zero initial error condition. The repetitive learning control algorithm for robotic systems with time-varying parameters proposed in [38] can be used in the situation that two following assumptions are satisfied: (1) The reference trajectory is smoothly closed, i.e., the initial state of reference trajectory must be equal to the final state of reference trajectory; (2) The initial state of current cycle must be equal to the final state of previous cycle. The main contributions of this work can be summarized as follows: 1) Error-tracking adaptive ILC is proposed for robot manipulators with time-varying parameters, which can guarantee the performance and overcome the initial position problem of ILC. To demonstrate the effectiveness of the proposed errortracking adaptive ILC scheme, several numerical simulations are shown in Section 6, followed by Section 7 which concludes the work

PROBLEM FORMULATION

THE DESIGN OF ADAPTIVE LEARNING

CONVERGENCE ANALYSIS

ILLUSTRATIVE EXAMPLE

CONCLUSION