An Improved Iterative Learning Control for Uncertain Multi-Axis Systems

A. Armstrong, A. Alleyne
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引用次数: 2

Abstract

For learning control algorithms to date, the convergence rate in the iteration domain depends on the level of plant knowledge. This work presents a Fast Cross-coupled Iterative Learning Control (F-CCILC) scheme to overcome the current limitations in learning control algorithms. F-CCILC achieves fast convergence for multi-input multi-output (MIMO) systems with high model uncertainty. The approach uses involves using a novel error term in the ILC learning law based on techniques from Sliding Mode Control (SMC). The input signal is guaranteed to remain bounded in the time and iteration domains, and the approach does not require end-user tuning of arbitrary gains. In this paper, the design for the F-CCILC system is presented, and the performance of this system is compared to the performance of existing ILC control schemes via simulations and experimental testing. Compared to the current control methods, the simulation results demonstrate increased robustness and learning speeds for multi-axis systems with significant model uncertainty.
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不确定多轴系统的改进迭代学习控制
对于迄今为止的学习控制算法,迭代域的收敛速度取决于植物知识的水平。本文提出了一种快速交叉耦合迭代学习控制(F-CCILC)方案,以克服当前学习控制算法的局限性。F-CCILC对模型不确定性较大的多输入多输出(MIMO)系统实现了快速收敛。该方法在基于滑模控制(SMC)技术的ILC学习律中引入了一种新的误差项。输入信号保证在时间和迭代域中保持有界,并且该方法不需要最终用户调整任意增益。本文介绍了F-CCILC系统的设计,并通过仿真和实验测试,将该系统的性能与现有ILC控制方案的性能进行了比较。与现有的控制方法相比,仿真结果表明,对于具有显著模型不确定性的多轴系统,该方法具有更高的鲁棒性和学习速度。
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