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ON ROBUSTNESS OF CONCURRENT LEARNING ADAPTIVE CONTROL TO TIME-VARYING DISTURBANCES AND SYSTEM UNCERTAINTIES

机译:时变干扰的并行学习自适应控制的鲁棒性和系统不确定性

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In this paper, we study the robustness characteristics of a recently developed concurrent learning model reference adaptive control approach to time-varying disturbances and system uncertainties. Specifically, the commonly-used constant (or slowly time-varying) assumption on disturbances and system uncertainties for this particular adaptive control approach is replaced with its bounded counterpart with piecewise continuous and bounded derivatives. Based on the Lyapunov's direct method, we then show that the solutions of the closed-loop system are uniformly ultimately bounded, without requiring a modification term in the adaptive law. Estimates for the ultimate bound and the exponential convergence rate to that ultimate bound are further provided. According to these estimates and illustrative numerical examples, similarities and differences between concurrent learning and one of the well-known robustness modifications in adaptive control, namely a modification, are explored.
机译:在本文中,我们研究时变扰动和系统不确定性的最新开发的并发学习模型参考自适应控制方法的鲁棒性特征。具体而言,针对此特定的自适应控制方法,通常将对干扰和系统不确定性的恒定(或时变缓慢)假设替换为带有分段连续和有界导数的有界对应项。然后,基于李雅普诺夫直接法,我们证明了闭环系统的解是一致最终有界的,而无需在自适应定律中进行修改。进一步提供了极限界限的估计值和该极限界限的指数收敛速度。根据这些估计和说明性的数值示例,探索了并行学习与自适应控制中众所周知的鲁棒性修改之一(即修改)之间的相似性和差异。

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