Self-Adjusting Prescribed Performance Control for Nonlinear Systems with Input Saturation
Zhuwu Shao, Yujuan Wang, Huanyu Yang, Yongduan Song

TL;DR
This paper introduces a self-adjusting prescribed performance control scheme for nonlinear systems with input saturation, enabling flexible convergence rate adjustment without predefining decay rates, thus improving system performance and safety.
Contribution
It proposes a novel control method that adaptively adjusts performance function decay rates based on a performance index, addressing actuator saturation and initial error issues.
Findings
Effective in avoiding error violation beyond prescribed envelopes
Reduces initial control efforts and accommodates arbitrary initial errors
Enhances convergence speed while ensuring system safety
Abstract
Among the existing works on enhancing system performance via prescribed performance functions (PPFs), the decay rates of PPFs need to be predetermined by the designer, directly affecting the convergence time of the closed-loop system. However, if only considering accelerating the system convergence by selecting a big decay rate of the performance function, it may lead to the severe consequence of closed-loop system failure when considering the prevalent actuator saturation in practical scenarios. To address this issue, this work proposes a control scheme that can flexibly self-adjust the convergence rates of the performance functions (PFs), aiming to achieve faster steady-state convergence while avoiding the risk of error violation beyond the PFs' envelopes, which may arise from input saturation and improper decay rate selection in traditional prescribed performance control (PPC)…
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Taxonomy
TopicsAdvanced Control Systems Optimization · Iterative Learning Control Systems · Extremum Seeking Control Systems
