Self-Adjusting Prescribed Performance Control for Nonlinear Systems with Input Saturation
<|reference_start|>Self-Adjusting Prescribed Performance Control for Nonlinear Systems with Input Saturation: 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) methods. Specifically, a performance index function (PIF) is introduced as a reference criterion, based on which the self-adjusting rates of the PFs are designed for different cases, exhibiting the following appealing features: 1) It can eliminate the need to prespecify the initial values of the PFs. In addition, it can also accommodate arbitrary magnitudes of initial errors while avoiding excessive initial control efforts. 2) Considering actuator saturation, this method can not only reduce the decay rates of the PFs when necessary to avoid violation of the PFs, but also increase the decay rates to accelerate system convergence when there is remaining control capacity. Finally, several numerical simulations are conducted to confirm the effectiveness and superiority of the proposed method.<|reference_end|>
@article{shao2024self-adjusting,
    title={Self-Adjusting Prescribed Performance Control for Nonlinear Systems with
  Input Saturation},
    author={Zhuwu Shao, Yujuan Wang, Huanyu Yang, Yongduan Song},
    journal={arXiv preprint arXiv:2404.13714},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13714},
    primaryClass={eess.SY cs.SY}
}