Wei Liu;Huanyu Zhao;Yeqin Wang;Shengyuan Xu;Ju H. Park
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引用次数: 0
Abstract
This article presents a novel command-filter-based finite-time fuzzy preassigned performance adaptive control approach for nonlinear systems with exogenous disturbances and asymmetric time-varying constraints. Combining the asymmetric barrier Lyapunov function (ABLF) with preassigned performance control (PPC), it is ensured that the state
$x_{1}$
satisfies the required asymmetric time-varying state constraints (ATSCs), and the system output y can track the desired signal with achieving preassigned performance indices (PPIs). The command-filter-based control design not only circumvents the computing complexity in the backstepping but also reduces the conservativeness of the assumption regarding the desired signal and the time-varying asymmetric constraints. Additionally, the nonlinear disturbance observer method (NDOM) is employed to effectively estimate unknown exogenous disturbances and enhance the robustness of the closed-loop system. Through rigorous theoretical analysis, the proposed finite-time command filter-based control method effectively ensures that all variables are bounded within a finite time. The practicality and feasibility of the proposed approach are further validated by simulation results.
本文针对具有外生干扰和非对称时变约束的非线性系统,提出了一种新颖的基于指令滤波的有限时间模糊预分配性能自适应控制方法。将非对称障碍李亚普诺夫函数(ABLF)与预分配性能控制(PPC)相结合,可确保状态 $x_{1}$ 满足所需的非对称时变状态约束(ATSC),并且系统输出 y 可以在实现预分配性能指标(PPI)的情况下跟踪所需的信号。基于指令滤波器的控制设计不仅规避了反步法的计算复杂性,还降低了对期望信号和非对称时变约束条件假设的保守性。此外,还采用了非线性扰动观测器方法(NDOM)来有效估计未知的外生扰动,增强闭环系统的鲁棒性。通过严格的理论分析,所提出的基于有限时间指令滤波器的控制方法能有效确保所有变量在有限时间内都是有界的。仿真结果进一步验证了所提方法的实用性和可行性。
期刊介绍:
The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.