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基于滤波器连接的开关滞环量化器的一类具有执行器滞后的时滞切换非线性系统的规定性能量化跟踪控制

Prescribed Performance Quantized Tracking Control for a Class of Delayed Switched Nonlinear Systems With Actuator Hysteresis Using a Filter-Connected Switched Hysteretic Quantizer.

作者信息

Kamali Sara, Tabatabaei Seyyed Mostafa, Arefi Mohammad Mehdi, Yin Shen

出版信息

IEEE Trans Neural Netw Learn Syst. 2022 Jan;33(1):61-74. doi: 10.1109/TNNLS.2020.3027492. Epub 2022 Jan 5.

DOI:10.1109/TNNLS.2020.3027492
PMID:33074825
Abstract

This article proposes a prescribed adaptive backstepping scheme with new filter-connected switched hysteretic quantizer (FCSHQ) for switched nonlinear systems with nonstrict-feedback structure and time-delay. The system model is subjected to unknown functions, unknown delays, and unknown Bouc-Wen hysteresis nonlinearity. The coexistence of quantized input and actuator hysteresis may deteriorate the shape of hysteresis loop and, consequently, fail to guarantee the stability. To deal with this issue, a new FCSHQ is introduced to smooth the input hysteresis. This adaptive filter also provides us a degree of freedom at choosing the desired communication rate. The repetitive differentiations of virtual control laws and existing a lot of learning parameters in the neural network (NN)-based controller may result in an algebraic loop problem and high computational time, especially in a nonstrict-feedback form. This challenge is eased by the key advantage of NNs' property where the upper bound of the weight vector is employed. Then, by an appropriate Lyapunov-Krasovskii functional, a common Lyapunov function is presented for all subsystems. It is shown that the proposed controller ensures the predefined output tracking accuracies and boundedness of the closed-loop signals under any arbitrary switching. Finally, the proposed control scheme is verified on a practical example where simulation results demonstrate the effectiveness of the proposed scheme.

摘要

本文针对具有非严格反馈结构和时滞的切换非线性系统,提出了一种带有新型滤波器连接切换滞环量化器(FCSHQ)的预设自适应反步控制方案。系统模型受到未知函数、未知时滞和未知Bouc-Wen滞环非线性的影响。量化输入和执行器滞环的共存可能会使滞环形状恶化,从而无法保证稳定性。为解决这个问题,引入了一种新型FCSHQ来平滑输入滞环。这种自适应滤波器还为我们在选择期望通信速率方面提供了一定的自由度。虚拟控制律的重复求导以及基于神经网络(NN)的控制器中存在大量学习参数,可能会导致代数环问题和高计算时间,特别是在非严格反馈形式中。神经网络特性的关键优势,即利用权重向量的上界,缓解了这一挑战。然后,通过适当的Lyapunov-Krasovskii泛函,为所有子系统提出了一个公共Lyapunov函数。结果表明,所提出的控制器在任意切换下确保了预定义的输出跟踪精度和闭环信号的有界性。最后,在一个实际例子中验证了所提出的控制方案,仿真结果证明了该方案的有效性。

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