Neural Network Based Adaptive Tracking Control for a Class of Pure Feedback Nonlinear Systems With Input Saturation

Nassira Zerari; Mohamed Chemachema; Najib Essounbouli

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Neural Network Based Adaptive Tracking Control for a Class of Pure Feedback Nonlinear Systems With Input Saturation

机译：具有输入饱和的一类纯反馈非线性系统的神经网络自适应跟踪控制

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In this paper,an adaptive neural networks (NNs) tracking controller is proposed for a class of single-input/singleoutput (SISO) non-affine pure-feedback non-linear systems with input saturation.In the proposed approach,the original input saturated nonlinear system is augmented by a low pass filter.Then,new system states are introduced to implement states transformation of the augmented model.The resulting new model in affine Brunovsky form permits direct and simpler controller design by avoiding back-stepping technique and its complexity growing as done in existing methods in the literature.In controller design of the proposed approach,a state observer,based on the strictly positive real (SPR) theory,is introduced and designed to estimate the new system states,and only two neural networks are used to approximate the uncertain nonlinearities and compensate for the saturation nonlinearity of actuator.The proposed approach can not only provide a simple and effective way for construction of the controller in adaptive neural networks control of non-affine systems with input saturation,but also guarantee the tracking performance and the boundedness of all the signals in the closed-loop system.The stability of the control system is investigated by using the Lyapunov theory.Simulation examples are presented to show the effectiveness of the proposed controller.

机译：针对一类具有输入饱和度的单输入/单输出（SISO）非仿射纯反馈非线性系统，提出了一种自适应神经网络跟踪控制器。通过低通滤波器对非线性系统进行扩充，然后引入新的系统状态以实现扩充后的模型的状态转换。由此产生的仿射Brunovsky形式的新模型可以避免后推技术及其复杂性的增长，从而可以直接，简化控制器设计在该控制器的控制器设计中，引入并设计了基于严格正实（SPR）理论的状态观测器来估计新的系统状态，并且仅使用了两个神经网络。该方法不仅可以为执行机构的构造提供简单有效的方法，而且可以对执行机构的饱和非线性进行补偿。在具有输入饱和的非仿射系统的自适应神经网络控制中，该控制器还保证了闭环系统中所有信号的跟踪性能和有界性。利用李雅普诺夫理论研究了控制系统的稳定性。给出了仿真示例，以说明所提出控制器的有效性。

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来源
《自动化学报（英文版）》 |2019年第1期|278-290|共13页
作者
Nassira Zerari; Mohamed Chemachema; Najib Essounbouli;
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作者单位

Department of Electronics,Faculty of Technology, University of Constantine 1, Compus A.Hamani, Route Aine El Bey, 2500 Constantine, Algeria;

Department of Electronics,Faculty of Technology, University of Constantine 1, Compus A.Hamani, Route Aine El Bey, 2500 Constantine, Algeria;

CReSTIC Laboratory, University of Reims Champagne Ardennes, France;

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Neural Network Based Adaptive Tracking Control for a Class of Pure Feedback Nonlinear Systems With Input Saturation

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