Development of an adaptive radial basis function neural network estimator-based continuous sliding mode control for uncertain nonlinear systems
Development of an adaptive radial basis function neural network estimator-based continuous sliding mode control for uncertain nonlinear systems
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DOI：
10.1016/j.isatra.2018.11.021
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发表时间：
2019-04-01
期刊：
影响因子：
7.3
通讯作者：
Sarhan, Amany M.
中科院分区：
文献类型：
--
作者：
Moawad, Nada M.;Elawady, Wael M.;Sarhan, Amany M.
针对不确定非线性系统的轨迹跟踪问题，提出了一种基于自适应神经网络的非线性控制器。所采用的控制算法结合了连续二阶滑模控制（CSOSMC）、径向基函数神经网络（RBFNN）和自适应控制方法。首先，将线性不确定系统的二阶滑模控制方法（SOSMC）推广到非线性不确定系统。其次，采用基于自适应径向基函数神经网络估计的连续二阶滑模控制算法（CSOSMC-ANNE）。在CSOSMC-ANNE控制方法中，利用具有自适应参数的径向基函数神经网络来逼近未知系统参数并提高抗扰动性能。同时，用平滑的连续控制动作取代了SOSMC的断续开关控制，完全消除了抖振现象。利用李雅普诺夫稳定性方法证明了闭环系统的收敛性和全局稳定性。利用非线性倒立摆系统的动力学模型进行了数值仿真，验证了所提控制方案的有效性和优越性。(c) 2018年。Elsevier Ltd.出版。版权所有。
In this paper an adaptive neural network (NN)-based nonlinear controller is proposed for trajectory tracking of uncertain nonlinear systems. The adopted control algorithm combines a continuous second-order sliding mode control (CSOSMC), the radial basis function neural network (RBFNN) and the adaptive control methodology. First, a second-order sliding mode control scheme (SOSMC), which is published recently in literature for linear uncertain systems, is extended for nonlinear uncertain systems. Second, an adaptive radial basis function neural network estimator-based continuous second order sliding mode control algorithm (CSOSMC-ANNE) is adopted. In CSOSMC-ANNE control methodology, a radial basis function neural network with adaptive parameters is exploited to approximate the unknown system parameters and improve performance against perturbations. Also, the discontinuous switching control of SOSMC is supplanted with a smooth continuous control action to completely eliminate the chattering phenomenon. The convergence and global stability of the closed-loop system are proved using Lyapunov stability method. Numerical computer simulations, with dynamical model of the nonlinear inverted pendulum system, are presented to demonstrate the effectiveness and advantages of the presented control scheme. (C) 2018 ISA. Published by Elsevier Ltd. All rights reserved.