Neuro-hierarchical sliding mode control for a class of under-actuated systems
by Dianwei Qian, Xiangjie Liu, Jianqiang Yi, Chengdong Li
International Journal of Modelling, Identification and Control (IJMIC), Vol. 13, No. 4, 2011

Abstract: A neuro-hierarchical sliding mode controller is presented for a class of under-actuated systems with a stable equilibrium point. Such controller is combined with the concept of neural networks and the methodology of hierarchical sliding mode control. At first, the hierarchical sliding mode control law is designed for the class as follows. The system is divided into several subsystems and the sliding surface of every subsystem is defined. Then, the sliding surface of one subsystem is selected as the first layer sliding surface. The first layer sliding surface is then to construct the second layer sliding surface with the sliding surface of another subsystem. This process continues until the sliding surfaces of all the subsystems are included. The control law is derived from Lyapunov theorem. By aiming at unknown factors and uncertainties, the neural networks are designed to approximate the terms of the hierarchical sliding mode control law. The asymptotic stability of the entire sliding surfaces and the convergence of the network weights are proven theoretically. Simulation and physical experiment results show the controller's validity and robustness.

Online publication date: Sat, 21-Mar-2015

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