Adaptive robust tracking of nonlinear systems and with an application to a robotic manipulator
Systems & Control Letters
Discrete adaptive sliding-mode tracking controller
Automatica (Journal of IFAC)
Direct adaptive fuzzy output tracking control of nonlinear systems
Fuzzy Sets and Systems - Featured Issue: Selected papers from ACIDCA 2000
Brief paper: Adaptive tracking of nonlinear systems with non-symmetric dead-zone input
Automatica (Journal of IFAC)
Technical communique: H∞ model reduction for uncertain switched linear discrete-time systems
Automatica (Journal of IFAC)
Brief paper: Periodic stabilizability of switched linear control systems
Automatica (Journal of IFAC)
Analysis and synthesis of switched linear control systems
Automatica (Journal of IFAC)
Journal of Intelligent and Robotic Systems
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In this paper, the adaptive tracking control problem for a class of switched affine nonlinear systems is investigated. We employ RBF neural networks (RBF NNs) to approximate unknown nonlinear functions. Due to the existence of approximation errors of the neural networks and external disturbance, we, respectively, utilize sliding mode method and H~ method as the robust controller to enhance system robustness and maintain boundedness. In addition, admissible switching laws are constructed and the weights of RBF NNs updated laws are chosen by switched Lyapunov function approach. With the two proposed methods, we can both prove that the resulting closed-loop switched system is robustly stable and uniformly ultimately bounded (UUB), and the output tracking errors converge to 0. Finally, we give a simulation example to demonstrate the proposed methods and do a comparative analysis.