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This paper presents the design, implementation, and comparative analysis of two intelligent neural network based controllers employed for nonlinear dynamic compensation and adaptive trajectory tracking of a mobile robot system. The first control law is an integration of a backstepping controller with a neural network which is designed to learn the inverse dynamic model of the robot and to compensate...
This paper proposes a method to design a robust reinforcement learning-based tracking control scheme for the wheeled mobile robot. A policy iteration algorithm and a neural network are used to design an adaptive critic robust controller. A H?? - tracking performance index optimal function is evaluated by this con troller. The stability of the closed-loop system while learning is proven by Lyapunov...
A nonaffine discrete-time system represented by the nonlinear autoregressive moving average with eXogenous input (NARMAX) representation with unknown nonlinear system dynamics is considered. An equivalent affinelike representation in terms of the tracking error dynamics is first obtained from the original nonaffine nonlinear discrete-time system so that reinforcement-learning-based near-optimal neural...
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