Haibo Gao, Xingguo Song, Liang Ding, and Zongquan Deng
Nonholonomic system, wheeled mobile robot, feedback error learning, radial basis function, neural networks, Lyapunov theory
In this paper, we present an adaptive feedback error learning (AFEL) control scheme that are suitable for a class of nonholonomic wheeled mobile robots with uncertainties. The proposed algorithm employs nonlinear function approximation with automatic growth of the neural network (NN) learning according to the nonlinearities and the working domain of the tracking control system. The unknown function in dynamical system is approximated by training nonlinear NN models, and, imperfect approximation errors of NNs are relaxed by designing parallel robust term. Lyapunov synthesis is proposed for AFEL control design with guaranteed stability. Inspired by composite adaptive control scheme, the proposed adaptive control algorithm employs both closed-loop tracking errors and estimation errors to optimize the parameters by NN online weight tuning algorithms, which guarantee small tracking errors and no loss of stability in robot motion with bounded input signals. We demonstrate superior tracking results using the proposed AFEL control method in various Matlab simulations.
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