Abstract
A direct adaptive neural network tracking control scheme is presented for a class of SISO affine nonlinear uncertain systems. Uncertainties meet the match conditions. Parameters in neural networks are updated using a gradient descent method which designed in order to minimize a quadratic cost function of the error between the unknown ideal implicit controller and the used neural networks controller. No robustifying control term is used in controller. The convergence of adaptive parameters and tracking error and the boundedness of all states in the corresponding closed-loop system are demonstrated by Lyapunov stability theorem.Simulation results illustrate the availability of this method .
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