A disturbance attenuating adaptive neural network controller for multi-input nonlinear systems

An adaptive neural network controller for multi-input nonlinear, affine in the control dynamical systems with unknown nonlinearities is designed, capable of attenuating L₂,L_∞ external disturbances. In the absence of disturbances, a uniform ultimate boundedness property of the tracking error with respect to an arbitrarily small set around the origin is guaranteed, as well as uniform boundedness of all the signals in the closed loop. Possible division by zero is avoided with the use of a novel resetting procedure, capable of guaranteeing the boundedness away from zero of certain signals. Simulations illustrate the approach.