Robust Adaptive Control Using Higher Order Neural Networks and Projection

By using dynamic higher order neural networks, we present a novel robust adaptive approach for a class of unknown nonlinear systems. Firstly, the neural networks are designed to identify the nonlinear systems. Dead-zone and projection techniques are applied to weights training, in order to avoid singular cases. Secondly, a linearization controller is proposed based on the neuro identifier. Since the approximation capability of the neural networks is limited, four types of compensators are addressed. We also proposed a robust neuro-observer, which has an extended Luenberger structure. Its weights are learned on-line by a new adaptive gradient-like technique. The control scheme is based on the proposed neuro-observer. The final structure is composed by two parts: the neuro-observer and the tracking controller. The simulations of a two-link robot show the effectiveness of the proposed algorithm.