STABLE ADAPTIVE CONTROL UNDER UNMEASURABLE PLANT STATES

A dynamic neural network based algorithm for learning control of an unknown nonlinear continuous-time multiple-input-multiple-output plant with unmeasurable states is proposed in this paper. A new dynamic neural network structure is utilised to model the unknown plant dynamics through modelling the input-output mapping. A feedback linearization is applied to design controller for the neural model and the neural states are used as a source of precious information about current plant dynamics. A gradient based update of the weights is performed at discrete time instants over a moving measurement window in order to reduce the model output – real output mismatch. The learning controller is applied to a double link robot arm. Stability of the system is analysed through ultimate boundedness of all signals.