Neural network pole placement controller for nonlinear systems through linearisation

A new adaptive pole placement controller for unknown nonlinear plants is developed using a modified neural network. The modified neural network is composed of two parts: one is a linear neural network (LNN), which is the linearised model at the operating point; and the other is a multilayered feedforward neural network (MFNN), which approximates the nonlinear dynamics of the plant that can not be modelled by the LNN. Then a fast learning algorithm is presented for training the proposed neural network. The controller design is based on the LNN, and the output of the MFNN is considered as a measurable disturbance and is eliminated through feedforward control. Simulation results reveal that the proposed training algorithm is much faster than the standard algorithm and the new adaptive pole placement controller can effectively control a class of nonlinear plants