A neural network-based adaptive pole placement controller for nonlinear systems

Abstract

A new adaptive pole placement controller for unknown nonlinear systems 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 linearized model at the operating point; and the other is a multitayered feedforward neural network (MFNN), which approximates the nonlinear dynamics of the system that cannot 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 back propagation (BP) algorithm and the new adaptive pole placement controller can effectively control a class of nonlinear systems.

Additional information

Notes on contributors

WANG FULI

e-mail: [email protected]