Robust Learning Control by Neural Network for an Active Four Wheel Steering System

摘要

The dynamics of a four wheel steering(4WS) system inherently has model uncertainties, resulting in degradation of vehicle handling performance. To compensate for model uncertainties of the vehicle system, a nonlinear neural network control scheme is proposed and evaluated. The control scheme is composed of a conventional model reference control term and a compensator term. The compensator term is generated by a neural network whose teaching signal is the error between the actual plant and the reference model. This control scheme does not require an inverse dynamics of the plant or a Jacobian information of the learned plant in order to carry out on-line learning. Since the teaching signal of this scheme is simple to compute, fast convergence can be realized. Adaptive capability of the neural network compensator for the structured uncertainties has been demonstrated. The validity and effectiveness of the proposed control scheme for a vehicle four wheel steering are verified by computer simulations. It is demonstrated that the 4WS system with the neural network control scheme can be improved dynamically over the conventional two wheel steering(2WS) system which is open loop system.