Neural control systems trained by dynamic gradient methods for automotive applications

The use of dynamic gradient-based training of neural controllers for automotive systems is illustrated. The authors use a recurrent structure that embeds an identification network and a neural controller and that properly treats both short- and long-term effects of controller weight changes. This results in an approximately optimal control strategy. Feedforward and hybrid feedforward-feedback neural controllers trained by dynamic backpropagation and a dynamic decoupled extended Kalman filter (DDEKF) are investigated. A quarter-car active suspension model is considered in both linear and nonlinear forms, and representative results are presented. Methods using higher-order information, e.g., DDEKF are very effective in comparison to methods based exclusively upon gradient descent, e.g., dynamic backpropagation (DBP). The use of a recurrent structure for obtaining derivatives for controller training is illustrated