Rollover prediction and control in heavy vehicles via recurrent neural networks

A state predictor is developed in order to estimate roll angle and lateral acceleration for tractor-semitrailers. Based on this prediction, an active control system is designed to prevent rollover. In order to develop this control structure, a high order recurrent neural network is used to model the unknown tractor semitrailer system; a learning law is obtained using the Lyapunov methodology. Then a control law, which stabilizes the reference tracking error dynamics, is developed using control Lyapunov functions. Via simulations, the control scheme is applied for speed-yaw rate trajectory tracking in a tractor-semitrailer during a cornering situation.