Neural Network Inverse Optimal Control of Ground Vehicles

In this paper an active controller for ground vehicles stability is presented. The objective of this controller is to force the vehicle to track a desired reference, ensuring safe driving conditions in the case of adhesion loss during hazardous maneuvers. To this aim, a nonlinear discrete-time inverse optimal control based on a neural network identification is designed, using a recurrent high order neural network (RHONN) trained by an Extended Kalman Filter. The RHONN ensures stability of the identification error, while the controller ensures the stability of the tracking errors. Moreover, a discrete-time reduced order state observer is utilized to reconstruct the lateral vehicle dynamic not usually available. For the control problem, the references of the lateral velocity and yaw rate are given by a dynamic system mimicking an ideal vehicle having not-decreasing tire lateral characteristics. The proposed approach avoids the identification of the Pacejka's lateral parameters of the tires, so simplifying the input control determination. Moreover, an optimal control is proposed to optimize the actuator effort and power, usually bounded. Control gains are determined using optimal "nature-inspired" algorithms such as particle swarm optimization. Test maneuvers, performed through the full vehicle simulator CarSim (R), have been used to test correctness, quality and performances of the observer, the neural identifier and the inverse optimal controller. Robustness of the reduced order discrete-time state observer is also discussed for different sample times. Finally, a fair comparison between optimal and non-optimal control schemes is presented, highlighting the numerical results obtained in simulation.