Control allocation algorithm for over-actuated electric vehicles

A control allocation algorithm based on pseudo-inverse method was proposed for the over-actuated system of four in-wheel motors independently driving and four-wheel steering-by-wire electric vehicles in order to improve the vehicle stability. The control algorithm was developed using a two-degree-of-freedom(DOF) vehicle model. A pseudo control vector was calculated by a sliding mode controller to minimize the difference between the desired and actual vehicle motions. A pseudo-inverse controller then allocated the control inputs which included driving torques and steering angles of the four wheels according to the pseudo control vector. If one or more actuators were saturated or in a failure state, the control inputs are re-allocated by the algorithm. The algorithm was evaluated in Matlab/Simulink by using an 8-DOF nonlinear vehicle model. Simulations of sinusoidal input maneuver and double lane change maneuver were executed and the results were compared with those for a sliding mode control. The simulation results show that the vehicle controlled by the control allocation algorithm has better stability and trajectory-tracking performance than the vehicle controlled by the sliding mode control. The vehicle controlled by the control allocation algorithm still has good handling and stability when one or more actuators are saturated or in a failure situation.

An adaptive cascade trajectory tracking control for over-actuated autonomous electric vehicles with input saturation

This paper presented a novel adaptive cascade nonlinear trajectory tracking control scheme of over-actuated autonomous electric vehicles involving input saturation. First, a nonlinear vehicle dynamic model with input saturation is established, which can accurately describe the features of uncertainties and coupling of autonomous electric vehicles, and the hyperbolic tangent function is designed to estimate the saturation function for dealing with the input saturation problem. Then, a novel adaptive cascade trajectory tracking control scheme is designed. An adaptive neural network-based terminal sliding control law is proposed for producing the generalized force/moment in real-time, the asymptotic stability of this adaptive control system is proven by Lyapunov theory, and a quasi-newton distribution law is designed to determine the optimum tire forces that guarantee the actual generalized forces/moment are close to the desired values. Finally, simulation results demonstrate the effectiveness of the proposed control scheme.