Vehicle Optimal Torque Vectoring Using State-Derivative Feedback and Linear Matrix Inequality

Abstract

A controller assistant system is developed based on the closed-form solution of an offline optimization problem for a four-wheel-drive front-wheel-steerable vehicle. The objective of the controller is to adjust the actual vehicle attitude and motion according to the driver's manipulating commands. The controller takes feedback from acceleration signals, and the imposed conditions and limitations on the controller are studied through the concept of state-derivative feedback control systems. The controller gains are optimized using linear matrix inequality (LMI) and genetic algorithm (GA) techniques. Reference signals are calculated using a driver command interpreter module (DCIM) to accurately interpret the driver's intentions for vehicle motion and to allow the controller to generate proper control actions. It is shown that the controller effectively enhances the handling performance and stability of the vehicle under different road conditions and driving scenarios. Although controller performance is studied for a four-wheel-drive front-wheel-steerable vehicle, the algorithm can also be applied to other vehicle configurations with slight changes.