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The uncertainty influences may result in performance deterioration and instability to the steer by wire (SBW) system. Thus, it must make the control system keep robust stability from uncertainty, and have good robustness. In order to effectively restrain the interference and improve steering stability, this paper presents a μsynthesis robust controller based on SBW system, which considers the effect of model uncertainty and external disturbance on the system dynamics. Taking the ideal yaw rate tracking, interference suppression and excellent robustness as the control objectives, the μsynthesis robust controller is designed using linear fractional transformation theory to deal with the uncertainty. Then, it is testified through time domain and robustness simulation analysis. Simulation results show that the proposed controller can not only ensure robustness and robust stability of the system quite well, but improve handling stability of the vehicle effectively. The results of this study provide certain theoretical basis for the research and application of SBW system.

Abstract: In view of the existence of uncertainties such as system model and disturbance signal in the electric power steering (EPS) system, and the demand for system dynamic performance, the mixed H2/H∞ controller based on genetic algorithm is proposed. In order to obtain satisfactory steering feel, robust performance and steering stability, models of EPS system and a two-degree-of-freedom car are set up, then the state space model and the augmented matrixes are built. The H∞ method is introduced to minimize the effect of disturbances on the outputs, and the H2 method is applied to optimizing the system performance based on genetic algorithm. The simulation results show that the modified mixed H2/H∞ controller, which synthesizes the advantage of H2 control and H∞ control, has better robust performance and robust stability. The designed controller can attenuate the noises and disturbances caused by road random motivation, torque sensor measurement and model parameter uncertainty, enabling the driver to obtain satisfactory road feel.