Adaptive Pseudoinverse Fuzzy Control for Steer-by-Wire System Using Nonlinear-Quantization Neural Network

The high-performance tracking control of steering angle for steer-by-wire (SBW) system is foundation of vehicle driving safety. However, the variable steering load will reduce the tracking performance in the form of system disturbance, thereby causing steering hysteresis, which is uncertain, nonlinear, difficult to be modeled precisely and addressed effectively. This leads the accurate and stable steering angle tracking control to remaining challenging. In order to solve this issue, this article proposes an adaptive pseudoinverse fuzzy control method using nonlinear-quantization neural network for SBW system. First, to dynamically describe the uncertain steering hysteresis of SBW system online, a pseudoinverse compensator is constructed using fuzzy nonlinear-quantization cerebellar model articulation neural network, which avoids the complex dynamics modeling and inverse calculation. The fuzzy logic system is introduced to nonlinearly quantize the neural network input to improve the compensation accuracy of pseudoinverse compensator without increasing the computational burden. Then, to reduce the tracking error caused by the system disturbance from the variable steering load, an adaptive fuzzy controller with adjustable fuzzy mapping is proposed to enhance the antidisturbance control ability in the tracking process of steering angle. Finally, the proposed control method is verified by a vehicle equipped with SBW system. Experimental results show that, the proposed control method can effectively reduce steering hysteresis caused by variable steering load, and improve the tracking accuracy and stability of steering angle of the SBW system.