Adaptive Network Trained Controller for Automotive Steering Systems 2017-01-9626

Electrical Power Assist Steering (EPAS) systems are currently eliminating the traditional hydraulic steering systems in vehicles. EPAS systems are nonlinear Multi Input Multi Output (MIMO) systems with multiple objectives, including fast response to the driver torque command, good driver feel, and attenuation of load disturbance and sensor noises. Optimal control method is employed to design EPAS system controllers for improved performance and robustness. But these controllers have showed acceptable performance for certain operating conditions and undesired steering feel for high steering gain. In this work, the neural networks are used which replace the optimal controllers of EPAS systems. A Euclidean adaptive resonance theory (EART) networks is trained according to the data collected from an H∞ optimal controller. The collected data represent the controller input and output signals. The said data are normalized and clustered into categories in the EART modules. The modules are interconnected by a map field. Once the training is accomplished, the EART controller replaces the optimal controller. The proposed controller provides improved robustness and comparatively high steering feel of EPAS system by reducing the amount required for intensive calculation. The rms value of the error signal with 75 number of clusters is lower than that of 15 clusters. The proposed technique is applicable to any arrangements of EPAS namely, rack, pinion and column EPAS.