Quantized fuzzy guaranteed cost control for electric vehicle with uncertain parameters

The issue of quantized fuzzy guaranteed cost control for the lateral dynamics of electric vehicles featuring norm-bounded uncertainty is examined. The change of vehicle condition during the driving process of an electric vehicle is modeled as a Takagi-Sugeno fuzzy model and uncertain parameters. A dynamic quantizer is incorporated into the input channel to process discrete vehicle network signals. Multiple parameters are introduced during the quantizer design to enhance flexibility and freedom in tuning quantizer parameters, while simultaneously facilitating the design of the quantized guaranteed cost controller. A key advantage of the proposed input quantization control scheme is that the guaranteed cost performance under quantization remains identical to that of the unquantized system. Finally, co-simulation using Matlab and Simulink validates the effectiveness of the control strategy under realistic driving conditions.