Neural-network-based optimal fuzzy control design for half-car active suspension systems

Abstract

Developing advanced design and synthesis of self-learning optimal intelligent active suspension systems. Artificial neural-based fuzzy modeling is applied to set up the neural-based fuzzy model based on the training data from the nonlinear half-car suspension system dynamics. Furthermore, a robust optimal fuzzy controller is designed based on the proposed fuzzy model to improve ride quality and support appropriate movement in suspension systems. Moreover, the development of self-learning optimal intelligent active suspension can not only absorb disturbance and shock, to adapt the model, the sensor and the actuator error but also cope with the parameter uncertainty with minimum power consumption. The simulation results also indicate the feasibility and the applicability of the designed controller.