A novel adaptive controller featuring inversely fuzzified values with application to vibration control of magneto-rheological seat suspension system

This work presents a novel adaptive fuzzy controller based on the inversely fuzzified values associated with the H infinity technique and it is applied to vibration control of a vehicle seat suspension system installed with a magneto-rheological damper. In this work, the fuzzified values are embedded into the Riccati-like equation to achieve enhanced robustness of control function and the sliding surface function of sliding mode control is used for designing the main control function. The synthetic function of controller includes a model of online interval type 2 fuzzy neural network system and a fast calculation for clustering. The combination between the fuzzy model system and the proposed control algorithm can shorten the calculation time and improve control responses of the system in the presence of disturbances and uncertainties. After formulating the controller, the effectiveness is validated through both simulation and experiment. In computer simulation, three different road profiles of random bump, regular bump, and random step wave are adopted to emulate severe external disturbances. It is shown from the simulation that both displacement and acceleration of the driver are remarkably well controlled under three different road conditions. This advantage is also validated in the experimental work. It is demonstrated that vibration control performances under random step wave road are much better than the comparative adaptive fuzzy controller. It is identified from the results that the use of the inversely fuzzified values plays a major role to enhance the robustness and hence better control responses in the presence of the disturbances.