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Numerous research studies have been performed to help develop advanced control algorithms for semi-active seat suspension. This paper experimentally investigates a state observer-based Takagi-Sugeno (T-S) fuzzy controller for a semi-active seat suspension by equipping an electrorheological (ER) damper. A new ER damper prototype is designed, assembled, and tested. Then, a T-S fuzzy model is established to describe the ER seat suspension, which can facilitate the $\text{H}\infty $ controller design considering the multi-objective optimization. A state observer is established and integrated into the controller to estimate the state information for the T-S fuzzy model in real-time. Additionally, the experimental validation of the control algorithm is critical in the practical application. A seat suspension test rig is built to validate the effectiveness of the proposed controller. The presented control algorithm is evaluated by comparing the corresponding test results to those with a skyhook controller. The experimental results demonstrate that the proposed T-S fuzzy control method, compared to the traditional control method, can further improve the performance of an ER seat suspension system.

As the concept of variable stiffness and variable damping (VSVD) has increasingly drawn attention, suspensions applied with magnetorheological (MR) dampers to achieve varying stiffness and damping have been an attractive method to improve vehicle performance and driver comfort further. As highly nonlinearity of MR damper dynamics and coupled interconnections in the case of multi-output control, to build a direct control system for VSVD suspension based on multiple MR dampers is difficult. Applying Takagi-Sugeno (T-S) fuzzy model on the VSVD system enables the linear control theory to be directly utilized to build the multi-output controller for multi-MR dampers. In this paper, a T-S fuzzy model is established to describe an MR VSVD suspension model, and then an H∞ controller that considers the multi-input/multi-output (MIMO) coupled interconnections characteristic and multi-object optimization is designed. To estimate state information for the T-S fuzzy model in real-time, a state observer is designed and integrated in the controller. Then, the performance of the VSVD control algorithm was evaluated by numerical simulation. The results demonstrate that the T-S fuzzy model-based H∞ controller outperforms the independent control method for a VSVD suspension system with multi-MR dampers.