Survey of advanced suspension developments and related optimal control applications
Automatica (Journal of IFAC)
Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach
Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach
Improved H∞ control of discrete-time fuzzy systems: a cone complementarity linearization approach
Information Sciences: an International Journal
Relaxed LMI conditions for closed-loop fuzzy systems with tensor-product structure
Engineering Applications of Artificial Intelligence
Sliding mode neural network inference fuzzy logic control for active suspension systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Adaptive fuzzy controller with sliding surface for vehicle suspension control
IEEE Transactions on Fuzzy Systems
A Survey on Analysis and Design of Model-Based Fuzzy Control Systems
IEEE Transactions on Fuzzy Systems
A Descriptor System Approach to Fuzzy Control System Design via Fuzzy Lyapunov Functions
IEEE Transactions on Fuzzy Systems
Analog Integrated Circuits and Signal Processing
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This paper presents a Takagi-Sugeno (T-S) model-based fuzzy control design approach for electrohydraulic active vehicle suspensions considering nonlinear dynamics of the actuator, sprung mass variation, and constraints on the control input. The T-S fuzzy model is first applied to represent the nonlinear uncertain electrohydraulic suspension. Then, a fuzzy state feed-back controller is designed for the obtained T-S fuzzy model with optimized H∞ performance for ride comfort by using the parallel-distributed compensation (PDC) scheme. The sufficient conditions for the existence of such a controller are derived in terms of linear matrix inequalities (LMIs). Numerical simulations on a full-car suspension model are performed to validate the effectiveness of the proposed approach. The obtained results show that the designed controller can achieve good suspension performance despite the existence of nonlinear actuator dynamics, sprung mass variation, and control input constraints.