Self-organizing fuzzy control of active suspension systems
International Journal of Systems Science
Expert Systems with Applications: An International Journal
Fuzzy control for nonlinear uncertain electrohydraulic active suspensions with input constraint
IEEE Transactions on Fuzzy Systems
Bounded-time system identification under neuro-sliding training
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Expert Systems with Applications: An International Journal
Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system
International Journal of Applied Mathematics and Computer Science - Semantic Knowledge Engineering
Neural network control of seat vibrations of a non-linear full vehicle model using PMSM
Mathematical and Computer Modelling: An International Journal
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In the automotive industry, suspension systems are designed to provide desirable vehicle ride and handling properties. This paper presents the development of a robust intelligent nonlinear controller for active suspension systems based on a comprehensive and realistic nonlinear model. The inherent complex nonlinear system model's structure, and the presence of parameter uncertainties, have increased the difficulties of applying conventional linear and nonlinear control techniques. Recently, the combination of sliding mode, fuzzy logic, and neural network methodologies has emerged as a promising technique for dealing with complex uncertain systems. In this paper, a sliding mode neural network inference fuzzy logic controller is designed for automotive suspension systems in order to enhance the ride and comfort. Extensive simulations are performed on a quarter-car model, and the results show that the proposed controller outperforms existing conventional controllers with regard to body acceleration, suspension deflection, and tire deflection