Sliding Mode Adaptive Active Suspension Control Combined with Particle Filter State Observation
When applying active suspension control,challenges such as parameter perturbation and the inability to directly acquire state variables in the algorithm may arise.Therefore,developing a robust control algorithm based on state observation is crucial.In this paper,a dynamic model of semi-vehicle roll suspension is established.A nonlinear filtering function coordinates the suspension deflection and the vertical acceleration of the vehicle body.It is then combined with a fuzzy sliding mode algorithm to achieve continuous sliding mode switching by utilizing fuzzy approximation,aiming to improve the chattering problem.On this basis,the stability of the control system under parameter perturbation is proven through the Lyapunov method,and a parameter adaptive law is designed.Additionally,for the state variables that cannot be directly measured in the algorithm,a particle filter state observer is designed to estimate their values in real-time.Finally,simulation analyses are conducted under typical working conditions,such as sinusoidal road excitation and random road excitation.The results demonstrate that the designed observer can provide real-time and accurate state information required by the control algorithm,and the fuzzy sliding mode controller with parameter adaptability exhibits good robustness and greatly improves vehicle posture and ride comfort.
active suspensionparticle filter state observerparameter adaptivefuzzy sliding mode
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