An active suspension system enables a road vehicle to run with increased stability, providing better safety and comfort, by maintaining the wheels in contact with the road surface. Therefore, there is currently demand for designing a robust controller that gives a satisfactory performance in the presence of the different road profiles. This investigation focuses on the design of a discrete-time non-switching networked sliding mode control for the active suspension system, using an event-triggered approach and actuation network delay compensation scheme. A novel event-triggered proportional-integral (PI) sliding variable is built with actuation network delay compensation in the presence of system uncertainties. The discrete-time non-switching networked sliding mode control law is designed using a novel sliding variable, and the control actions at the controller side are updated based on the event generation. The event generator block in the feedback channel increases the communication rate and reduces network congestion by restricting the transmission of unnecessary data packets. The random actuation network delay in the forward and feedback channel is modeled using the Poisson distribution and compensated using the Thiran’s approximation technique. The stability of the closed-loop system is derived using the proposed control law that ensures the finite-time convergence of the system state variables within the specified sliding band. Furthermore, the proposed controller is validated on an active suspension system in the presence of network abnormalities and system uncertainties. Numerical results inferred that the proposed control strategy with event-triggered approach and actuation network delay compensation scheme outperforms as considered the Gao’s reaching law, conventional non-switching reaching law, or the control strategy without actuation network delay compensation.


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    Title :

    Event-Triggered Non-Switching Networked Sliding Mode Control for Active Suspension System With Random Actuation Network Delay


    Contributors:


    Publication date :

    2022-07-01


    Size :

    2700169 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English