Adaptive neural network control for active suspension systems with asymmetric time-varying output constraints

In this paper, the influence of asymmetric vertical motion of the car on suspension dynamic performance is studied. An adaptive neural network control scheme with asymmetric time-varying displacement and speed constraints in the vertical direction is proposed, which is aimed to insulate the car from the impact of the road. Firstly, the asymmetric time-varying Barrier Lyapunov functions (ATBLFs) are constructed to prevent the car from surpassing the constraints. Moreover, taking the variance of the car-body mass into account, the radical basis function (RBF) neural networks are adopted to approximate the part related to the uncertain car-body mass. The stability of the closed-loop system is then demonstrated. Finally, to verify whether the proposed control scheme is effective, numerical simulations of the quarter-car Active suspension systems (ASSs) are provided.