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

Abstract

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.