Adaptive Sliding Mode Control for Uncertain Active Suspension Systems With Prescribed Performance

Abstract

In this article, the adaptive sliding mode (ASM) control scheme of half-car active suspension systems with prescribed performance is studied. Because of the affected by model uncertainty, time-varying parameter, pavement roughness excitation, etc., the study of suspension systems can be regarded as the multivariable nonlinear control problem. First of all, the prescribed performance function (PPF) is applied to constrain the displacement and pitch angle of the suspension systems to ensure the transient and steady-state suspension responses. Second, an integral terminal sliding mode control method with strong robustness is put forward, which can make the system converge rapidly in a finite-time when it is far from the equilibrium point, solve the singularity problem in the control process, and reduce the chattering phenomenon in the traditional sliding mode control. Then, the neural networks (NNs) approximation characteristics are used to deal with unknown items in the design of the controller, and the Lyapunov stability theory is employed to analyze the stability of the closed-loop system. In the end, the comparative simulation results demonstrate the feasibility and effectiveness of the proposed control scheme.