Active Suspension Control of Full-Car Systems Without Function Approximation

Abstract

This article proposes a new control approach for full-car active suspension systems with unknown nonlinearities. The main advantage of this approach is that the uncertainties and nonlinearities in the system can be handled without using any function approximator (e.g., neural networks and fuzzy logic systems), and the associated online adaptation. Hence, the heavy computational costs and sluggish learning phase to achieve convergence can be remedied. To maintain the transient and steady-state suspension responses, a coordinate suspension error transformation with prescribed performance functions is adopted. Then an approximation-free control is developed to achieve stabilization of the transformed system so as to retain a predefined suspension response. Extreme Value Theorem is used together with the Lyapunov theorem to prove the stability and convergence of the closed-loop control system. To validate the proposed method and show its practical applicability, a dynamic simulator is built by using a commercial vehicle software, Carsim, where an E-SUV type vehicle is configured to describe realistic vehicle dynamics. Simulation results reveal that the proposed control can achieve better suspension performance and require less model information compared with some existing approaches.