Event-triggered controller design for active suspension systems: An adaptive backstepping method with error-dependent gain

Abstract

This paper studies an adaptive control problem for active suspension systems (ASSs) with parameter uncertainties based on the event-triggered mechanism (ETM). The main focus of this work is to improve the transient response of the body and save the communication resources of the in-vehicle network. The controller area network (CAN) is the most widely utilized in-vehicle communication network in automotive systems, so it is a vital issue that how to improve the utilization of the CAN resources since the CAN has a low bandwidth as a shared media. To this end, we present two error-dependent fixed and relative threshold ETMs to achieve satisfactory suspension performance while significantly reducing the communication burden on the CAN. Then, in the recursive design process, the backstepping strategy with error-dependent gain is employed to improve the transient response of the systems. Meanwhile, the radial basis function neural networks are adopted to identify the unknown nonlinearities in ASSs. Finally, the developed adaptive control algorithm ensures that all the signals in the closed-loop suspension systems are bounded in presence of uncertainties. Simulation results based on the professional vehicle simulation software Carsim verify the effectiveness of the presented scheme.