Ride comfort improvement using robust multi-input multi-output fuzzy logic dynamic compensator

This article aims to improve the ride comfort of a vehicle keeping a satisfactory without road-holding performance by a novel adaptive control method that is insensitive to unknown system dynamics, model parameter changes, external disturbances and without any loss in suspension working space. With this purpose in mind, a new fuzzy integrated model-based adaptive control law for vehicle suspension systems is proposed in this study to improve the ride comfort and to ensure the robustness of system towards unknown model parameters and external disturbances. First, a model-based adaptive control law, which has the robust characteristics is presented. Afterwards, a multi-input multi-output fuzzy logic controller is designed to determine the controller gains dynamically. The stability of controller is ensured by Lyapunov Theory to achieve uniform boundedness error convergence. A 4 degree-of-freedom half-car model with active suspension system is used in this study to assess the performance of the controller. The results are compared among passive, model-based adaptive control law-controlled and novel fuzzy model-based adaptive control law-controlled systems. It has been concluded that fuzzy model-based adaptive control law further attenuates linear and angular motions of the vehicle increasing the ride comfort. The robustness is also verified for vehicle components having different possible parameter values. It is noteworthy that suspension working length returns to its initial position. Thus, the vehicle ride comfort is improved with no suspension working space loss. Finally, the economic feasibility of controllers has been checked in terms of energy consumption.