In this article, an adaptive neural network control system is proposed for a quarter car electrohydraulic active suspension system coping with dynamic nonlinearities and uncertainties. The proposed control system is primarily designed to stabilize a sprung mass position of the quarter car electrohydraulic active suspension. Linear controllers such as the proportional–integral–differential controller have limited control performances. The limited control performances are caused by dynamic phenomena such as nonlinearity, parametric uncertainties, and stiff external disturbances. To overcome these dynamic phenomena, we propose a combined adaptive radial basis function neural network with a backstepping control system for a quarter car active suspension system. This setup can handle the unmatched model uncertainty of the system, while the adaptive neural network can take care of its unknown smoothing functions. In general, radial basis function neural network can represent a complicated function, and therefore, semi-strict-feedback dynamic systems are considered to simplify the adaptive neural network control design. Simulation results are indicated to illustrate adaptive neural network control effectiveness.

In this paper, a novel adaptive control system (NAC) is proposed for a restricted quarter-car electrohydraulic active suspension system. The main contribution of this NAC is its explicit tackling of the trade-off between passenger comfort/road holding and passenger comfort/suspension travel. Reducing suspension travel oscillations is another control target that was considered. Many researchers have developed control laws for constrained active suspension systems. However, most of the studies in the works of the latter have not directly examined the compromise between road holding, suspension travel, and passenger comfort. In this study, we proposed a novel adaptive control system to explicitly address the trade-off between passenger comfort and road holding, as well as the compromise between passenger comfort and suspension travel limits. The novelty of our control technique lies in its introduction of a modeling system for a dynamic landing tire system aimed at avoiding a dynamic tire liftoff. The proposed control consists of an adaptive neural networks’ backstepping control, coupled with a nonlinear control filter system aimed at tracking the output position of the nonlinear filter. The tracking control position is the difference between the sprung mass position and the output nonlinear filter signal. The results indicate that the novel adaptive control (NAC) can achieve the handling of car–road stability, ride comfort, and safe suspension travel compared to that of the other studies, demonstrating the controller's effectiveness.