Neuro-adaptive optimized control for full active suspension systems with full state constraints

In this paper, an adaptive neural network (NN) optimized control strategy is presented to improve the inherent tradeoff between ride comfort of passengers and the suspension travel for full vehicle active suspension system with state constraints. To the best of our knowledge, the automotive suspension system control using hydraulic actuators is a highly complex nonlinear control task, involving external disturbances and uncertainties. To address it, this paper develops the virtual and actual optimal controllers based on backstepping technique and the identifier-actor-critic structure. Meanwhile, the Barrier Lyapunov functions are developed to prevent the systems from the state constraints and the systems states are limited in the preselected compact sets. It is particularly worth mentioning that the proposed optimal control strategy ensures that all the closed-loop signals remain bounded, while the power of the control input as well as the amplitude of the vertical displacement has been minimized. The simulation results show how the full-car system can be controlled optimally and satisfactorily, and confirm the superiority of proposed method.