Adaptive optimal control for nonlinear active suspension systems

This paper proposes an adaptive optimal control method for active suspension systems subject to unknown dynamics and multiple performance indices. dynamics. Then, a cost function concerning the ride comfort, suspension stroke and control input is established, which aims at achieving a compromise between the performance indices. Furthermore, a single layer neural network (NN) is used to estimate the optimal, cost f by which the Hamiltonian function can be derived. To obtain the online solution, a novel adaptive law driven by the parameter estimation error is developed to update the unknown NN weights and calculate the optimal control action. Theoretical analysis is carried out to prove the stability and convergence of the closed-loop system. Finally, simulation results based on the vehicle simulation software, Carsim and Matlab/Simulink, are presented to demonstrate that the proposed adaptive optimal control method can make a trade -off between the performance indices and improve the overall suspension performance.