Optimal Parametric Control of a Semi-Active Suspension System using Neural Networks

In recent years, there has been a growing interest in controlling both active and semi-active automotive suspension systems with a goal of improving ride comfort and vehicle handling. Many such resulting approaches have used linearized models Of the syspension's dynamics, allowing th use of linear (optimal) control theory. In actuality through, these systems and their optimal control are quite nonlinear. In this paper we propose a novel, yet highly practical alternative to such linearized design methods. This alternate optimal design method consists of a modified A* optimal-path, farward-search algorithm which is highly efficient, together with neural networks. The A* search, using a reasonably accurate system model and a given cost function, establishes te nonlinear optimal parametric control Of the suspension. The neural network, as will be shown, learns this nonlinear optimal control function, and in many ways outperforms the search from which it was taught.