Development of a Hydraulic Spring System for Vehicles Based on Various Control Laws

Abstract

This article discusses the development of a hydraulic spring system for vehicles based on various control laws. Two approaches to the development of a control algorithm for nonlinear systems are presented. One approach uses the least squares method to calculate the controller parameters of a linearized dynamic system model. The other approach uses a multilayer perceptron-type neural network to model and estimate the actual parameters of the nonlinear system. The main idea is to demonstrate how the actual parameter estimation of a nonlinear neural model that is being trained is applied in a linearized model controller with real-time computation.