Research on Active Suspension System Based on BP and RBF Network Algorithm

Abstract

A six degrees of freedom half body vehicle suspension system is presented in the paper .The Back Propagation neural network algorithm and the Radial-Basis Function network algorithm is adopted to control the suspension system. With the aid of software Matlab/Simulink , the simulation model is obtained. A great deal of simulation work is done. Simulation results demonstrate that both the designed radius basis function neural network and the back propagation neural network work well for the proposed vehicle suspension model in the paper .

Optimization of the New Index Reaching Law of the Active Suspension Sliding Mode Controller Based on the Cuckoo Search Algorithm

“…An actuator fault compensation scheme is proposed, which can achieve arbitrarily small tracking errors in the presence of nonlinear actuators with random faults [5]. And there are more and more control strategies for active suspension, such as H∞ control [4,[8][9][10][11], optimal control [12][13][14][15], neural network control [16][17][18][19], LQG control [20,21], predictive control [22], and sliding mode control [14,23].…”