Research on Finite Element Structure of Vehicle Suspension Control Arm Based on Neural Network Sensing Control

Abstract

To improve the adaptive control of the neural network under the influence of vehicle suspension control, the neural network control method is proposed. The specific content of the method analyzes the nonlinear properties of vehicle suspensions, proposes neural network-based adaptive control strategies, and develops neural network-based nonlinear algorithms and neural identifiers. Genetic algorithms perform predictive control of rear suspension through a compensation network. The experimental results show that the model structure is order $n=m=2$ , the AN1 network node is 4-6-1, the AN2 network node is 5-4-1, the AN3 network node is 6-4-1, and the learning correction rate is $\alpha =0.90$ . In the actual simulation calculation, the number of nodes in the hidden layer of the network is increased, and the minimum number of nodes is chosen to determine the structure of the network, since the control effect obtained is not fundamentally changed. The suspension, which is controlled by the neural network’s adaptive control, has a vibration-reducing effect and is more effective by increasing the control of the rear suspension. The neural network has been shown to be able to effectively control the vehicle’s control arm.