Control of Vehicle Suspension Using Neural Network
Abstract
Holographic neural network, which is a new algorithm of neural network, is applied to the control of a vehicle suspension. For the simplified suspension model, using the feedback gain obtained from the optimal control theory, the equation of motion is solved by Runge-Kutta method. The displacement, velocity, and control force obtained are adopted as training data for neural network. In the simulation the road displacement is assumed to be sinusoidal and its frequency is changed from 1.0Hz to 4.0Hz by 0.1Hz intervals. As the trained network is tested, the results for the road frequency not contained in the training data agree well with the results obtained from the optimal control theory.
Fields of Study & Subjects
Magnetic Bearings and Levitation Dynamics
Neural Networks and Applications
Electric Power Systems and Control
Publication details
- Journal / source
- The Dynamics of Vehicles on Roads and on Tracks Supplement to Vehicle System Dynamics
- Publisher
- CRC Press
- Volume
- 41
- Pages
- 153–161
- Publication date
- 30 January 2026
- Language
- EN
- Document type
- Book chapter