Control of an active suspension system based on long short-term memory (LSTM) learning
Issam Dridi, Anis Hamza, Noureddine Ben Yahia
Abstract
As part of our study, which is a continuation of the research carried out by Dr. Anis HAMZA, Intelligent Neural Network Control for Active Heavy Truck Suspension Chapter of the book Advances in Mechanics and Mechanics. This working model is an intelligent, active suspension system with RNN (Recurrent Neural Network), which seeks the stability of heavy vehicles under all external or internal conditions (weight, mass, road deformation, acceleration, braking, etc.), to find a compromise between all these constraints. Standard control methods, such as (PID and LQR…) do not solve our multi-parameter problem. Our contribution is to exploit any servo system (PID, LQR, FUZZY, …). To train our LSTM (Long short-term memory) neural network with a Root Mean Square (RMS) rating value. Our method has proven effective by the results obtained. The view is an adaptation to classification, processing, and making predictions based on time series data, which is well in line with our suspension system that each moment depends on the previous state. The results have been confirmed by the ISO 26315 standard concerning the exposure of individuals to vibration and mechanical shock.