Deep Learning Based Intelligent Active Suspension Control for Heavy Trucks (DMPSO)

We will discuss why we are interested in developing a brand new active suspension control approach for trucks that is supported by Deep Learning in this article. The mechanical suspension can be classified as passive, semi-active, or active. The desired output of active suspension control is dependent on the following factors: ride comfort, suspension travels, and road handling. The look of a cushy and efficient active mechanical system for the vehicle was an exciting and difficult problem for control engineering. MATLAB Toolbox was used to build the model. One approach for modelling a system is to use the laws of physics to describe the system and then use experimental data or provided knowledge about the system’s parameters. The aim of this study is to create a robust controller using Deep Learning that will boost the efficiency of the Heavy Truck’s nonlinear active mechanical system. This proposed Deep Learning (DMPSO) based Particle Swarm Optimization technique (PSO) for an efficient non-linear active mechanical system. The DMPSO approach is proposed to extend the results of driving comfort in lighter damping and longer suspension strokes.