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.

Active suspension provides better vehicle control and safety on the road with optimal driving comfort compared to passive suspension. Achieving this requires a good control system that can adapt to any environment. This article uses a deep reinforcement learning method to develop an optimal neural network that meets the comfort requirements according to ISO 2631-5 standards. The algorithm trains the agent without any prior knowledge of the environment. Various simulations were performed, and the results were validated with the literature and the standard until the appropriate reward function was found. Simple and consistent road profiles were used while maintaining constant system parameters during training. The results show that suspension based on deep reinforcement learning reduces vehicle body acceleration and improves ride comfort without sacrificing suspension deflection and dynamic tire loading. The controller expects the RMS value of the acceleration to be 0.228 with a minimum overrun of the suspended mass.

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