Online Reinforcement Learning-Based Control of an Active Suspension System Using the Actor Critic Approach

Fares, Ahmad; Younes, Ahmad Bani

doi:10.3390/app10228060

Cited by 16 publications

(9 citation statements)

References 16 publications

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“…Several articles have used the reinforcement learning method, for example, that of Fares and Younes, 54 who used the critical actor algorithm, but the results obtained with the PPO algorithm are more efficient.…”

Section: Resultsmentioning

confidence: 99%

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A new approach to controlling an active suspension system based on reinforcement learning

Dridi

Hamza

Yahia

2023

Advances in Mechanical Engineering

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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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Section: Resultsmentioning

confidence: 99%

“…The suspension system considers the vertical movement of the body x s and that of the wheel x us along the road presented by x r . We can present the dynamics of our suspension system by the following differential equations 54 :…”

Section: Numerical Modelingmentioning

confidence: 99%

A new approach to controlling an active suspension system based on reinforcement learning

Dridi

Hamza

Yahia

2023

Advances in Mechanical Engineering

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“…Our system is moving along the road that is presented by Z r . The dynamics of our suspension system are represented by the differential equations shown below 26 :…”

Section: Suspension Systemsmentioning

confidence: 99%

Control of an active suspension system based on long short-term memory (LSTM) learning

Dridi

Hamza

Yahia

2023

Advances in Mechanical Engineering

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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.

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“…However, offline training requires a large number of labeled samples, and it is hard to label optimal control results (Ming et al., 2020). Thus, neural networks were rarely used as controllers themselves (Fares & Bani Younes, 2020).…”

Section: Introductionmentioning

confidence: 99%

A hierarchical framework for improving ride comfort of autonomous vehicles via deep reinforcement learning with external knowledge

Chen

Zhao

et al. 2022

Computer aided Civil Eng

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Ride comfort plays an important role in determining the public acceptance of autonomous vehicles (AVs). Many factors, such as road profile, driving speed, and suspension system, influence the ride comfort of AVs. This study proposes a hierarchical framework for improving ride comfort by integrating speed planning and suspension control in a vehicle-to-everything environment. Based on safe, comfortable, and efficient speed planning via dynamic programming, a deep reinforcement learning-based suspension control is proposed to adapt to the changing pavement conditions. Specifically, a deep deterministic policy gradient with external knowledge (EK-DDPG) algorithm is designed for the efficient selfadaptation of suspension control strategies. The external knowledge of action selection and value estimation from other AVs are combined into the loss functions of the DDPG algorithm. In numerical experiments, real-world pavements detected in 11 districts of Shanghai, China, are applied to verify the proposed method. Experimental results demonstrate that the EK-DDPG-based suspension control improves ride comfort on untrained rough pavements by 27.95% and 3.32%, compared to a model predictive control (MPC) baseline and a DDPG baseline, respectively. Meanwhile, the EK-DDPG-based suspension control improves computational efficiency by 22.97%, compared to the MPC baseline, and performs at the same level as the DDPD baseline. This study provides a generalized and computationally efficient approach for improving the ride comfort of AVs.

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Online Reinforcement Learning-Based Control of an Active Suspension System Using the Actor Critic Approach

Cited by 16 publications

References 16 publications

A new approach to controlling an active suspension system based on reinforcement learning

A new approach to controlling an active suspension system based on reinforcement learning

Control of an active suspension system based on long short-term memory (LSTM) learning

A hierarchical framework for improving ride comfort of autonomous vehicles via deep reinforcement learning with external knowledge

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