An Actor-Critic Reinforcement Learning Control Approach for Discrete-Time Linear System with Uncertainty

Chen, Hsin‐Chang; Lin, Yu‐Chen; Chang, Yu-Heng

doi:10.1109/cacs.2018.8606740

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…A more recent successful attempt was the study done by [16] where they applied a stochastic real-valued reinforcement learning control to a non-linear quarter model. A similar approach to the one considered in this research was conducted by [17]; the actor critic networks were trained by the policy gradient method, and the controller was tested to some extent with the same road profile considered in this study. They compared their work with the passive suspension system and showed 62% improvement.…”

Section: Introductionmentioning

confidence: 99%

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

Fares

Younes

2020

Applied Sciences

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In this paper, a controller learns to adaptively control an active suspension system using reinforcement learning without prior knowledge of the environment. The Temporal Difference (TD) advantage actor critic algorithm is used with the appropriate reward function. The actor produces the actions, and the critic criticizes the actions taken based on the new state of the system. During the training process, a simple and uniform road profile is used while maintaining constant system parameters. The controller is tested using two road profiles: the first one is similar to the one used during the training, while the other one is bumpy with an extended range. The performance of the controller is compared with the Linear Quadratic Regulator (LQR) and optimum Proportional-Integral-Derivative (PID), and the adaptiveness is tested by estimating some of the system’s parameters using the Recursive Least Squares method (RLS). The results show that the controller outperforms the LQR in terms of the lower overshoot and the PID in terms of reducing the acceleration.

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Section: Introductionmentioning

confidence: 99%

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

Fares

Younes

2020

Applied Sciences

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“…The main idea of reinforcement learning is to develop the suspension environment that interacts with the agent throughout the learning phase, the objective of which is to maximize the reward function to achieve the best neural network performance. The results obtained by the articles 7 are optimal compared to the Linear Quadratic Gaussian (LQG), and show an improvement of 62% compared to the passive suspension.…”

Section: Introductionmentioning

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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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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Physics-Guided Reinforcement Learning System for Realistic Vehicle Active Suspension Control

Nhu,

Le,

et al. 2023

2023 International Conference on Machine Learning and Applications (ICMLA)

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An Actor-Critic Reinforcement Learning Control Approach for Discrete-Time Linear System with Uncertainty

Cited by 3 publications

References 18 publications

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

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

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

Physics-Guided Reinforcement Learning System for Realistic Vehicle Active Suspension Control

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