Reinforcement-Learning-Based Vibration Control for a Vehicle Semi-Active Suspension System via the PPO Approach
The vehicle semi-active suspension system plays an important role in improving the driving safety and ride comfort by adjusting the coefficients of the damping and spring. The main contribution of this paper is the proposal of a PPO-based vibration control strategy for a vehicle semi-active suspensi...
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| Format: | Article |
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MDPI AG
2022-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/6/3078 |
| _version_ | 1827650035209207808 |
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| author | Shi-Yuan Han Tong Liang |
| author_facet | Shi-Yuan Han Tong Liang |
| author_sort | Shi-Yuan Han |
| collection | DOAJ |
| description | The vehicle semi-active suspension system plays an important role in improving the driving safety and ride comfort by adjusting the coefficients of the damping and spring. The main contribution of this paper is the proposal of a PPO-based vibration control strategy for a vehicle semi-active suspension system, in which the designed reward function realizes the dynamic adjustment according to the road condition changes. More specifically, for the different suspension performances caused by different road conditions, the three performances of the suspension system, body acceleration, suspension deflection, and dynamic tire load, were taken as the state space of the PPO algorithm, and the reward value was set according to the numerical results of the passive suspension, so that the corresponding damping force was selected as the action space, and the weight matrix of the reward function was dynamically adjusted according to different road conditions, so that the agent could have a better improvement effect at different speeds and road conditions. In this paper, a quarter–car semi-active suspension model was analyzed and simulated, and numerical simulations were performed using stochastic road excitation for different classes of roads, vehicle models, and continuously changing road conditions. The simulation results showed that the body acceleration was reduced by 46.93% under the continuously changing road, which proved that the control strategy could effectively improve the performance of semi-active suspension by combining the dynamic changes of the road with the reward function. |
| first_indexed | 2024-03-09T20:08:51Z |
| format | Article |
| id | doaj.art-91da21f1b7434611b6c48f0a6bc0487d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:08:51Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-91da21f1b7434611b6c48f0a6bc0487d2023-11-24T00:23:22ZengMDPI AGApplied Sciences2076-34172022-03-01126307810.3390/app12063078Reinforcement-Learning-Based Vibration Control for a Vehicle Semi-Active Suspension System via the PPO ApproachShi-Yuan Han0Tong Liang1Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, ChinaShandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, ChinaThe vehicle semi-active suspension system plays an important role in improving the driving safety and ride comfort by adjusting the coefficients of the damping and spring. The main contribution of this paper is the proposal of a PPO-based vibration control strategy for a vehicle semi-active suspension system, in which the designed reward function realizes the dynamic adjustment according to the road condition changes. More specifically, for the different suspension performances caused by different road conditions, the three performances of the suspension system, body acceleration, suspension deflection, and dynamic tire load, were taken as the state space of the PPO algorithm, and the reward value was set according to the numerical results of the passive suspension, so that the corresponding damping force was selected as the action space, and the weight matrix of the reward function was dynamically adjusted according to different road conditions, so that the agent could have a better improvement effect at different speeds and road conditions. In this paper, a quarter–car semi-active suspension model was analyzed and simulated, and numerical simulations were performed using stochastic road excitation for different classes of roads, vehicle models, and continuously changing road conditions. The simulation results showed that the body acceleration was reduced by 46.93% under the continuously changing road, which proved that the control strategy could effectively improve the performance of semi-active suspension by combining the dynamic changes of the road with the reward function.https://www.mdpi.com/2076-3417/12/6/3078proximal policy optimizationvehicle semi-active suspensionroad changereward function |
| spellingShingle | Shi-Yuan Han Tong Liang Reinforcement-Learning-Based Vibration Control for a Vehicle Semi-Active Suspension System via the PPO Approach Applied Sciences proximal policy optimization vehicle semi-active suspension road change reward function |
| title | Reinforcement-Learning-Based Vibration Control for a Vehicle Semi-Active Suspension System via the PPO Approach |
| title_full | Reinforcement-Learning-Based Vibration Control for a Vehicle Semi-Active Suspension System via the PPO Approach |
| title_fullStr | Reinforcement-Learning-Based Vibration Control for a Vehicle Semi-Active Suspension System via the PPO Approach |
| title_full_unstemmed | Reinforcement-Learning-Based Vibration Control for a Vehicle Semi-Active Suspension System via the PPO Approach |
| title_short | Reinforcement-Learning-Based Vibration Control for a Vehicle Semi-Active Suspension System via the PPO Approach |
| title_sort | reinforcement learning based vibration control for a vehicle semi active suspension system via the ppo approach |
| topic | proximal policy optimization vehicle semi-active suspension road change reward function |
| url | https://www.mdpi.com/2076-3417/12/6/3078 |
| work_keys_str_mv | AT shiyuanhan reinforcementlearningbasedvibrationcontrolforavehiclesemiactivesuspensionsystemviatheppoapproach AT tongliang reinforcementlearningbasedvibrationcontrolforavehiclesemiactivesuspensionsystemviatheppoapproach |