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<p>This study proposes a robust control strategy for semi-active air suspension systems (SASS) based on entropy theory. The multi-objective optimization of a system can be described as a long-term problem using entropy values by innovatively introducing entropy theory. The state marginal probability of the SASS is incorporated into the reward function as the entropy value. This incorporation incentivizes the agent to focus on reducing the entropy value of the system state over a period of time during the exploration process, thereby reducing the degree of coupling between system states. This study also proposes an optimization strategy that introduces a state observer based on a variational auto-encoder. The observer can extract environmental features from historical states and expand the dimension of the state, thereby enhancing the generalization performance of the system under different road excitations. Bench test results show that the algorithm improves ride comfort while ensuring robustness. The root mean square (RMS) of body vertical acceleration decreased by 13.01%, while the RMS of dynamic tyre displacement only increased by 2.36%.</p>

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Robust control strategy for semi-active air suspension systems based on reinforcement learning with entropy theory

  • 期刊论文

  • 发表日期：

    2026年4月28日

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  • Da Wang,
  • Guoqing Zhang,
  • Chunyang Qi,
  • Chuanxue Song,
  • Feng Xiao,
  • Liqiang Jin

摘要

This study proposes a robust control strategy for semi-active air suspension systems (SASS) based on entropy theory. The multi-objective optimization of a system can be described as a long-term problem using entropy values by innovatively introducing entropy theory. The state marginal probability of the SASS is incorporated into the reward function as the entropy value. This incorporation incentivizes the agent to focus on reducing the entropy value of the system state over a period of time during the exploration process, thereby reducing the degree of coupling between system states. This study also proposes an optimization strategy that introduces a state observer based on a variational auto-encoder. The observer can extract environmental features from historical states and expand the dimension of the state, thereby enhancing the generalization performance of the system under different road excitations. Bench test results show that the algorithm improves ride comfort while ensuring robustness. The root mean square (RMS) of body vertical acceleration decreased by 13.01%, while the RMS of dynamic tyre displacement only increased by 2.36%.