Fault diagnosis and classified fault-tolerant control of electromagnetic linear actuators using an improved sliding mode learning observer AITranslate
Abstract AITranslate
For gain faults in electromagnetic linear actuators (EMLAs) used in active suspension systems, this paper proposes a fault diagnosis and classified fault-tolerant control (CFTC) scheme based on an improved sliding mode learning observer (ISMLO). Considering external disturbances, actuator gain faults, and measurement noise, a comprehensive mathematical model of the faulty EMLA system is established. The proposed ISMLO integrates a proportional-derivative (PD) type learning observer with a sliding mode control law, and a forgetting factor is introduced to enhance adaptability and estimation performance. The observer enables rapid state tracking and accurate fault estimation under disturbance conditions. Based on the estimated fault information, a CFTC strategy is developed in which faults are classified into minor and major categories, and corresponding primary and secondary fault-tolerant controllers (PFTC and SFTC) are designed accordingly. To improve robustness, an H∞ performance index is incorporated into the design of the observer and controllers. The stability of the overall system is rigorously analyzed using Lyapunov theory and linear matrix inequalities (LMIs), and the effectiveness of the proposed method is validated through Hardware-in-the-Loop (HIL) experiments. Experimental results demonstrate that the ISMLO reduces displacement and fault estimation errors by up to 88% and 86% compared with the conventional learning observer (LO) and sliding mode observer (SMO), with strong fault-tolerant performance confirmed. After applying the CFTC strategy, the average steady-state error is reduced to 0.212 mm. These results confirm that the proposed approach provides fast state tracking, accurate fault estimation, and strong fault-tolerant performance for EMLA systems.
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Basic Information:
DOI：10.23919/CHAIN.2026.000020
Chinese Library Classification Number:
Citation Information:
For gain faults in electromagnetic linear actuators (EMLAs) used in active suspension systems, this paper proposes a fault diagnosis and classified fault-tolerant control (CFTC) scheme based on an improved sliding mode learning observer (ISMLO). Considering external disturbances, actuator gain faults, and measurement noise, a comprehensive mathematical model of the faulty EMLA system is established. The proposed ISMLO integrates a proportional-derivative (PD) type learning observer with a sliding mode control law, and a forgetting factor is introduced to enhance adaptability and estimation performance. The observer enables rapid state tracking and accurate fault estimation under disturbance conditions. Based on the estimated fault information, a CFTC strategy is developed in which faults are classified into minor and major categories, and corresponding primary and secondary fault-tolerant controllers (PFTC and SFTC) are designed accordingly. To improve robustness, an H∞ performance index is incorporated into the design of the observer and controllers. The stability of the overall system is rigorously analyzed using Lyapunov theory and linear matrix inequalities (LMIs), and the effectiveness of the proposed method is validated through Hardware-in-the-Loop (HIL) experiments. Experimental results demonstrate that the ISMLO reduces displacement and fault estimation errors by up to 88% and 86% compared with the conventional learning observer (LO) and sliding mode observer (SMO), with strong fault-tolerant performance confirmed. After applying the CFTC strategy, the average steady-state error is reduced to 0.212 mm. These results confirm that the proposed approach provides fast state tracking, accurate fault estimation, and strong fault-tolerant performance for EMLA systems.
quote
| GB/T 7714-2015 | [1] Zhihao Hao, Jiayu Lu, Bo Li, et al. Fault diagnosis and classified fault-tolerant control of electromagnetic linear actuators using an improved sliding mode learning observer[J]. Chain, 2026, 3(3): 415-433. DOI:10.23919/CHAIN.2026.000020. |
| MLA | [1] Zhihao Hao, et al., "Fault diagnosis and classified fault-tolerant control of electromagnetic linear actuators using an improved sliding mode learning observer." Chain, vol. 3, no. 3, 2026, pp. 415-433, https://doi.org/10.23919/CHAIN.2026.000020. |
| APA | [1] Zhihao Hao, Jiayu Lu, Bo Li, Cao Tan, Huichao Zhang, Ting Shu, & Sung-Ki Lyu. (2026). Fault diagnosis and classified fault-tolerant control of electromagnetic linear actuators using an improved sliding mode learning observer. Chain, 3(3), 415-433. https://doi.org/10.23919/CHAIN.2026.000020 |
| IEEE | [1] Zhihao Hao, Jiayu Lu, Bo Li, Cao Tan, Huichao Zhang, Ting Shu, and Sung-Ki Lyu, "Fault diagnosis and classified fault-tolerant control of electromagnetic linear actuators using an improved sliding mode learning observer," Chain, vol. 3, no. 3, pp. 415-433, 2026, doi: 10.23919/CHAIN.2026.000020. keywords: {active suspension;classified fault-tolerant control;electromagnetic linear actuator;fault diagnosis;improved sliding mode learning observer} |