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The magnetorheological fluid (MRF) damper is widely utilized in semi-active suspension (SAS) systems to improve both ride comfort and handling performance. Nevertheless, the MRF damper can be prone to faults due to fluctuating road conditions and the complexity of its working environment. To tackle this challenge, this report introduces an optimized fault-tolerant control (FTC) approach for the MRF-SAS system, leveraging the sum-of-squares (SOS) programming technique to enhance system reliability. Initially, a mathematical model of the SAS system incorporating the MRF damper is developed. Subsequently, the SOS approximation method is applied, which represents the nonlinearities within the system as polynomial squares. Following this, a relaxed L2-gain optimization problem is formulated, incorporating H \(\infty\) control objectives to determine the most effective FTC strategy for the MRF-SAS system. The linear matrix inequality (LMI) method is then used to solve this relaxed optimization problem. Experimental findings indicate that, in the event of a damper fault, the proposed FTC method consistently delivers superior performance compared to systems lacking fault-tolerant control.

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H∞ Optimal Control for Magnetorheological Suspension Systems via Sum-of-Squares Programming

  • 会议论文

  • 发表日期：

    2026年

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  • Chenhui Zheng,
  • Jiaming Li,
  • Zhiwei Wu,
  • Zhi-Xin Yang

摘要

The magnetorheological fluid (MRF) damper is widely utilized in semi-active suspension (SAS) systems to improve both ride comfort and handling performance. Nevertheless, the MRF damper can be prone to faults due to fluctuating road conditions and the complexity of its working environment. To tackle this challenge, this report introduces an optimized fault-tolerant control (FTC) approach for the MRF-SAS system, leveraging the sum-of-squares (SOS) programming technique to enhance system reliability. Initially, a mathematical model of the SAS system incorporating the MRF damper is developed. Subsequently, the SOS approximation method is applied, which represents the nonlinearities within the system as polynomial squares. Following this, a relaxed L2-gain optimization problem is formulated, incorporating H \(\infty\) control objectives to determine the most effective FTC strategy for the MRF-SAS system. The linear matrix inequality (LMI) method is then used to solve this relaxed optimization problem. Experimental findings indicate that, in the event of a damper fault, the proposed FTC method consistently delivers superior performance compared to systems lacking fault-tolerant control.