The actuator fault estimation (FE) problem is addressed in this study for the quarter-car active suspension system (ASS) with consideration of the sprung mass variation. Firstly, the ASS is modeled as a parameter-dependent system with actuator fault and external disturbance input. Then, a parameter-dependent FE observer is designed by using the radial basis function neural network (RBFNN) to approximate the actuator fault. In addition, the design conditions are turned into a linear matrix inequality (LMI) problem which can be easily solved with the aid of LMI toolbox. Finally, simulation and comparison results are given to show the accuracy and rapidity of the proposed FE method, as well as good adaptability against the sprung mass variation. Moreover, a simple FE-based active fault-tolerant control (AFTC) strategy is provided to further demonstrate the effectiveness and applicability of the proposed FE method.

本研究针对四轮车主动悬架系统（ASS）的执行器故障估计（FE）问题，考虑了簧载质量变化的问题。首先，将ASS建模为具有执行器故障和外部干扰输入的参数相关系统。然后，通过使用径向基函数神经网络（RBFNN）设计参数相关的有限元观测器，以近似执行器故障。此外，设计条件变成了线性矩阵不等式（LMI）问题，借助LMI工具箱可以轻松解决。最后，仿真和比较结果表明所提出的有限元方法的准确性和快速性，以及对簧载质量变化的良好适应性。而且，