Fault isolation and fault‐tolerant control for nonlinear stochastic distribution control systems with multiplicative faults
Fault isolation and fault‐tolerant control for nonlinear stochastic distribution control systems with multiplicative faults
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DOI：
10.1002/rnc.6682
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发表时间：
2023-03
影响因子：
3.9
通讯作者：
Yunfeng Kang;L. Yao;Jinglin Zhou;Hong Wang
中科院分区：
文献类型：
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作者：
Yunfeng Kang;L. Yao;Jinglin Zhou;Hong Wang
本文提出了一种采用 Takagi-Sugeno 模糊系统的非线性多重乘性故障随机分布控制系统的故障隔离、诊断和容错控制算法。为了获得详细的故障信息，引入故障检测算法来发现故障发生时间。然后建立故障隔离观测器来产生残差，并将误差系统分离为仅受扰动和乘性故障影响的子系统。此外，提出了一种故障估计方案来获取故障强度信息。当故障发生时，系统输出的概率密度函数将偏离期望的分布。因此需要模型预测控制容错控制方案来尽可能减少故障的影响，以确保故障后输出概率密度函数跟踪期望的概率密度函数。通过仿真算例证明了所设计算法的有效性，其中容错控制算法保证了在多个乘性故障同时发生的复杂情况下，系统输出概率密度函数仍然跟踪给定的输出概率密度函数。
In this paper, a fault isolation, diagnosis and fault tolerant control algorithm is proposed for nonlinear multiple multiplicative faults stochastic distribution control systems employing Takagi–Sugeno fuzzy system. To obtain the detailed fault information, a fault detection algorithm is introduced to discover the fault occurrence time. Then a fault isolation observer is built to produce the residual, and the error system is separated to subsystems affected only by disturbance and multiplicative faults. Moreover, a fault estimation scheme is presented to obtain the fault magnitude information. When faults occur, the system output probability density function will deviate from the desired distribution. So the model predictive control fault tolerant control scheme is needed to minimize the impact of faults as much as possible to make sure that the post fault output probability density function track the desired probability density function. The validity of the designed algorithm is demonstrated through a simulation example, where the fault tolerant control algorithm ensures that the system output probability density function still track the given output probability density function despite the complex case of multiple multiplicative faults occurring simultaneously.