随机多源干扰系统的复合DOBC和容错控制
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摘要
针对一类带有多源干扰和常值故障的随机系统,研究其抗干扰和故障诊断问题.多源干扰包括由外源系统生成的部分信息已知的干扰和白噪声干扰两类.第1类干扰包含状态和干扰耦合,不仅可以代表一类部分信息已知的干扰,还可以代表一类随机干扰,且耦合增加了系统的复杂性,导致必然等价原则的无效.为了解决这个问题,提出复合极点配置和线性矩阵不等式(LMI)方法相结合的策略.首先,设计随机干扰观测器来估计第1类干扰;其次,设计随机故障诊断观测器来估计系统故障.基于此,结合容错控制和随机控制,提出基于观测器的复合容错控制策略,在满足一定条件下,该策略可以保证复合系统满足依均方渐近有界.最后,通过仿真验证所提出策略的正确性与有效性.
The problem about anti-disturbance and fault-diagnosis is considered for a class of stochastic systems with multiple disturbances and fault. The multiple disturbances include the disturbance with partially-known information generated by exogenous systems and the white noise. The disturbance generated by exogenous systems can not only represent the disturbance with partially-known information, but also describe a stochastic disturbance. The coupling leads to the invalidity of certainty equivalence principle. To solve the difficulty, the composite pole placement and LMI methods are proposed. Firstly, a stochastic disturbance observer is constructed to estimate the disturbance with partially-known information. Then, a stochastic fault diagnosis observer is constructed to estimate the fault, based on which, a composite fault-tolerant control scheme is proposed by combining fault-tolerant control and stochastic control. It can be guaranteed that all the signals in the composite system are asymptotically bounded in mean square under certain conditions. Finally,a simulation example is given to illustrate the correctness and effectiveness of the proposed method.
The problem about anti-disturbance and fault-diagnosis is considered for a class of stochastic systems with multiple disturbances and fault. The multiple disturbances include the disturbance with partially-known information generated by exogenous systems and the white noise. The disturbance generated by exogenous systems can not only represent the disturbance with partially-known information, but also describe a stochastic disturbance. The coupling leads to the invalidity of certainty equivalence principle. To solve the difficulty, the composite pole placement and LMI methods are proposed. Firstly, a stochastic disturbance observer is constructed to estimate the disturbance with partially-known information. Then, a stochastic fault diagnosis observer is constructed to estimate the fault, based on which, a composite fault-tolerant control scheme is proposed by combining fault-tolerant control and stochastic control. It can be guaranteed that all the signals in the composite system are asymptotically bounded in mean square under certain conditions. Finally,a simulation example is given to illustrate the correctness and effectiveness of the proposed method.
引文
[1] Gao Z W, Ding S X. State and disturbance estimator for time-delay systems with application to fault estimation and signal compensation[J]. IEEE Trans on Signal Process, 2007, 55(12):5541-5551.
[2]张可,周东华,柴毅.复合故障诊断技术综述[J].控制理论与应用, 2015, 32(9):1142-1157.(Zhang K, Zhou D H, Chai Y. Summary of compound fault diagnosis[J]. Control Theory&Applications, 2015,32(9):1142-1157.)
[3] Guo L, Zhang Y M, Wang H, et al. Observer-based optimal fault detection and diagnosis using conditional probability distributions[J]. IEEE Trans on Signal Process, 2006, 54(10):3713-3719.
[4] Jiang B, Chowdhury F N. Fault estimation and accommodation for linear MIMO discrete time systems[J]. IEEE Trans on Control Systems Technology,2005, 13(3):493-499.
[5] Cheng Y P. The theory of matrices[M]. 2nd ed.Xi’an:Northwestern Polytechnical University Press,2000:333-335.
[6] Mao X R, Yuan C G. Stochastic differential equations with Markovian switching[M]. London:Imperial College Press, 2006:157-158.
[7] Chen W H. Disturbance observer based control for nonlinear systems[J]. IEEE/ASME Trans on Mechatronics, 2004, 9(4):706-710.
[8] Wei X J, Wu Z J, Karimi H R. Disturbance observer-based anti-disturbance control for a class of stochastic systems[J]. Automatica, 2016, 63(1):21-25.
[2]张可,周东华,柴毅.复合故障诊断技术综述[J].控制理论与应用, 2015, 32(9):1142-1157.(Zhang K, Zhou D H, Chai Y. Summary of compound fault diagnosis[J]. Control Theory&Applications, 2015,32(9):1142-1157.)
[3] Guo L, Zhang Y M, Wang H, et al. Observer-based optimal fault detection and diagnosis using conditional probability distributions[J]. IEEE Trans on Signal Process, 2006, 54(10):3713-3719.
[4] Jiang B, Chowdhury F N. Fault estimation and accommodation for linear MIMO discrete time systems[J]. IEEE Trans on Control Systems Technology,2005, 13(3):493-499.
[5] Cheng Y P. The theory of matrices[M]. 2nd ed.Xi’an:Northwestern Polytechnical University Press,2000:333-335.
[6] Mao X R, Yuan C G. Stochastic differential equations with Markovian switching[M]. London:Imperial College Press, 2006:157-158.
[7] Chen W H. Disturbance observer based control for nonlinear systems[J]. IEEE/ASME Trans on Mechatronics, 2004, 9(4):706-710.
[8] Wei X J, Wu Z J, Karimi H R. Disturbance observer-based anti-disturbance control for a class of stochastic systems[J]. Automatica, 2016, 63(1):21-25.