Fault detection design of networked control systems

In this study, the fault detection design of networked control systems (NCSs) considering time varying transmission delays, packet dropouts and quantisation errors is proposed. The design of the residual generator is formulated in the H∞ framework, where the transmission delays are described as polytopic uncertainties, quantisation errors are modelled as norm bounded uncertainties and packet dropouts are described as a binary Bernoulli process. The dynamics of residual generator is shown to be governed by a Markov jumping linear system with uncertainties, and then the residual generator is designed to be sensitive to system faults and robust against network-induced effects by applying a reference model strategy. A new residual evaluation scheme for NCSs is also proposed, where the absolute value of each residual signal is selected as the evaluation function and the threshold is computed by considering the mean value and variance of residual signals. In this way, the upper bound of the false alarm rate is ensured.

[1]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[2]  Mario A. Rotea,et al.  The generalized H2 control problem , 1993, Autom..

[3]  Lihua Xie,et al.  Output feedback H∞ control of systems with parameter uncertainty , 1996 .

[4]  P. Frank,et al.  Survey of robust residual generation and evaluation methods in observer-based fault detection systems , 1997 .

[5]  C. Scherer,et al.  Multiobjective output-feedback control via LMI optimization , 1997, IEEE Trans. Autom. Control..

[6]  Paul M. Frank,et al.  Issues of Fault Diagnosis for Dynamic Systems , 2010, Springer London.

[7]  Nicola Elia,et al.  Stabilization of linear systems with limited information , 2001, IEEE Trans. Autom. Control..

[8]  Wei Zhang,et al.  Stability of networked control systems , 2001 .

[9]  Raja Sengupta,et al.  A bounded real lemma for jump systems , 2003, IEEE Trans. Autom. Control..

[10]  H. Ye,et al.  Fault detection for Markovian jump systems , 2005 .

[11]  Dong Yue,et al.  Network-based robust H ∞ control of systemswith uncertainty , 2005 .

[12]  Lihua Xie,et al.  The sector bound approach to quantized feedback control , 2005, IEEE Transactions on Automatic Control.

[13]  Michel Kinnaert,et al.  Diagnosis and Fault-Tolerant Control , 2004, IEEE Transactions on Automatic Control.

[14]  Zehui Mao,et al.  H/sub /spl infin// fault detection filter design for networked control systems modelled by discrete Markovian jump systems , 2007 .

[15]  Antonio Barreiro,et al.  Analysis of networked control systems with drops and variable delays , 2007, Autom..

[16]  Huijun Gao,et al.  ${\cal H}_{\infty}$ Estimation for Uncertain Systems With Limited Communication Capacity , 2007, IEEE Transactions on Automatic Control.

[17]  James Lam,et al.  Stabilization of linear systems over networks with bounded packet loss , 2007, Autom..

[18]  Peng Shi,et al.  Sampled-data control of networked linear control systems , 2007, Autom..

[19]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .

[20]  Ping Zhang,et al.  On fault detection in linear discrete-time, periodic, and sampled-data systems , 2008 .

[21]  Long Wang,et al.  Robust fault detection with missing measurements , 2008, Int. J. Control.

[22]  Yongqiang Wang,et al.  A New Fault Detection Scheme for Networked Control Systems Subject to Uncertain Time-Varying Delay , 2008, IEEE Transactions on Signal Processing.

[23]  Yongqiang Wang,et al.  Residual generation and evaluation of networked control systems subject to random packet dropout , 2009, Autom..

[24]  Youan Zhang,et al.  Sliding mode control for a class of nonlinear systems based on robust adaptive neural network estimation , 2010, Kybernetes.