Fault-tolerant control design for unreliable networked control systems via constrained model predictive control

This work deals with the problem of passive fault-tolerant control (FTC) for discrete-time networked control systems (NCSs). Network imperfections such as random time delay and packet dropout are modeled as a Markov chain that results in a Markovian jump linear system (MJLS). Some of the elements in the transition probability matrix (TPM) are supposed to be unknown so as to address network complexities. In addition, a comprehensive and practical fault model that considers the stochastic nature of networks is employed. By utilizing this fault model, the closed-loop NCS model is obtained by means of state augmentation technique. Then, a constrained model predictive control (MPC) is proposed to develop a fault-tolerant control strategy in which all these issues are considered as well as input constraint. Sufficient conditions to design the proposed reliable controller are derived in terms of linear matrix inequalities (LMIs). Finally, two examples are utilized to demonstrate the validity of the proposed FTC. The simulation results show that the proposed strategy works well, and results in more effective responses compared to state-of-the-arts studies.