Fault-tolerant predictive control for Markov Linear Systems

Abstract

This work considers fault-tolerant control (FTC) for discrete-time Markov Jump Linear System (MJLS) subject to constraints on the state an control variables. The objective is to design a control law which depends on the jump variable, minimizing an average quadratic function. Improving over previous MJLS literature, input and state constraints are enforced. The initial condition of the system and the transition probability of the Markov chain are available to the controller at each instant of time. Concepts arising from the receding horizon framework and invariant set theory are incorporated in a constrained fault-tolerant predictive control approach.