access icon free Constrained robust model predicted control of discrete-time Markov jump linear systems

This study is concerned with the problem of designing a robust model predictive control (MPC) for a class of uncertain discrete-time Markov jump linear systems. The main contribution is a set of linear matrix inequality (LMI) conditions obtained under new control policies for the unconstrained as well as the constrained MPC when uncertainties are present both in the system's matrices and in the transition probabilities of the modes. For the constrained MPC, hard constraints are considered over the input control and the states and results are extended to the so-called multi-step mode-dependent state-feedback control design. To illustrate the improvements obtained with the new set of LMI conditions, numerical simulations are carried out and compared with a recent reference in the literature.

Inspec keywords: discrete time systems; uncertain systems; robust control; state feedback; Markov processes; stochastic systems; linear matrix inequalities; control system synthesis; predictive control; linear systems

Other keywords: MPC; linear matrix inequality conditions; multistep mode-dependent state-feedback control design; model predictive control; LMI; uncertain system; robust model; discrete-time Markov jump linear systems; transition probability

Subjects: Markov processes; Algebra; Control system analysis and synthesis methods; Time-varying control systems; Stability in control theory; Linear control systems; Optimal control; Discrete control systems

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2018.5543
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