Journal
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
Volume 67, Issue 5, Pages 2466-2472Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2021.3077353
Keywords
Differential linear matrix inequality (DLMI); model predictive control (MPC); sampled-data control
Funding
- National Council for Scientific and Technological Development (CNPq - Brazil) [302012/2019-9]
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This article focuses on the design of model predictive control (MPC) in sampled-data control systems with full-state measurements. The results show that recent research in this area can be successfully applied to cope with sampled-data MPC. By minimizing a guaranteed H-2 performance index with infinity horizon, the feedback control preserves asymptotic stability and feasibility. The design conditions are expressed through differential linear matrix inequalities and no discrete-time modeling approximation is used for continuous-time systems. Comparisons with classical methods from previous literature are presented and discussed.
This article focuses on model predictive control (MPC) design in the context of sampled-data control systems with full-state measurements. It is shown that recent results on this area can be successfully generalized to cope with sampled-data MPC. The open-loop plant is subjected to polytopic parameter uncertainty and at sampling times, a controlled output variable satisfies a set of convex constraints. A guaranteed H-2 performance index with infinity horizon is minimized such that the feedback control preserves asymptotic stability and feasibility. The design conditions are expressed through differential linear matrix inequalities. Continuous-time systems are treated with no kind of discrete-time modeling approximation. Comparisons with classical methods from the literature dealing with continuous-time systems are presented and discussed. Examples are included for illustration.
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