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Robust Self-Triggered MPC With Adaptive Prediction Horizon for Perturbed Nonlinear Systems
Beijing Inst Technol, Sch Automat, Key Lab Complex Syst Control & Decis, Beijing 100081, Peoples R China..
Beijing Inst Technol, Sch Automat, Key Lab Complex Syst Control & Decis, Beijing 100081, Peoples R China..
Beijing Inst Technol, Sch Automat, Key Lab Complex Syst Control & Decis, Beijing 100081, Peoples R China..
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0001-7309-8086
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2019 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 64, no 11, p. 4780-4787Article in journal (Refereed) Published
Abstract [en]

This paper proposes a robust self-triggered model predictive control (MPC) with an adaptive prediction horizon scheme for constrained nonlinear discrete-time systems subject to additive disturbances. At each triggering instant, the controller provides an optimal control sequence by solving an optimal control problem (OCP), and at the same time, determines the next triggering time and prediction horizon. By implementing the algorithm, the average sampling frequency is reduced and the prediction horizon is adaptively decreased as the system state approaches a terminal region. Meanwhile, an upper bound of performance loss is guaranteed when compared with a nominal periodic sampling MPC. Feasibility of the OCP and stability of the closed-loop system are established. Simulation results verify the effectiveness of the scheme.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 64, no 11, p. 4780-4787
Keywords [en]
Adaptive systems, Computational complexity, Predictive control, Optimal control, Cost function, Adaptive prediction horizon, model predictive control (MPC), nonlinear systems, self-triggered control
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-264858DOI: 10.1109/TAC.2019.2905223ISI: 000495647600038Scopus ID: 2-s2.0-85074537054OAI: oai:DiVA.org:kth-264858DiVA, id: diva2:1377259
Note

QC 20191211

Available from: 2019-12-11 Created: 2019-12-11 Last updated: 2019-12-11Bibliographically approved

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Dimarogonas, Dimos V.

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