Event-Triggered Robust MPC With Terminal Inequality Constraints: A Data-Driven Approach

An event-triggered robust model predictive control (MPC) design is proposed for unknown systems using initially measured input-output data. A terminal inequality constraint is developed for the MPC optimization problem without any prior identification, resulting in a larger feasible region and a low...

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Bibliographic Details
Published inIEEE transactions on automatic control Vol. 69; no. 7; pp. 4773 - 4780
Main Authors Deng, Li, Shu, Zhan, Chen, Tongwen
Format Journal Article
LanguageEnglish
Published New York IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Costs
Data-driven control
event-triggered robust MPC
Feasibility
input-to-state stability
Lower bounds
Mathematical models
Optimization
Predictive control
Predictive models
Robust control
Stability criteria
Stochastic processes
terminal inequality constraint
Trajectory
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Summary:An event-triggered robust model predictive control (MPC) design is proposed for unknown systems using initially measured input-output data. A terminal inequality constraint is developed for the MPC optimization problem without any prior identification, resulting in a larger feasible region and a lower bound for the prediction horizon when compared with a terminal equality constraint. An event-triggered scheme associated with a local controller is designed to trigger the solution of the data-driven MPC optimization problem when necessary, leading to the reduction of resource consumption. Under mild conditions, recursive feasibility and input-to-state stability are guaranteed theoretically. Simulation results are provided to show the effectiveness of the proposed approach.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2024.3357417