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An event-triggered subspace predictive control method for partially unknown linear parameter-varying systems
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DOI：10.1002/rnc.7192.png)
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摘要
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This article develops a novel event-triggered subspace predictive control (ET-SPC) method for discrete-time linear parameter-varying (LPV) systems with partially unknown system parameters. An event-triggered law is developed to alleviate the heavy load of data transmission and computation in the conventional subspace predictive control methods for the LPV systems. The design of the event-triggered law relies on the input-to-state stability (ISS) theory and applies the robust stability condition based on linear matrix inequalities (LMIs), which guarantees the stability of the proposed method. Consequently, the input component can be directly transmitted to the system without receding horizon optimization when the event-triggered law evaluates that the system satisfies the stability condition. The simulation results illustrate its satisfactory performance, which indicates its potential application involving a considerable amount of data transmission and computation such as the network control systems (NCSs).
Keyword:
computation efficiency
event-triggered
linear parameter-varying (LPV) systems
networked control systems (NCSs)
subspace predictive control (SPC)
期刊
IF:
3.2
论文数:
7.0K
被引数:
1.4W
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