期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 30, 期 6, 页码 1875-1888出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2021.3069329
关键词
Cone complementarity linearization; fuzzy systems; linear matrix inequality (LMI); proportional-integral-derivative (PID) control; round-robin (RR) protocol
资金
- National Natural Science Foundation of China [61703245, 61873058, 61873148, 61933007]
- China Postdoctoral Science Foundation [2018T110702]
- Postdoctoral Special Innovation Foundation of Shandong Province of China [201701015]
- Alexander Von Humboldt Foundation of Germany
- Natural Science Foundation of Heilongjiang Province of China [ZD2019F001]
This article focuses on the H-infinity PID control problem for a class of discrete-time Takagi-Sugeno fuzzy systems, utilizing RR communication protocol and fuzzy PID controller to ensure system stability and performance index.
This article is concerned with the H-infinity proportional- integral-derivative (PID) control problem for class of discrete-time Takagi-Sugeno fuzzy systems subject to infinite-distributed time delays and round-robin (RR) protocol scheduling effects. The information exchange between the sensors and the controller is conducted through a shared communication network. For the purpose of alleviating possible data collision, the well-known RR communication protocol is deployed to schedule the data transmissions. To stabilize the target system with guaranteed H-infinity performance index, a novel yet easy-to-implement fuzzy PID controller is developed whose integral term is calculated based on the past measurements defined in a limited time window with hope to improve computational efficiency and reduce accumulation error. Based on the Lyapunov stability theory and the convex optimization technique, sufficient conditions are derived to ensure the exponential stability as well as the H-infinity disturbance attenuation/rejection capacity of the underlying system. Furthermore, by utilizing the cone complementarity linearization algorithm, the nonconvex controller design problem is transformed into an iterative optimization one that facilitates the controller implementation. Finally, simulation examples are given to show the effectiveness and correctness of the developed control method.
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