Displaying similar documents to “State feedback guaranteed cost repetitive control for uncertain discrete-time systems.”

Switching LPV control design with MDADT and its application to a morphing aircraft

Yong He, Chunjuan Li, Weiguo Zhang, Jingping Shi, Yongxi Lü (2016)

Kybernetika

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In flight control of a morphing aircraft, the design objective and the dynamics may be different in its various configurations. To accommodate different performance goals in different sweep wing configurations, a novel switching strategy, mode dependent average dwell time (MDADT), is adopted to investigate the flight control of a morphing aircraft in its morphing phase. The switching signal used in this note is more general than the average dwell time (ADT), in which each mode has its...

Special issue on decentralized control of large scale complex systems

Lubomír Bakule (2009)

Kybernetika

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This special issue provides information on current and future research directions in the emerging field of Decentralized Control of Large Scale Complex Systems. There is generally adopted view that a dynamic system is large scale complex whenever it is necessary to partition its analysis or synthesis problem to manageable subproblems. Its fundamental characteristics in modeling and control are high dimensionality, uncertainty, information structure constraints, and delays. Theory of...

A 2D system approach to the design of a robust modified repetitive-control system with a dynamic output-feedback controller

Lan Zhou, Jinhua She, Shaowu Zhou (2014)

International Journal of Applied Mathematics and Computer Science

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This paper is concerned with the problem of designing a robust modified repetitive-control system with a dynamic outputfeedback controller for a class of strictly proper plants. Employing the continuous lifting technique, a continuous-discrete two-dimensional (2D) model is built that accurately describes the features of repetitive control. The 2D control input contains the direct sum of the effects of control and learning, which allows us to adjust control and learning preferentially....