Journal
AUTOMATICA
Volume 121, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2020.109163
Keywords
Event-triggering; Sliding mode control; Reduced-order controller; Model-order reduction
Funding
- Alexander von Humboldt Foundation, Germany
- German Academic Exchange Service (DAAD)
- Department of Science and Technology (DST), Govt. of India through program DAAD-DST PPP (DAAD project) [57458791]
- DST [DST/INT/DAAD/P-9/2019]
- European Union Horizon 2020 research and innovation program under Marie Sklodowska-Curie grant [824046]
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In this paper, we propose the design of event-triggered sliding mode control (SMC) for a high-order system via a reduced-order model based design. It comprises a triggering mechanism with a reduced-order state vector and a controller based on a reduced-order model which stabilizes the (high-order) plant against perturbation. Apart from its low order synthesis of the controller, the proposed technique possesses many other advantages. The important one is the sampling pattern, which could be sparser by the use of a reduced-order vector in the event condition than with a full vector itself. This observation comes from the fact that the triggering instants explicitly depend on the state vector and its behavior over time, and thereby relaxing a few components of the state vector in the triggering mechanism may decrease its rate of the violation. Another significant outcome of this strategy is that the transmission of the reduced-order vector, particularly in a network-based implementation can outperform the full-order based design owing to the severe challenges that persistently occur in the data network. The event-triggered SMC is considered to achieve a robust performance for the closed-loop system. We show that our proposal guarantees the stability of the full-order plant with the reduced-order triggering mechanism, which does not admit a triggering sequence with Zeno behavior. The simulation results are given for a practical example to illustrate the performance and benefits of the proposed method. (C) 2020 Elsevier Ltd. All rights reserved.
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