4.7 Article

Robust implementable regulator design of linear systems with non-vanishing measurements

期刊

AUTOMATICA
卷 143, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2022.110418

关键词

Robust output regulation; Sampling; Washout filter; Internal model; Generalized hold device

资金

  1. Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China [ICT20069]

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This paper studies the design approaches for robust implementable output regulators in linear continuous-time systems with periodically sampled measurements. The design framework incorporates a generalized hold device and transforms the original problem into the design problem of an output feedback controller for a discrete-time system. The proposed approach ensures exponential convergence of the regulation errors to zero.
Robust implementable output regulator design approaches are studied for linear continuous-time systems with periodically sampled measurements, consisting of both the regulation errors and extra measurements that are generally non-vanishing in steady state. A digital regulator is first developed via the conventional emulation-based approach, rendering the regulation errors asymptotically bounded with a small sampling period. We then develop a hybrid design framework by incorporating a generalized hold device, which transforms the original problem into the problem of designing an output feedback controller fulfilling two conditions for a discrete-time system, given any (large) nonpathological sampling period. In our hybrid design framework, we show that such a controller can always be obtained by designing a discrete-time internal model, a discrete-time washout filter, and a discrete-time output feedback stabilizer. As a result, the regulation errors are shown to be globally exponentially convergent to zero. This design framework is further developed for a multi-rate digital regulator with a large sampling period of the measurements and a small control execution period.(c) 2022 Elsevier Ltd. All rights reserved.

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