Pengju Ning, Sergey N. Dashkovskiy, Changchun Hua, Kuo Li
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引用次数: 0
Abstract
SIAM Journal on Control and Optimization, Volume 62, Issue 4, Page 2254-2272, August 2024. Abstract. This paper investigates the prescribed-time output feedback stabilization problem for a class of nonlinear time-delay systems. First, a novel dual-gain function is put forward by exploiting the dynamic gain and the time-varying gain function to design the reduced-order observer for reconstructing unavailable states. Then, by utilizing the Lyapunov–Krasovskii functional and state variables of the reduced-order observer, a new prescribed-time controller is presented based on the nonscaling design framework. Since no state scaling is required in controller design process under this framework, our control strategy is simpler and can greatly reduce the computational burden. Further, compared with the previous prescribed-time stabilization results, our designed controller acts on the entire time domain, not just a limited time interval. Based on our proposed stability criterion, it is proved that the controller can render that all system state variables converge to the origin within the prescribed time. Finally, a numerical example is provided to illustrate the effectiveness of the proposed control strategy.
期刊介绍:
SIAM Journal on Control and Optimization (SICON) publishes original research articles on the mathematics and applications of control theory and certain parts of optimization theory. Papers considered for publication must be significant at both the mathematical level and the level of applications or potential applications. Papers containing mostly routine mathematics or those with no discernible connection to control and systems theory or optimization will not be considered for publication. From time to time, the journal will also publish authoritative surveys of important subject areas in control theory and optimization whose level of maturity permits a clear and unified exposition.
The broad areas mentioned above are intended to encompass a wide range of mathematical techniques and scientific, engineering, economic, and industrial applications. These include stochastic and deterministic methods in control, estimation, and identification of systems; modeling and realization of complex control systems; the numerical analysis and related computational methodology of control processes and allied issues; and the development of mathematical theories and techniques that give new insights into old problems or provide the basis for further progress in control theory and optimization. Within the field of optimization, the journal focuses on the parts that are relevant to dynamic and control systems. Contributions to numerical methodology are also welcome in accordance with these aims, especially as related to large-scale problems and decomposition as well as to fundamental questions of convergence and approximation.