Adaptive Finite-Time Prescribed Performance Control of Nonlinear Power Systems with Symmetry Full-State Constraints

Author:

Cheng Xiaohong12,Liu Shuang1ORCID,Wang Wenbo1,Zhang Cong1ORCID

Affiliation:

1. Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China

2. School of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China

Abstract

Power system control is commonly based on linear controllers, where linear controllers are designed using a linearized model of the system at a specific operating point. However, when the system’s operating point is changed, the dynamic characteristics of the system shift significantly. At this point, linear controllers often fail to meet system stability requirements. Furthermore, the range of state variables in the power system is limited by the objective conditions. In addition, the power system has high-precision constraints on the deviation of the load frequency and so on. Therefore, it is worth designing a finite-time controller that satisfies the prescribed performance and full-state constraints based on the nonlinear model of the power systems. Firstly, the prescribed performance is incorporated into the barrier Lyapunov function to ensure that the tracking error is within the desired accuracy. Then, the tracking strategy is designed based on backstepping and incorporating a first-order filter to ensure that the controlled system’s signals and tracking errors remain bounded in finite time. Finally, two simulations are given to illustrate the effectiveness of the proposed control scheme, confirming that all states keep within the predefined range.

Funder

Major Program of the National Natural Science Foundation of China

Central Government to Guide local scientific and Technological Development of Hebei Province

Innovation Group Program of Hebei province

Hebei Natural Science Foundation

Provincial Key Laboratory Performance Subsidy Project

Publisher

MDPI AG

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