This paper is concerned with the problem of stabilization of chaotic systems via nonfragile fuzzy proportional retarded sampled-data control. Compared with existing sampled-data control schemes, a more practical nonfragile fuzzy proportional retarded sampled-data controller is designed, which involves not only a signal transmission delay but also uncertainties. Based on the Wirtinger inequality, a new discontinuous Lyapunov-Krasovskii functional (LKF), namely, Wirtinger-inequality-based time-dependent discontinuous (WIBTDD) LKF, is the first time to be proposed for sampled-data systems. With the WIBTDD LKF approach and employing the developed estimation technique, a less conservative stabilization criterion is established. The desired fuzzy proportional retarded sampled-data controller can be obtained by solving a set of linear matrix inequalities. Finally, numerical examples are given to demonstrate the effectiveness and advantages of the proposed results.
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http://dx.doi.org/10.1109/TCYB.2018.2831782 | DOI Listing |
PLoS One
January 2025
Faculty of Mechanical Engineering, Thuyloi University, Hanoi, Vietnam.
Road surface roughness is the cause of vehicle vibration, which is considered a system disturbance. Previous studies on suspension system control often ignore the influence of disturbances while designing the controller, leading to system performance degradation under severe vibration conditions. In this work, we propose a control method to improve active suspension performance that reduces vehicle vibration by eliminating the influence of road disturbances.
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January 2025
Electrical Power and Machines Department, Egyptian Chinese University, Cairo, Egypt.
This research is dedicated to improving the control system of wind turbines (WT) to ensure optimal efficiency and rapid responsiveness. To achieve this, the fuzzy logic control (FLC) method is implemented to control the converter in the rotor side (RSC) of a doubly fed induction generator (DFIG) and its performance is compared with an optimized proportional integral (PI) controller. The study demonstrated an enhancement in the performance of the DFIG through the utilization of the proposed FLC, effectively overcoming limitations and deficiencies observed in the conventional controllers, this approach significantly improved the performance of the wind turbine.
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January 2025
The Key Laboratory for Agricultural Machinery Intelligent Control and Manufacturing of Fujian Education Institutions, Wuyi University, Nanping, 354300, Fujian, China.
This paper proposes an adaptive real-time tillage depth control system for electric rotary tillers, based on Linear Active Disturbance Rejection Control (LADRC), to improve tillage depth accuracy in tea garden intercropping with soybeans. The tillage depth control system comprises a body posture sensor, a control unit, and a hybrid stepper motor, integrating sensor data to drive the motor and achieve precise depth control. Real-time displacement sensor signals are compared with target values, enabling closed-loop control of the rotary tiller.
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January 2025
Institute of Sustainable Energy, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang, 43000, Selangor, Malaysia.
The microgrid (MG) faces significant security issues due to the two-way power and information flow. Integrating an Energy Management System (EMS) to balance energy supply and demand in Malaysian microgrids, this study designs a Fuzzy Logic Controller (FLC) that considers intermittent renewable sources and fluctuating demand patterns. FLC offers a flexible solution to energy scheduling effectively assessed by MATLAB/Simulink simulations.
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January 2025
Electrical Computer and Control Engineering Department, Faculty of Engineering, Suez Canal University, Ismailia, 41522, Egypt.
This study presents a novel optimization algorithm known as the Energy Valley Optimizer Approach (EVOA) designed to effectively develop six optimal adaptive fuzzy logic controllers (AFLCs) comprising 30 parameters for a grid-tied doubly fed induction generator (DFIG) utilized in wind power plants (WPP). The primary objective of implementing EVOA-based AFLCs is to maximize power extraction from the DFIG in wind energy applications while simultaneously improving dynamic response and minimizing errors during operation. The performance of the EVOA-based AFLCs is thoroughly investigated and benchmarked against alternative optimization techniques, specifically chaotic billiards optimization (C-BO), genetic algorithms (GA), and marine predator algorithm (MPA)-based optimal proportional-integral (PI) controllers.
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