Generator governing system based on grey prediction and extension control

Wang, Mingdong; Wang, Xueke; Su, Xiangjing; Li, Xiangyang; Yang, Yin

doi:10.1049/iet-gtd.2016.0947

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…The different control algorithms are adopted in the three different domains for improving the vehicle lane keeping system control performance. But through equation (17), it is easy to find out that the control output is expressed in a segmented function which is naturally due to the extension control design principle. This control way results in the control variation near the control domain boundaries.…”

Section: Tsk Fuzzy Extension Controller Designmentioning

confidence: 99%

“…Extension control provides a new way to solve this problem, and a small amount of literature have been reported in the related fields. For example, in the design of generator speed control system, M Wang et al 17 proposed a gray prediction extension control algorithm to improve the performance of the control system and the stability of the power system. X Lü et al 18 put forward an extension control scheme to improve the fuel cell hybrid power system dynamic characteristics, and used the state distance instead of the original parameters to solve the problem of the parameter tuning, so that extension control method was improved.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle lane keeping system based on TSK fuzzy extension control

Wang

Cui

Xia

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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In the traditional lane keeping system, a single control algorithm is used for global-region control, which often results in poor control effect in some control areas. In view of this problem, a control method combining the extension control and Takagi-Sugeno-Kang fuzzy control is proposed for lane keeping system control. The extension control strategy based on the idea of control with area division divides different control regions according to the states of the system, and adopts different control algorithms in different control areas to address the limitation of a single control algorithm. For the randomness and inaccuracy of artificial experience in control domain division, a method for control domain division based on the density distribution of characteristic plane is proposed in this article. In order to further improve and explore the performance of extension control, Takagi-Sugeno-Kang extension controller is designed with Takagi-Sugeno-Kang fuzzy control theory, and the extension control performance is improved when switching among different control algorithms. The proposed control strategy is simulated and tested on the CarSim/Simulink joint simulation platform and the CarSim/LabVIEW hardware-in-loop test bench, respectively. The results show that the proposed control strategy can effectively keep the vehicle near the center line of the lane, and both the lane keeping control accuracy and vehicle stability are improved, which guarantees driving safety and comfort.

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Section: Tsk Fuzzy Extension Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicle lane keeping system based on TSK fuzzy extension control

Wang

Cui

Xia

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…Since the 1980s, the discrete grey prediction model (DGPM) has tempted broad research interests because of its efficient and fast computation [14,15]. The DGPM just needs a few sampled data to depict the tendency of the time-series data from the anterior system dynamics, acquiring the dependable and acceptable forecast accuracy [16][17][18][19]. The DGPM is thus employed to lessen the chattering when the dynamic system with uncertainty bounds is overestimated.…”

Section: Introductionmentioning

confidence: 99%

Robust Stable Control Design for AC Power Supply Applications

Chang

Yang

2019

Electronics

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This paper applies modified feedback technology to carry out the exact steady-state and fast transient in a high-performance alternating current (AC) power supply. The presented scheme displays the virtues of a finite-time convergence control (FTCC) and a discrete grey prediction model (DGPM). The FTCC, derived from a terminal sliding-mode (TSM) design principle, can produce the finite system-state convergence time and evade the singularity. It is noteworthy that the chattering/steady-state error around the FTCC may occur because of the overestimated or underestimated uncertainty bound. The DGPM with the bound estimate ability is integrated into the FTCC to cope with internal parameter variations and external load disturbances. The less chattering and steady-state error can be obtained, providing more robust performance in the AC power supply. The combination of the FTCC and the DGPM extends the standard TSM design for the purpose of faster singularity-free convergence, as well as introducing the grey modeling method in the case of a more exact uncertainty estimate. The modified control technology has a high-precision tracking performance and a fast convergent speed. Simulated and experimental results point out that the modified control technology can effectuate low total harmonic distortion (THD) and fast dynamic response in the presence of rectifier loads and abrupt step load changes.

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