Robust Control of Uncertain Linear Systems Based on Reinforcement Learning Principles

Xu, Dengguo; Wang, Qinglin; Li, Yuan

doi:10.1109/access.2019.2894594

Cited by 5 publications

(4 citation statements)

References 50 publications

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“…In overall, to assign the interval polynomial (8), all zeros of the family in polynomials (9) should have a real negative part as adopted in References 10-12.…”

Section: Interval Systemmentioning

confidence: 99%

State feedback control for stabilization of PMSM ‐based servo‐drive with parametric uncertainty using interval analysis

Khelouat

Ahriche

Mekhilef

2021

Int Trans Electr Energ Syst

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“…In overall, to assign the interval polynomial (8), all zeros of the family in polynomials (9) should have a real negative part as adopted in References 10-12.…”

Section: Interval Systemmentioning

confidence: 99%

State feedback control for stabilization of PMSM ‐based servo‐drive with parametric uncertainty using interval analysis

Khelouat

Ahriche

Mekhilef

2021

Int Trans Electr Energ Syst

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“…28 Xu et al enhanced the reliability of WPGS through importance gradient assessment, which is essential for the safe operation of the entire grid network. 29 Falugi and Mayne optimized the operational reliability of WPGS based on the real-time state of wind turbines. 30 Guntur et al described the efficiency of the FAST v8 code, an aeroelastic engineering simulation tool for wind turbines.…”

Section: Introductionmentioning

confidence: 99%

“…This achieved a smooth power output and reduced dynamic loads 28 . Xu et al enhanced the reliability of WPGS through importance gradient assessment, which is essential for the safe operation of the entire grid network 29 . Falugi and Mayne optimized the operational reliability of WPGS based on the real‐time state of wind turbines 30 .…”

Section: Introductionmentioning

confidence: 99%

Robust control of wind turbines to reduce wind power fluctuation

Tang,

Wang,

Zhen

et al. 2024

Energy Science & Engineering

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The wind power generation system of a 5 MW horizontal axis wind turbine has a high wind energy conversion efficiency. The proportion of installed capacity in practical production applications is increasing year on year, so that the stability of its operation becomes a central factor in determining the productivity of the wind farm in question. This paper takes a 5 MW wind turbine as the research object and proposes a parameter‐adaptive robust model predictive control method to achieve self‐optimization of controller parameters through a Bayesian optimization approach. A robust model predictive control strategy, aiming to reduce the power fluctuation while maximizing the power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A Bayesian algorithm is used in this paper to optimize the parameters of the controller. Moreover, wind speeds are simulated using TurbSim for different turbulence intensities of 5%, 10%, and 15% turbulence. Finally, the robust model predictive control toolbox in MATLAB is designed and simulated. The results show that the operational instability of the wind energy system is overcome. Meanwhile, the robustness of the wind energy system operation is improved compared to the traditional model predictive control approach.

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“…In addition, random fluctuation of wind speed not only affects the steady performance of wind power generation, but also requires constant switching of control targets according Tang et al 10.3389/fenrg.2023.1306167 to different wind speed ranges (Gao et al, 2022). Therefore, this paper focuses on how to minimize the influences of wind speed variations on the steady performance of wind turbines, which will place further demands on the controlling methods of wind power systems (Xu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Robust model predictive control of wind turbines based on Bayesian parameter self-optimization

Tang,

Wang,

Yan

et al. 2023

Front. Energy Res.

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This paper studies the effect of different turbulent wind speeds on the operation of wind turbines. The proportion of wind power in the field of new energy generation has increased massively and has gained wide application and attention. However, the smooth operation and the stability of the output power of the wind power generation system are susceptible to wind speed fluctuations. To tackle this problem, this paper takes a 5 MW horizontal axis wind turbine as the research object that proposes a parameter adaptive robust control method to achieve self-optimization of controller parameters by means of Bayesian optimization. The 5 MW wind turbine model is utilized to verify the feasibility of the algorithm by combining the wind speed types commonly found in a high-altitude region in northwestern. The simulation results validate the effectiveness of the proposed scheme. The outcomes demonstrate that Bayesian optimization can significantly decrease the effects of wind speed instability. The output power increases by 1.9% on average at low wind speed and stabilizes on 5 MW at high wind speed. Therefore, the stable controller for wind power output is the robust model predictive controller with parameter improvement.

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Robust Control of Uncertain Linear Systems Based on Reinforcement Learning Principles

Cited by 5 publications

References 50 publications

State feedback control for stabilization of PMSM ‐based servo‐drive with parametric uncertainty using interval analysis

State feedback control for stabilization of PMSM ‐based servo‐drive with parametric uncertainty using interval analysis

Robust control of wind turbines to reduce wind power fluctuation

Robust model predictive control of wind turbines based on Bayesian parameter self-optimization

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