Tradeoff-optimal-controller based on compact fuzzy data-driven model and multi-gradient learning

The stable flight region can be extended by adding control flap at the wing trailing edge and combined with active control technology. We studied the active flutter control by considering the input constraints. By designing the data-driven optimal controller, the limit cycle oscillations of a typical two-dimensional airfoil wing can be suppressed with single trailing edge control surface. The traditional control methods always need a precise mathematical model of the system, which put high requirements on system modeling. In this study, a novel data-driven optimal controller is proposed by using the input–output data and without depending on the nonlinear system dynamic model. This model-free approach avoids the effects of modeling errors and system uncertainty. When the data-driven controller is applied, the limit cycle oscillation phenomenon of the airfoil wing is eliminated within several seconds. It can be seen from the numerical simulation result that the data-driven adaptive dynamic programming control method possess superiority and feasibility.

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A fuzzy data-driven and rule-based reasoning system for setting the nano- particle milling process parameters

2007 IEEE International Conference on Industrial Engineering and Engineering Management ◽

10.1109/ieem.2007.4419256 ◽

2007 ◽

Author(s):

Tung-Hsu Hou ◽

Chi-Hung Su

Keyword(s):

Process Parameters ◽

Milling Process ◽

Data Driven ◽

Fuzzy Data ◽

Nano Particle ◽

Rule Based ◽

Reasoning System

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Data-Driven Virtual Inertia Control Method of Doubly Fed Wind Turbine

Energies ◽

10.3390/en14175572 ◽

2021 ◽

Vol 14 (17) ◽

pp. 5572

Author(s):

Tai Li ◽

Leqiu Wang ◽

Yanbo Wang ◽

Guohai Liu ◽

Zhiyu Zhu ◽

...

Keyword(s):

Wind Turbine ◽

Riccati Equation ◽

Control Method ◽

Frequency Measurement ◽

Data Driven ◽

Optimal Controller ◽

Differential Riccati Equation ◽

Virtual Inertia ◽

Inertia Control ◽

Doubly Fed

This paper presents a data-driven virtual inertia control method for doubly fed induction generator (DFIG)-based wind turbine to provide inertia support in the presence of frequency events. The Markov parameters of the system are first obtained by monitoring the grid frequency and system operation state. Then, a data-driven state observer is developed to evaluate the state vector of the optimal controller. Furthermore, the optimal controller of the inertia emulation system is developed through the closed solution of the differential Riccati equation. Moreover, a differential Riccati equation with self-correction capability is developed to enhance the anti-noise ability to reject noise interference in frequency measurement process. Finally, the simulation verification was performed in Matlab/Simulink to validate the effectiveness of the proposed control strategy. Simulation results showed that the proposed virtual inertia controller can adaptively tune control parameters online to provide transient inertia supports for the power grid by releasing the kinetic energy, so as to improve the robustness and anti-interference ability of the control system of the wind power system.

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