Power generation control of a hydrostatic wind turbine implemented by model‐free adaptive control scheme

The variation of aeroelastic system dynamics is treated as the change of time-varying aerodynamic loads along the operation trajectory of a spinning wind turbine. An Adaptive Control scheme is introduced to suppress flutter based on the proposed model. The robustness and effectiveness of Adaptive Control is shown by simulation results. For stability analysis, Adaptive Stability Theorem is proved theoretically by Kalman-Yacubovic Lemma and demonstrated numerically by certain cases.

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Model-free adaptive control scheme for EGR/VNT control of a diesel engine using the simultaneous perturbation stochastic approximation

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331215602327 ◽

2016 ◽

Vol 39 (1) ◽

pp. 114-128 ◽

Cited By ~ 4

Author(s):

Shinichi Ishizuka ◽

Itsuro Kajiwara ◽

Junichi Sato ◽

Yoshifumi Hanamura ◽

Satoshi Hanawa

Keyword(s):

Adaptive Control ◽

Diesel Engine ◽

Stochastic Approximation ◽

Simultaneous Perturbation Stochastic Approximation ◽

Model Free ◽

Control Scheme ◽

Adaptive Control Scheme

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Performance Analysis and Structure Optimization of a Nautilus Isometric Spiral Wind Turbine

Energies ◽

10.3390/en13010120 ◽

2019 ◽

Vol 13 (1) ◽

pp. 120

Author(s):

Zheng Li ◽

Wenda Zhang ◽

Hao Dong ◽

Yongsheng Tian

Keyword(s):

Power Generation ◽

Control System ◽

Wind Turbine ◽

Aerodynamic Performance ◽

Energy Utilization ◽

Simulation Platform ◽

Grid Connection ◽

Power Generation Control ◽

Generation Control ◽

Power Generation Performance

Background: This paper proposes a Nautilus isometric spiral vertical axis wind turbine, which is a new structure, and its aerodynamic performance and power generation performance need to be analyzed. Methods: A 3D model of the wind turbine was built and its aerodynamic performance was analyzed. Then the wind turbine power generation and grid-connected simulation platform was built by MATLAB/SIMULINK, and its power generation performance and subsequent grid connection were studied. Results: The basic parameters of the wind turbine were obtained. In order to improve efficiency, parameters such as pressure, torque, wind energy utilization rate and relative velocity of wind turbines with different blade numbers and different sizes were compared. In addition, by building a simulation platform for the power generation control system, the power generation characteristics and grid connection characteristic curves of the generator were obtained. Conclusions: When the number of blades is three and the ratio between the ellipse major axis and minor axis of the blade inlet is 0.76, the best efficiency of the wind turbine can be obtained. Application of the power generation control system used in this paper can achieve grid-connected operation of this wind turbine. It also confirmed that the Nautilus isometric spiral wind turbine has good performance and is worthy of in-depth research.

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Model-Free Adaptive Sliding Mode Robust Control with Neural Network Estimator for the Multi-Degree-of-Freedom Robotic Exoskeleton

Complexity ◽

10.1155/2020/8327456 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Xiangjian Chen ◽

Di Li ◽

Pingxin Wang ◽

Xibei Yang ◽

Hongmei Li

Keyword(s):

Neural Network ◽

Adaptive Control ◽

Robust Control ◽

Sliding Mode ◽

Robotic Exoskeleton ◽

Adaptive Sliding Mode ◽

Model Free ◽

Control Scheme ◽

System Uncertainties ◽

Adaptive Control Scheme

A new model-free adaptive robust control method has been proposed for the robotic exoskeleton, and the proposed control scheme depends only on the input and output data, which is different from model-based control algorithms that require exact dynamic model knowledge of the robotic exoskeleton. The dependence of the control algorithm on the prior knowledge of the robotic exoskeleton dynamics model is reduced, and the influence of the system uncertainties are compensated by using the model-free adaptive sliding mode controller based on data-driven methodology and neural network estimator, which improves the robustness of the system. Finally, real-time experimental results show that the control scheme proposed in this paper achieves better control performances with good robustness with respect to system uncertainties and external wind disturbances compared with the model-free adaptive control scheme and model-free sliding mode adaptive control scheme.

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