NDLI: Online predictive control based trajectory optimization of tethered foils for wind energy generator

Content Provider	IEEE Xplore Digital Library
Author	Sun Mingwei Liu Donghui Quan Sheng Chen Zengqiang
Copyright Year	2012
Description	Author affiliation: Department of Automation, School of Information Technology, Nankai University, Tianjin 300071, China (Sun Mingwei; Liu Donghui; Chen Zengqiang) \|\| Beijing Electro-mechanical Engineering Institute, Beijing 100074, China (Quan Sheng)
Abstract	Capturing the stable wind energy in the high altitude is a novel green generation approach. Rational trajectory optimization can obtain the maximal power difference between the traction phase and the recovery phase. Receding horizon model predictive control in the offline manner was previously employed to handle this type of problem, however it is time-consuming and lacks of adaptability and flexibility to varying aerodynamic parameters. This paper presents a receding horizon optimization method for the tethered foil generator based on an online searching strategy. The nonlinear optimization problem can be approximately reformulated to a univariate receding horizon sub-optimal issue in a short interval in four phases with different objectives. By using uniform sampling and chaotic search approaches, the sub-optimal solution, subject to the physical constraints, is obtained online. The simulation results demonstrate its effectiveness. This algorithm has the potential to be realized in a parallel pattern.
Starting Page	4183
Ending Page	4188
File Size	600603
Page Count	6
File Format	PDF
ISBN	9781467325813
ISSN	21612927
e-ISBN	9789881563811
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Chaos Wind energy Optimal control Tethered foil for wind energy generator Educational institutions Generators Trajectory Electronic mail Optimization Trajectory optimization Predictive control
Content Type	Text
Resource Type	Article

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