Nonlinear optimal control of a ballast-stabilized floating wind turbine via
externally stabilised Hamiltonian dynamics
externally stabilised Hamiltonian dynamics
File(s)optimal_control_wind_turbine.pdf (338.78 KB)
Accepted version
Author(s)
Mylvaganam, Thulasi
Sassano, Mario
Astolfi, Alessandro
Type
Conference Paper
Abstract
We consider the problem of controlling a ballast-stabilized offshore wind turbine. We formulate an optimal control problem with the objective of maximising the power generation while minimising structural fatigue of the wind turbine. Due to the nonlinear nature of the model, obtaining a solution to the above control task poses a severe challenge.Recalling that solutions of the optimal control problem are characterised by a certain (unstable) invariant manifold of the underlying Hamiltonian system, we demonstrate that nonlinear control strategies which approximate the solution of the optimalcontrol problem can be constructed through the introduction of an externally stabilised Hamiltonian system. This observation enables the construction of an algorithm to compute (with rel-atively low computational complexity) an approximate solution of the optimal control problem, without ignoring nonlinearities in the control design. This approach has several benefits, asdemonstrated via simulations on a ballast-stabilized offshore wind turbine.
Date Issued
2022-02-01
Date Acceptance
2021-07-31
Citation
2022, pp.2428-2433
Publisher
IEEE
Start Page
2428
End Page
2433
Copyright Statement
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Commission of the European Communities
Identifier
https://ieeexplore.ieee.org/document/9682815
Grant Number
739551
Source
IEEE Conference on Decision and Control
Subjects
Science & Technology
Technology
Automation & Control Systems
Engineering, Electrical & Electronic
Engineering
Publication Status
Published
Start Date
2021-12-13
Finish Date
2021-12-17
Coverage Spatial
Austin, Texas, USA
Date Publish Online
2022-02-01