Title |
Identification of optimal feedback control rules from micro-quadrotor and insect flight trajectories
|
---|---|
Published in |
Biological Cybernetics, January 2018
|
DOI | 10.1007/s00422-017-0742-x |
Pubmed ID | |
Authors |
Imraan A. Faruque, Florian T. Muijres, Kenneth M. Macfarlane, Andrew Kehlenbeck, J. Sean Humbert |
Abstract |
This paper presents "optimal identification," a framework for using experimental data to identify the optimality conditions associated with the feedback control law implemented in the measurements. The technique compares closed loop trajectory measurements against a reduced order model of the open loop dynamics, and uses linear matrix inequalities to solve an inverse optimal control problem as a convex optimization that estimates the controller optimality conditions. In this study, the optimal identification technique is applied to two examples, that of a millimeter-scale micro-quadrotor with an engineered controller on board, and the example of a population of freely flying Drosophila hydei maneuvering about forward flight. The micro-quadrotor results show that the performance indices used to design an optimal flight control law for a micro-quadrotor may be recovered from the closed loop simulated flight trajectories, and the Drosophila results indicate that the combined effect of the insect longitudinal flight control sensing and feedback acts principally to regulate pitch rate. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 33% |
United States | 1 | 33% |
Unknown | 1 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 67% |
Members of the public | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 22 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 6 | 27% |
Student > Master | 4 | 18% |
Student > Bachelor | 3 | 14% |
Researcher | 2 | 9% |
Professor | 2 | 9% |
Other | 3 | 14% |
Unknown | 2 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 9 | 41% |
Agricultural and Biological Sciences | 3 | 14% |
Computer Science | 2 | 9% |
Physics and Astronomy | 1 | 5% |
Business, Management and Accounting | 1 | 5% |
Other | 4 | 18% |
Unknown | 2 | 9% |