Abstract
The paper develops a feature learning-based method to solve optimal control problems using B-splines to approximate the optimal solutions. The feature learning-based optimal control method can quickly generate near-optimal trajectories for general optimal control problems subject to system dynamics and constraints. The steps in the proposed method are as follows: Firstly, by representing the state and control variables with B-spline functions, the optimal control problem is converted into an approximate nonlinear programming (NLP) problem, where parameters of the B-splines are identified as features of the optimal solution. Secondly, for a specific problem with designated inputs, a dataset of the optimal trajectories under varying inputs is generated by solving the corresponding NLP problem offline. Finally, the neural network is applied to map the relationship between the designated inputs and identified features, represented by the parameters of B-splines and time variables. To show the effectiveness and efficiency of the proposed method for solving the optimal control problems, extensive simulation cases are presented and analyzed.
Recommended Citation
V. Kenny et al., "Feature Learning for Optimal Control with B-Spline Representations," Proceedings of the American Control Conference, pp. 2917 - 2923, Institute of Electrical and Electronics Engineers, Jan 2022.
The definitive version is available at https://doi.org/10.23919/ACC53348.2022.9867713
Department(s)
Mechanical and Aerospace Engineering
Keywords and Phrases
B-spline; Optimal Control; Supervised Learning
International Standard Book Number (ISBN)
978-166545196-3
International Standard Serial Number (ISSN)
0743-1619
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2022
