Time Dependent Inverse Optimal Control using Trigonometric Basis Functions

Rahel Rickenbach, Elena Arcari, Melanie Zeilinger
Proceedings of The 5th Annual Learning for Dynamics and Control Conference, PMLR 211:1193-1204, 2023.

Abstract

The choice of objective is critical for the performance of an optimal controller. When control requirements vary during operation, e.g. due to changes in the environment with which the system is interacting, these variations should be reflected in the cost function. In this paper we consider the problem of identifying a time dependent cost function from given trajectories. We propose a strategy for explicitly representing time dependency in the cost function, i.e. decomposing it into the product of an unknown time dependent parameter vector and a known state and input dependent vector, modelling the former via a linear combination of trigonometric basis functions. These are incorporated within an inverse optimal control framework that uses the Karush–Kuhn–Tucker (KKT) conditions for ensuring optimality, and allows for formulating an optimization problem with respect to a finite set of basis function hyperparameters. Results are shown for two systems in simulation and evaluated against state-of-the-art approaches.

Cite this Paper

BibTeX
@InProceedings{pmlr-v211-rickenbach23a, title = {Time Dependent Inverse Optimal Control using Trigonometric Basis Functions}, author = {Rickenbach, Rahel and Arcari, Elena and Zeilinger, Melanie}, booktitle = {Proceedings of The 5th Annual Learning for Dynamics and Control Conference}, pages = {1193--1204}, year = {2023}, editor = {Matni, Nikolai and Morari, Manfred and Pappas, George J.}, volume = {211}, series = {Proceedings of Machine Learning Research}, month = {15--16 Jun}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v211/rickenbach23a/rickenbach23a.pdf}, url = {https://proceedings.mlr.press/v211/rickenbach23a.html}, abstract = {The choice of objective is critical for the performance of an optimal controller. When control requirements vary during operation, e.g. due to changes in the environment with which the system is interacting, these variations should be reflected in the cost function. In this paper we consider the problem of identifying a time dependent cost function from given trajectories. We propose a strategy for explicitly representing time dependency in the cost function, i.e. decomposing it into the product of an unknown time dependent parameter vector and a known state and input dependent vector, modelling the former via a linear combination of trigonometric basis functions. These are incorporated within an inverse optimal control framework that uses the Karush–Kuhn–Tucker (KKT) conditions for ensuring optimality, and allows for formulating an optimization problem with respect to a finite set of basis function hyperparameters. Results are shown for two systems in simulation and evaluated against state-of-the-art approaches.} }
Endnote
%0 Conference Paper %T Time Dependent Inverse Optimal Control using Trigonometric Basis Functions %A Rahel Rickenbach %A Elena Arcari %A Melanie Zeilinger %B Proceedings of The 5th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2023 %E Nikolai Matni %E Manfred Morari %E George J. Pappas %F pmlr-v211-rickenbach23a %I PMLR %P 1193--1204 %U https://proceedings.mlr.press/v211/rickenbach23a.html %V 211 %X The choice of objective is critical for the performance of an optimal controller. When control requirements vary during operation, e.g. due to changes in the environment with which the system is interacting, these variations should be reflected in the cost function. In this paper we consider the problem of identifying a time dependent cost function from given trajectories. We propose a strategy for explicitly representing time dependency in the cost function, i.e. decomposing it into the product of an unknown time dependent parameter vector and a known state and input dependent vector, modelling the former via a linear combination of trigonometric basis functions. These are incorporated within an inverse optimal control framework that uses the Karush–Kuhn–Tucker (KKT) conditions for ensuring optimality, and allows for formulating an optimization problem with respect to a finite set of basis function hyperparameters. Results are shown for two systems in simulation and evaluated against state-of-the-art approaches.
APA
Rickenbach, R., Arcari, E. & Zeilinger, M.. (2023). Time Dependent Inverse Optimal Control using Trigonometric Basis Functions. Proceedings of The 5th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 211:1193-1204 Available from https://proceedings.mlr.press/v211/rickenbach23a.html.

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