Adjustable and Adaptive Control for an Unstable Mobile Robot Using Imitation Learning with Trajectory Optimization
In this contribution, we develop a feedback controller in the form of a parametric function for a mobile inverted pendulum. The control both stabilizes the system and drives it to target positions with target orientations. A design of the controller based only on a cost function is difficult for thi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-04-01
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| Series: | Robotics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-6581/9/2/29 |
| _version_ | 1797569661813915648 |
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| author | Christian Dengler Boris Lohmann |
| author_facet | Christian Dengler Boris Lohmann |
| author_sort | Christian Dengler |
| collection | DOAJ |
| description | In this contribution, we develop a feedback controller in the form of a parametric function for a mobile inverted pendulum. The control both stabilizes the system and drives it to target positions with target orientations. A design of the controller based only on a cost function is difficult for this task, which is why we choose to train the controller using imitation learning on optimized trajectories. In contrast to popular approaches like policy gradient methods, this approach allows us to shape the behavior of the system by including equality constraints. When transferring the parametric controller from simulation to the real mobile inverted pendulum, the control performance is degraded due to the reality gap. A robust control design can reduce the degradation. However, for the framework of imitation learning on optimized trajectories, methods that explicitly consider robustness do not yet exist to the knowledge of the authors. We tackle this research gap by presenting a method to design a robust controller in the form of a recurrent neural network, to improve the transferability of the trained controller to the real system. As a last step, we make the behavior of the parametric controller adjustable to allow for the fine tuning of the behavior of the real system. We design the controller for our system and show in the application that the recurrent neural network has increased performance compared to a static neural network without robustness considerations. |
| first_indexed | 2024-03-10T20:14:45Z |
| format | Article |
| id | doaj.art-b5ef2fa82e9a412f96ade63e3ffc7470 |
| institution | Directory Open Access Journal |
| issn | 2218-6581 |
| language | English |
| last_indexed | 2024-03-10T20:14:45Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Robotics |
| spelling | doaj.art-b5ef2fa82e9a412f96ade63e3ffc74702023-11-19T22:40:34ZengMDPI AGRobotics2218-65812020-04-01922910.3390/robotics9020029Adjustable and Adaptive Control for an Unstable Mobile Robot Using Imitation Learning with Trajectory OptimizationChristian Dengler0Boris Lohmann1Automatic Control, Technical University of Munich, 80333 Munich, GermanyAutomatic Control, Technical University of Munich, 80333 Munich, GermanyIn this contribution, we develop a feedback controller in the form of a parametric function for a mobile inverted pendulum. The control both stabilizes the system and drives it to target positions with target orientations. A design of the controller based only on a cost function is difficult for this task, which is why we choose to train the controller using imitation learning on optimized trajectories. In contrast to popular approaches like policy gradient methods, this approach allows us to shape the behavior of the system by including equality constraints. When transferring the parametric controller from simulation to the real mobile inverted pendulum, the control performance is degraded due to the reality gap. A robust control design can reduce the degradation. However, for the framework of imitation learning on optimized trajectories, methods that explicitly consider robustness do not yet exist to the knowledge of the authors. We tackle this research gap by presenting a method to design a robust controller in the form of a recurrent neural network, to improve the transferability of the trained controller to the real system. As a last step, we make the behavior of the parametric controller adjustable to allow for the fine tuning of the behavior of the real system. We design the controller for our system and show in the application that the recurrent neural network has increased performance compared to a static neural network without robustness considerations.https://www.mdpi.com/2218-6581/9/2/29imitation learningadaptive controlmachine learningmobile robot |
| spellingShingle | Christian Dengler Boris Lohmann Adjustable and Adaptive Control for an Unstable Mobile Robot Using Imitation Learning with Trajectory Optimization Robotics imitation learning adaptive control machine learning mobile robot |
| title | Adjustable and Adaptive Control for an Unstable Mobile Robot Using Imitation Learning with Trajectory Optimization |
| title_full | Adjustable and Adaptive Control for an Unstable Mobile Robot Using Imitation Learning with Trajectory Optimization |
| title_fullStr | Adjustable and Adaptive Control for an Unstable Mobile Robot Using Imitation Learning with Trajectory Optimization |
| title_full_unstemmed | Adjustable and Adaptive Control for an Unstable Mobile Robot Using Imitation Learning with Trajectory Optimization |
| title_short | Adjustable and Adaptive Control for an Unstable Mobile Robot Using Imitation Learning with Trajectory Optimization |
| title_sort | adjustable and adaptive control for an unstable mobile robot using imitation learning with trajectory optimization |
| topic | imitation learning adaptive control machine learning mobile robot |
| url | https://www.mdpi.com/2218-6581/9/2/29 |
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