Adaptive Nonlinear Model Predictive Control for a Real-World Labyrinth Game
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910819536601088 |
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| author | Gaber, Johannes Bi, Thomas D'Andrea, Raffaello |
| author_facet | Gaber, Johannes Bi, Thomas D'Andrea, Raffaello |
| contents | We present a nonlinear non-convex model predictive control approach to solving a real-world labyrinth game. We introduce adaptive nonlinear constraints, representing the non-convex obstacles within the labyrinth. Our method splits the computation-heavy optimization problem into two layers; first, a high-level model predictive controller which incorporates the full problem formulation and finds pseudo-global optimal trajectories at a low frequency. Secondly, a low-level model predictive controller that receives a reduced, computationally optimized version of the optimization problem to follow the given high-level path in real-time. Further, a map of the labyrinth surface irregularities is learned. Our controller is able to handle the major disturbances and model inaccuracies encountered on the labyrinth and outperforms other classical control methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08650 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive Nonlinear Model Predictive Control for a Real-World Labyrinth Game Gaber, Johannes Bi, Thomas D'Andrea, Raffaello Robotics Systems and Control We present a nonlinear non-convex model predictive control approach to solving a real-world labyrinth game. We introduce adaptive nonlinear constraints, representing the non-convex obstacles within the labyrinth. Our method splits the computation-heavy optimization problem into two layers; first, a high-level model predictive controller which incorporates the full problem formulation and finds pseudo-global optimal trajectories at a low frequency. Secondly, a low-level model predictive controller that receives a reduced, computationally optimized version of the optimization problem to follow the given high-level path in real-time. Further, a map of the labyrinth surface irregularities is learned. Our controller is able to handle the major disturbances and model inaccuracies encountered on the labyrinth and outperforms other classical control methods. |
| title | Adaptive Nonlinear Model Predictive Control for a Real-World Labyrinth Game |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2406.08650 |