L4acados: Learning-based models for acados, applied to Gaussian process-based predictive control
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917350554468352 |
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| author | Lahr, Amon Näf, Joshua Wabersich, Kim P. Frey, Jonathan Siehl, Pascal Carron, Andrea Diehl, Moritz Zeilinger, Melanie N. |
| author_facet | Lahr, Amon Näf, Joshua Wabersich, Kim P. Frey, Jonathan Siehl, Pascal Carron, Andrea Diehl, Moritz Zeilinger, Melanie N. |
| contents | Incorporating learning-based models, such as artificial neural networks or Gaussian processes, into model predictive control (MPC) strategies can significantly improve control performance and online adaptation capabilities for real-world applications. Still, enabling state-of-the-art implementations of learning-based models for MPC is complicated by the challenge of interfacing machine learning frameworks with real-time optimal control software. This work aims at filling this gap by incorporating external sensitivities in sequential quadratic programming solvers for nonlinear optimal control. To this end, we provide L4acados, a general framework for incorporating Python-based dynamics models in the real-time optimal control software acados. By computing external sensitivities via a user-defined Python module, L4acados enables the implementation of MPC controllers with learning-based residual models in acados, while supporting parallelization of sensitivity computations when preparing the quadratic subproblems. We demonstrate significant speed-ups and superior scaling properties of L4acados compared to available software using a neural-network-based control example. Last, we provide an efficient and modular real-time implementation of Gaussian process-based MPC using L4acados, which is applied to two hardware examples: autonomous miniature racing, as well as motion control of a full-scale autonomous vehicle for an ISO lane change maneuver. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_19258 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | L4acados: Learning-based models for acados, applied to Gaussian process-based predictive control Lahr, Amon Näf, Joshua Wabersich, Kim P. Frey, Jonathan Siehl, Pascal Carron, Andrea Diehl, Moritz Zeilinger, Melanie N. Systems and Control Robotics Optimization and Control 49M15 G.1.4; G.4 Incorporating learning-based models, such as artificial neural networks or Gaussian processes, into model predictive control (MPC) strategies can significantly improve control performance and online adaptation capabilities for real-world applications. Still, enabling state-of-the-art implementations of learning-based models for MPC is complicated by the challenge of interfacing machine learning frameworks with real-time optimal control software. This work aims at filling this gap by incorporating external sensitivities in sequential quadratic programming solvers for nonlinear optimal control. To this end, we provide L4acados, a general framework for incorporating Python-based dynamics models in the real-time optimal control software acados. By computing external sensitivities via a user-defined Python module, L4acados enables the implementation of MPC controllers with learning-based residual models in acados, while supporting parallelization of sensitivity computations when preparing the quadratic subproblems. We demonstrate significant speed-ups and superior scaling properties of L4acados compared to available software using a neural-network-based control example. Last, we provide an efficient and modular real-time implementation of Gaussian process-based MPC using L4acados, which is applied to two hardware examples: autonomous miniature racing, as well as motion control of a full-scale autonomous vehicle for an ISO lane change maneuver. |
| title | L4acados: Learning-based models for acados, applied to Gaussian process-based predictive control |
| topic | Systems and Control Robotics Optimization and Control 49M15 G.1.4; G.4 |
| url | https://arxiv.org/abs/2411.19258 |