Sampling-based Near Time-Optimal Trajectory Generation for Pneumatic Drives
When servo-pneumatic drives are applied in automation, their motion trajectories should be fast to maximize productivity. There occur nonlinear state-dependent jerk constraints because the pressure dynamics are not negligibly fast, the air mass flow through the valves is subject to pressure-dependen...
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| Vydané v: | IEEE International Conference on Automation Science and Engineering (CASE) s. 513 - 518 |
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| Hlavní autori: | , , , , |
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| Jazyk: | English |
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IEEE
17.08.2025
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| ISSN: | 2161-8089 |
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| Abstract | When servo-pneumatic drives are applied in automation, their motion trajectories should be fast to maximize productivity. There occur nonlinear state-dependent jerk constraints because the pressure dynamics are not negligibly fast, the air mass flow through the valves is subject to pressure-dependent constraints, and the mechanics and pneumatics are coupled. The goal of this work is to generate near time-optimal trajectories for pneumatic drives, taking into account the aforementioned in a model-based way. To this end, first, the system dynamics and constraints are formulated using differential flatness such that they can be incorporated into trajectory generation frameworks. Then, the class of sampling-based near time-optimal path parametrization approaches, which build a tree of samples in the path parameter space, is chosen and extended to the present type of constraints. Results for various scenarios are discussed, compared to our previous work where nonlinear programming was applied, and validated in real-world experiments. The experimental outcomes demonstrate the applicability of the sampling-based algorithm to the present system. |
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| AbstractList | When servo-pneumatic drives are applied in automation, their motion trajectories should be fast to maximize productivity. There occur nonlinear state-dependent jerk constraints because the pressure dynamics are not negligibly fast, the air mass flow through the valves is subject to pressure-dependent constraints, and the mechanics and pneumatics are coupled. The goal of this work is to generate near time-optimal trajectories for pneumatic drives, taking into account the aforementioned in a model-based way. To this end, first, the system dynamics and constraints are formulated using differential flatness such that they can be incorporated into trajectory generation frameworks. Then, the class of sampling-based near time-optimal path parametrization approaches, which build a tree of samples in the path parameter space, is chosen and extended to the present type of constraints. Results for various scenarios are discussed, compared to our previous work where nonlinear programming was applied, and validated in real-world experiments. The experimental outcomes demonstrate the applicability of the sampling-based algorithm to the present system. |
| Author | Sawodny, Oliver Baumgart, Michaela Verl, Alexander Hoffmann, Kathrin Kanagalingam, Gajanan |
| Author_xml | – sequence: 1 givenname: Kathrin surname: Hoffmann fullname: Hoffmann, Kathrin email: kathrin.hoffmann@isys.uni-stuttgart.de organization: Institute for System Dynamics, University of Stuttgart,Germany – sequence: 2 givenname: Michaela surname: Baumgart fullname: Baumgart, Michaela organization: Institute for System Dynamics, University of Stuttgart,Germany – sequence: 3 givenname: Gajanan surname: Kanagalingam fullname: Kanagalingam, Gajanan organization: Institute for System Dynamics, University of Stuttgart,Germany – sequence: 4 givenname: Alexander surname: Verl fullname: Verl, Alexander organization: Institute for Control Engineering of Machine Tools and Manufacturing Units, University of Stuttgart,Germany – sequence: 5 givenname: Oliver surname: Sawodny fullname: Sawodny, Oliver email: sawodny@isys.uni-stuttgart.de organization: Institute for System Dynamics, University of Stuttgart,Germany |
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| Snippet | When servo-pneumatic drives are applied in automation, their motion trajectories should be fast to maximize productivity. There occur nonlinear state-dependent... |
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| StartPage | 513 |
| SubjectTerms | Automation Computational modeling Dynamics Heuristic algorithms Optimization Productivity Programming System dynamics Trajectory Valves |
| Title | Sampling-based Near Time-Optimal Trajectory Generation for Pneumatic Drives |
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