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Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators
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| Zeitschriftentitel: | International Journal of Advanced Robotic Systems |
|---|---|
| Personen und Körperschaften: | , |
| In: | International Journal of Advanced Robotic Systems, 16, 2019, 1, S. 172988141982807 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
SAGE Publications
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| Schlagwörter: |
| author_facet |
Zhou, Xu Zhang, Xiaoli Zhou, Xu Zhang, Xiaoli |
|---|---|
| author |
Zhou, Xu Zhang, Xiaoli |
| spellingShingle |
Zhou, Xu Zhang, Xiaoli International Journal of Advanced Robotic Systems Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators Artificial Intelligence Computer Science Applications Software |
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zhou, xu |
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Zhou, Xu Zhang, Xiaoli 1729-8814 1729-8814 SAGE Publications Artificial Intelligence Computer Science Applications Software http://dx.doi.org/10.1177/1729881419828071 <jats:p> The aerial manipulator has recently attracted much research attention due to its wide applications such as aerial cleaning, aerial transportation, and aerial manipulation. It is important to design a reliable controller for the aerial manipulator to robustly perform aerial tasks with different settings. However, current controllers still employ manual parameters tuning methods, which is mostly limited to a specific setting like a fixed aerial manipulator configuration or an unchanged environment. In fact, there could be diverse configurations of aerial manipulators and uncertain environments in practice, which requires the manual tuning process to be frequently repeated. This repetition is easy to be unavailable due to its significant cost of time and expensive involvements of control-tuning experts. To solve these problems, a novel multi-objective-optimization-based control parameters auto-tuning method is proposed for the aerial manipulator. Based on a conventional proportional–integral–derivative control structure, an evolutionary-algorithm-based optimization is used to automatically find optimal proportional–integral–derivative control parameters to satisfy conflicting objectives such as minimizing the integrated time square error and the control rate. Simulation results prove that the proposed method can achieve better control performances like smaller overshoots and faster stabilization time than manual tuning methods. </jats:p> Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators International Journal of Advanced Robotic Systems |
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| title |
Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators |
| title_unstemmed |
Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators |
| title_full |
Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators |
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Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators |
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Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators |
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Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators |
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multi-objective-optimization-based control parameters auto-tuning for aerial manipulators |
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Artificial Intelligence Computer Science Applications Software |
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http://dx.doi.org/10.1177/1729881419828071 |
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2019 |
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<jats:p> The aerial manipulator has recently attracted much research attention due to its wide applications such as aerial cleaning, aerial transportation, and aerial manipulation. It is important to design a reliable controller for the aerial manipulator to robustly perform aerial tasks with different settings. However, current controllers still employ manual parameters tuning methods, which is mostly limited to a specific setting like a fixed aerial manipulator configuration or an unchanged environment. In fact, there could be diverse configurations of aerial manipulators and uncertain environments in practice, which requires the manual tuning process to be frequently repeated. This repetition is easy to be unavailable due to its significant cost of time and expensive involvements of control-tuning experts. To solve these problems, a novel multi-objective-optimization-based control parameters auto-tuning method is proposed for the aerial manipulator. Based on a conventional proportional–integral–derivative control structure, an evolutionary-algorithm-based optimization is used to automatically find optimal proportional–integral–derivative control parameters to satisfy conflicting objectives such as minimizing the integrated time square error and the control rate. Simulation results prove that the proposed method can achieve better control performances like smaller overshoots and faster stabilization time than manual tuning methods. </jats:p> |
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| description | <jats:p> The aerial manipulator has recently attracted much research attention due to its wide applications such as aerial cleaning, aerial transportation, and aerial manipulation. It is important to design a reliable controller for the aerial manipulator to robustly perform aerial tasks with different settings. However, current controllers still employ manual parameters tuning methods, which is mostly limited to a specific setting like a fixed aerial manipulator configuration or an unchanged environment. In fact, there could be diverse configurations of aerial manipulators and uncertain environments in practice, which requires the manual tuning process to be frequently repeated. This repetition is easy to be unavailable due to its significant cost of time and expensive involvements of control-tuning experts. To solve these problems, a novel multi-objective-optimization-based control parameters auto-tuning method is proposed for the aerial manipulator. Based on a conventional proportional–integral–derivative control structure, an evolutionary-algorithm-based optimization is used to automatically find optimal proportional–integral–derivative control parameters to satisfy conflicting objectives such as minimizing the integrated time square error and the control rate. Simulation results prove that the proposed method can achieve better control performances like smaller overshoots and faster stabilization time than manual tuning methods. </jats:p> |
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| spelling | Zhou, Xu Zhang, Xiaoli 1729-8814 1729-8814 SAGE Publications Artificial Intelligence Computer Science Applications Software http://dx.doi.org/10.1177/1729881419828071 <jats:p> The aerial manipulator has recently attracted much research attention due to its wide applications such as aerial cleaning, aerial transportation, and aerial manipulation. It is important to design a reliable controller for the aerial manipulator to robustly perform aerial tasks with different settings. However, current controllers still employ manual parameters tuning methods, which is mostly limited to a specific setting like a fixed aerial manipulator configuration or an unchanged environment. In fact, there could be diverse configurations of aerial manipulators and uncertain environments in practice, which requires the manual tuning process to be frequently repeated. This repetition is easy to be unavailable due to its significant cost of time and expensive involvements of control-tuning experts. To solve these problems, a novel multi-objective-optimization-based control parameters auto-tuning method is proposed for the aerial manipulator. Based on a conventional proportional–integral–derivative control structure, an evolutionary-algorithm-based optimization is used to automatically find optimal proportional–integral–derivative control parameters to satisfy conflicting objectives such as minimizing the integrated time square error and the control rate. Simulation results prove that the proposed method can achieve better control performances like smaller overshoots and faster stabilization time than manual tuning methods. </jats:p> Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators International Journal of Advanced Robotic Systems |
| spellingShingle | Zhou, Xu, Zhang, Xiaoli, International Journal of Advanced Robotic Systems, Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators, Artificial Intelligence, Computer Science Applications, Software |
| title | Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators |
| title_full | Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators |
| title_fullStr | Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators |
| title_full_unstemmed | Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators |
| title_short | Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators |
| title_sort | multi-objective-optimization-based control parameters auto-tuning for aerial manipulators |
| title_unstemmed | Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators |
| topic | Artificial Intelligence, Computer Science Applications, Software |
| url | http://dx.doi.org/10.1177/1729881419828071 |