Quadrotor Trajectory Control Based on Energy-Optimal Reference Generator
Inspired by the limited battery life of multi-rotor unmanned aerial vehicles (UAVs), this research investigated hierarchical real-time control of UAVs with the generation of energy-optimal reference trajectories. The goal was to design a reference generator and controller based on optimal-control th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/8/1/29 |
| _version_ | 1827372457187606528 |
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| author | Domenico Bianchi Alessandro Borri Federico Cappuzzo Stefano Di Gennaro |
| author_facet | Domenico Bianchi Alessandro Borri Federico Cappuzzo Stefano Di Gennaro |
| author_sort | Domenico Bianchi |
| collection | DOAJ |
| description | Inspired by the limited battery life of multi-rotor unmanned aerial vehicles (UAVs), this research investigated hierarchical real-time control of UAVs with the generation of energy-optimal reference trajectories. The goal was to design a reference generator and controller based on optimal-control theory that would guarantee energy consumption close to optimal with lower computational cost. First, a least-squares-estimation-(LSE) algorithm identified the parameters of the UAV mathematical model. Then, by considering a precise electrical model for the brushless DC motors and rest-to-rest maneuvers, the extraction of clear rules to compute the optimal mission time and generate ’energetic trajectories’ was performed. These rules emerged from analyzing the optimal-control strategy results that minimized the consumption over many simulations. Afterward, a hierarchical controller tracked those desired energetic trajectories identified as sub-optimal. Numerical experiments compared the results regarding trajectory tracking, energy performance index, and battery state of charge (SOC). A co-simulation framework consisting of commercial software tools, Simcenter Amesim for the physical modeling of the UAV, and Matlab-Simulink executed numerical simulations of the implemented controller. |
| first_indexed | 2024-03-08T10:59:55Z |
| format | Article |
| id | doaj.art-33e7ba0ec02448e589f24ae237a3389b |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-08T10:59:55Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-33e7ba0ec02448e589f24ae237a3389b2024-01-26T16:06:13ZengMDPI AGDrones2504-446X2024-01-01812910.3390/drones8010029Quadrotor Trajectory Control Based on Energy-Optimal Reference GeneratorDomenico Bianchi0Alessandro Borri1Federico Cappuzzo2Stefano Di Gennaro3Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, Via Vetoio, Loc. Coppito, 67100 L’Aquila, ItalyCenter of Excellence DEWS, University of L’Aquila Via Vetoio, Loc. Coppito, 67100 L’Aquila, ItalySiemens Digital Industries Software, 69007 Lyon, FranceDepartment of Information Engineering, Computer Science and Mathematics, University of L’Aquila, Via Vetoio, Loc. Coppito, 67100 L’Aquila, ItalyInspired by the limited battery life of multi-rotor unmanned aerial vehicles (UAVs), this research investigated hierarchical real-time control of UAVs with the generation of energy-optimal reference trajectories. The goal was to design a reference generator and controller based on optimal-control theory that would guarantee energy consumption close to optimal with lower computational cost. First, a least-squares-estimation-(LSE) algorithm identified the parameters of the UAV mathematical model. Then, by considering a precise electrical model for the brushless DC motors and rest-to-rest maneuvers, the extraction of clear rules to compute the optimal mission time and generate ’energetic trajectories’ was performed. These rules emerged from analyzing the optimal-control strategy results that minimized the consumption over many simulations. Afterward, a hierarchical controller tracked those desired energetic trajectories identified as sub-optimal. Numerical experiments compared the results regarding trajectory tracking, energy performance index, and battery state of charge (SOC). A co-simulation framework consisting of commercial software tools, Simcenter Amesim for the physical modeling of the UAV, and Matlab-Simulink executed numerical simulations of the implemented controller.https://www.mdpi.com/2504-446X/8/1/29UAV controlenergetic reference generatoroptimal controlhierarchical controlenergy consumption |
| spellingShingle | Domenico Bianchi Alessandro Borri Federico Cappuzzo Stefano Di Gennaro Quadrotor Trajectory Control Based on Energy-Optimal Reference Generator Drones UAV control energetic reference generator optimal control hierarchical control energy consumption |
| title | Quadrotor Trajectory Control Based on Energy-Optimal Reference Generator |
| title_full | Quadrotor Trajectory Control Based on Energy-Optimal Reference Generator |
| title_fullStr | Quadrotor Trajectory Control Based on Energy-Optimal Reference Generator |
| title_full_unstemmed | Quadrotor Trajectory Control Based on Energy-Optimal Reference Generator |
| title_short | Quadrotor Trajectory Control Based on Energy-Optimal Reference Generator |
| title_sort | quadrotor trajectory control based on energy optimal reference generator |
| topic | UAV control energetic reference generator optimal control hierarchical control energy consumption |
| url | https://www.mdpi.com/2504-446X/8/1/29 |
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