PX4 Simulation Results of a Quadcopter with a Disturbance-Observer-Based and PSO-Optimized Sliding Mode Surface Controller
This work designed a disturbance-observer-based nonlinear sliding mode surface controller (SMC) and validated the controller using a simulated PX4-conducted quadcopter. To achieve this goal, this research (1) developed a dynamic mathematical model; (2) built a PX4-based simulated UAV following the m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/6/9/261 |
| _version_ | 1797489269508407296 |
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| author | Yutao Jing Xianghe Wang Juan Heredia-Juesas Charles Fortner Christopher Giacomo Rifat Sipahi Jose Martinez-Lorenzo |
| author_facet | Yutao Jing Xianghe Wang Juan Heredia-Juesas Charles Fortner Christopher Giacomo Rifat Sipahi Jose Martinez-Lorenzo |
| author_sort | Yutao Jing |
| collection | DOAJ |
| description | This work designed a disturbance-observer-based nonlinear sliding mode surface controller (SMC) and validated the controller using a simulated PX4-conducted quadcopter. To achieve this goal, this research (1) developed a dynamic mathematical model; (2) built a PX4-based simulated UAV following the model-based design process; (3) developed appropriate sliding mode control laws for each degree of freedom; (4) implemented disturbance observers on the proposed SMC controller to achieve finer disturbance rejection such as crosswind effect and other mutational disturbances; (5) optimized the SMC controller’s parameters based on particle swarm optimization (PSO) method; and (6) evaluated and compared the quadcopter’s tracking performance under a range of noise and disturbances. Comparisons of PID control strategies against the SMC were documented under the same conditions. Consequently, the SMC controller with disturbance observer facilitates accurate and fast UAV adaptation in uncertain dynamic environments. |
| first_indexed | 2024-03-10T00:14:08Z |
| format | Article |
| id | doaj.art-827f74474a6144b788ac3412ad9e30d3 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-10T00:14:08Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-827f74474a6144b788ac3412ad9e30d32023-11-23T15:54:06ZengMDPI AGDrones2504-446X2022-09-016926110.3390/drones6090261PX4 Simulation Results of a Quadcopter with a Disturbance-Observer-Based and PSO-Optimized Sliding Mode Surface ControllerYutao Jing0Xianghe Wang1Juan Heredia-Juesas2Charles Fortner3Christopher Giacomo4Rifat Sipahi5Jose Martinez-Lorenzo6Department of Mechanical & Industrial Engineering, Northeastern University, Boston, MA 02115, USAConducted Research While at Department of Mechanical & Industrial Engineering, Northeastern University, Boston, MA 02115, USAConducted Research While at Department of Mechanical & Industrial Engineering, Northeastern University, Boston, MA 02115, USAConducted Research While at Department of Mechanical & Industrial Engineering, Northeastern University, Boston, MA 02115, USAAir Force Research Laboratory, Rome, NY 13441, USADepartment of Mechanical & Industrial Engineering, Northeastern University, Boston, MA 02115, USADepartment of Mechanical & Industrial Engineering, Northeastern University, Boston, MA 02115, USAThis work designed a disturbance-observer-based nonlinear sliding mode surface controller (SMC) and validated the controller using a simulated PX4-conducted quadcopter. To achieve this goal, this research (1) developed a dynamic mathematical model; (2) built a PX4-based simulated UAV following the model-based design process; (3) developed appropriate sliding mode control laws for each degree of freedom; (4) implemented disturbance observers on the proposed SMC controller to achieve finer disturbance rejection such as crosswind effect and other mutational disturbances; (5) optimized the SMC controller’s parameters based on particle swarm optimization (PSO) method; and (6) evaluated and compared the quadcopter’s tracking performance under a range of noise and disturbances. Comparisons of PID control strategies against the SMC were documented under the same conditions. Consequently, the SMC controller with disturbance observer facilitates accurate and fast UAV adaptation in uncertain dynamic environments.https://www.mdpi.com/2504-446X/6/9/261sliding-mode surface controldisturbance observernonlinear controlparticle-swarm optimizationPX4simulation |
| spellingShingle | Yutao Jing Xianghe Wang Juan Heredia-Juesas Charles Fortner Christopher Giacomo Rifat Sipahi Jose Martinez-Lorenzo PX4 Simulation Results of a Quadcopter with a Disturbance-Observer-Based and PSO-Optimized Sliding Mode Surface Controller Drones sliding-mode surface control disturbance observer nonlinear control particle-swarm optimization PX4 simulation |
| title | PX4 Simulation Results of a Quadcopter with a Disturbance-Observer-Based and PSO-Optimized Sliding Mode Surface Controller |
| title_full | PX4 Simulation Results of a Quadcopter with a Disturbance-Observer-Based and PSO-Optimized Sliding Mode Surface Controller |
| title_fullStr | PX4 Simulation Results of a Quadcopter with a Disturbance-Observer-Based and PSO-Optimized Sliding Mode Surface Controller |
| title_full_unstemmed | PX4 Simulation Results of a Quadcopter with a Disturbance-Observer-Based and PSO-Optimized Sliding Mode Surface Controller |
| title_short | PX4 Simulation Results of a Quadcopter with a Disturbance-Observer-Based and PSO-Optimized Sliding Mode Surface Controller |
| title_sort | px4 simulation results of a quadcopter with a disturbance observer based and pso optimized sliding mode surface controller |
| topic | sliding-mode surface control disturbance observer nonlinear control particle-swarm optimization PX4 simulation |
| url | https://www.mdpi.com/2504-446X/6/9/261 |
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