System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs
Accurate trajectory tracking is a critical property of unmanned aerial vehicles (UAVs) due to system nonlinearities, under-actuated properties and constraints. Specifically, the use of unmanned rotorcrafts with accuracy trajectory tracking controllers in dynamic environments has the potential to imp...
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| Format: | Article |
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MDPI AG
2022-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/13/4712 |
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| author | Luis F. Recalde Bryan S. Guevara Christian P. Carvajal Victor H. Andaluz José Varela-Aldás Daniel C. Gandolfo |
| author_facet | Luis F. Recalde Bryan S. Guevara Christian P. Carvajal Victor H. Andaluz José Varela-Aldás Daniel C. Gandolfo |
| author_sort | Luis F. Recalde |
| collection | DOAJ |
| description | Accurate trajectory tracking is a critical property of unmanned aerial vehicles (UAVs) due to system nonlinearities, under-actuated properties and constraints. Specifically, the use of unmanned rotorcrafts with accuracy trajectory tracking controllers in dynamic environments has the potential to improve the fields of environment monitoring, safety, search and rescue, border surveillance, geology and mining, agriculture industry, and traffic control. Monitoring operations in dynamic environments produce significant complications with respect to accuracy and obstacles in the surrounding environment and, in many cases, it is difficult to perform even with state-of-the-art controllers. This work presents a nonlinear model predictive control (NMPC) with collision avoidance for hexacopters’ trajectory tracking in dynamic environments, as well as shows a comparative study between the accuracies of the Euler–Lagrange formulation and the dynamic mode decomposition (DMD) models in order to find the precise representation of the system dynamics. The proposed controller includes limits on the maneuverability velocities, system dynamics, obstacles and the tracking error in the optimization control problem (OCP). In order to show the good performance of this control proposal, computational simulations and real experiments were carried out using a six rotary-wind unmanned aerial vehicle (hexacopter—DJI MATRICE 600). The experimental results prove the good performance of the predictive scheme and its ability to regenerate the optimal control policy. Simulation results expand the proposed controller in simulating highly dynamic environments that showing the scalability of the controller. |
| first_indexed | 2024-03-09T12:35:38Z |
| format | Article |
| id | doaj.art-250487173e194a46bfd209882cfe0cb7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:35:38Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-250487173e194a46bfd209882cfe0cb72023-11-30T22:24:38ZengMDPI AGSensors1424-82202022-06-012213471210.3390/s22134712System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVsLuis F. Recalde0Bryan S. Guevara1Christian P. Carvajal2Victor H. Andaluz3José Varela-Aldás4Daniel C. Gandolfo5SISAu Research Group, Facultad de Ingeniería y Tecnologías de la Información y Comunicación, Universidad Tecnológica Indoamérica, Ambato 180103, EcuadorInstituto de Automática, Universidad Nacional de San Juan-CONICET, Av. San Martín Oeste 1109, San Juan J5400ARL, ArgentinaInstituto de Automática, Universidad Nacional de San Juan-CONICET, Av. San Martín Oeste 1109, San Juan J5400ARL, ArgentinaDepartamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas–ESPE, Sangolquí 171103, EcuadorSISAu Research Group, Facultad de Ingeniería y Tecnologías de la Información y Comunicación, Universidad Tecnológica Indoamérica, Ambato 180103, EcuadorInstituto de Automática, Universidad Nacional de San Juan-CONICET, Av. San Martín Oeste 1109, San Juan J5400ARL, ArgentinaAccurate trajectory tracking is a critical property of unmanned aerial vehicles (UAVs) due to system nonlinearities, under-actuated properties and constraints. Specifically, the use of unmanned rotorcrafts with accuracy trajectory tracking controllers in dynamic environments has the potential to improve the fields of environment monitoring, safety, search and rescue, border surveillance, geology and mining, agriculture industry, and traffic control. Monitoring operations in dynamic environments produce significant complications with respect to accuracy and obstacles in the surrounding environment and, in many cases, it is difficult to perform even with state-of-the-art controllers. This work presents a nonlinear model predictive control (NMPC) with collision avoidance for hexacopters’ trajectory tracking in dynamic environments, as well as shows a comparative study between the accuracies of the Euler–Lagrange formulation and the dynamic mode decomposition (DMD) models in order to find the precise representation of the system dynamics. The proposed controller includes limits on the maneuverability velocities, system dynamics, obstacles and the tracking error in the optimization control problem (OCP). In order to show the good performance of this control proposal, computational simulations and real experiments were carried out using a six rotary-wind unmanned aerial vehicle (hexacopter—DJI MATRICE 600). The experimental results prove the good performance of the predictive scheme and its ability to regenerate the optimal control policy. Simulation results expand the proposed controller in simulating highly dynamic environments that showing the scalability of the controller.https://www.mdpi.com/1424-8220/22/13/4712system identificationmodel predictive controlobstacles avoidancehexacopter UAVsystem constraintsoptimization |
| spellingShingle | Luis F. Recalde Bryan S. Guevara Christian P. Carvajal Victor H. Andaluz José Varela-Aldás Daniel C. Gandolfo System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs Sensors system identification model predictive control obstacles avoidance hexacopter UAV system constraints optimization |
| title | System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs |
| title_full | System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs |
| title_fullStr | System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs |
| title_full_unstemmed | System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs |
| title_short | System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs |
| title_sort | system identification and nonlinear model predictive control with collision avoidance applied in hexacopters uavs |
| topic | system identification model predictive control obstacles avoidance hexacopter UAV system constraints optimization |
| url | https://www.mdpi.com/1424-8220/22/13/4712 |
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