000118735 001__ 118735
000118735 005__ 20240319081024.0
000118735 0247_ $$2doi$$a10.3390/s22134712
000118735 0248_ $$2sideral$$a129675
000118735 037__ $$aART-2022-129675
000118735 041__ $$aeng
000118735 100__ $$aRecalde, Luis F.
000118735 245__ $$aSystem identification and nonlinear model predictive control with collision avoidance applied in Hexacopters UAVs
000118735 260__ $$c2022
000118735 5060_ $$aAccess copy available to the general public$$fUnrestricted
000118735 5203_ $$aAccurate 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.
000118735 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000118735 590__ $$a3.9$$b2022
000118735 592__ $$a0.764$$b2022
000118735 591__ $$aCHEMISTRY, ANALYTICAL$$b26 / 86 = 0.302$$c2022$$dQ2$$eT1
000118735 593__ $$aInstrumentation$$c2022$$dQ1
000118735 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b19 / 63 = 0.302$$c2022$$dQ2$$eT1
000118735 593__ $$aAnalytical Chemistry$$c2022$$dQ1
000118735 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b100 / 274 = 0.365$$c2022$$dQ2$$eT2
000118735 593__ $$aMedicine (miscellaneous)$$c2022$$dQ2
000118735 593__ $$aInformation Systems$$c2022$$dQ2
000118735 593__ $$aBiochemistry$$c2022$$dQ2
000118735 593__ $$aAtomic and Molecular Physics, and Optics$$c2022$$dQ2
000118735 593__ $$aElectrical and Electronic Engineering$$c2022$$dQ2
000118735 594__ $$a6.8$$b2022
000118735 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000118735 700__ $$aGuevara, Bryan S.
000118735 700__ $$aCarvajal, Christian P.
000118735 700__ $$aAndaluz, Víctor H.
000118735 700__ $$aVarela Aldás, José
000118735 700__ $$aGandolfo, Daniel C.
000118735 773__ $$g22, 13 (2022), 4712 [29 pp.]$$pSensors$$tSensors$$x1424-8220
000118735 8564_ $$s6674002$$uhttps://zaguan.unizar.es/record/118735/files/texto_completo.pdf$$yVersión publicada
000118735 8564_ $$s2736125$$uhttps://zaguan.unizar.es/record/118735/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000118735 909CO $$ooai:zaguan.unizar.es:118735$$particulos$$pdriver
000118735 951__ $$a2024-03-18-16:30:28
000118735 980__ $$aARTICLE