prikaz prve stranice dokumenta Nadogradnja modelskog prediktivnog upravljanja bespilotnom letjelicom MORUS
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master's thesis
Nadogradnja modelskog prediktivnog upravljanja bespilotnom letjelicom MORUS
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:997862
Radoš, Branko
University of Zagreb
Faculty of Electrical Engineering and Computing

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Cite this document

Radoš, B. (2019). Nadogradnja modelskog prediktivnog upravljanja bespilotnom letjelicom MORUS (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:997862

Radoš, Branko. "Nadogradnja modelskog prediktivnog upravljanja bespilotnom letjelicom MORUS." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:997862

Radoš, Branko. "Nadogradnja modelskog prediktivnog upravljanja bespilotnom letjelicom MORUS." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:997862

Radoš, B. (2019). 'Nadogradnja modelskog prediktivnog upravljanja bespilotnom letjelicom MORUS', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 17 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:997862

Radoš B. Nadogradnja modelskog prediktivnog upravljanja bespilotnom letjelicom MORUS [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 July 17] Available at: https://urn.nsk.hr/urn:nbn:hr:168:997862

B. Radoš, "Nadogradnja modelskog prediktivnog upravljanja bespilotnom letjelicom MORUS", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:997862

Metadata
TitleNadogradnja modelskog prediktivnog upravljanja bespilotnom letjelicom MORUS
Title (english)Extension of Model Predictive Ccontroller for MORUS Unamnned Aerial Vehicle
AuthorBranko Radoš
MentorStjepan Bogdan (mentor)
Committee memberStjepan Bogdan (predsjednik povjerenstva)
Committee memberZdenko Kovačić (član povjerenstva)
Committee memberTamara Petrović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2019-07-18, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Abstract
Glavni cilj ovog rada je implementirati "racionalno" korištenje električne energije za upravljane pomičnim masama MORUS letjelice kako bi uspješno proveli MORUS projekta. U tu svrhu implementiran je MPC kontroler koji će izvući maksimum iz raspoloživih sredstava. Analizom same letjelice određeni su karakteristični parametri letjelice. Jedno od krucijalnih je malo vrijeme diskretizacije koje je postalo nepremostiva prepreka dijelu algoritama optimizacije. Iz tog razloga modificirani su algoritmi učenja neuronskim mreža kako bi se mogli iskoristiti kao MPC optimizatori. Jedan od najtežih dijelova implementacije MPC je pronalazak "optimalnih" upravljačkih matrica. U tu svrhu iskorišteni su GA i PSO algoritmi. U radu su implementirani linearni i nelinearni MPC regulatori za kuteve valjanja, skretanja i poniranja zajedno s kontrolom po x,y i z osi. Na samom kraju implementiran je linearni MPC linearne brzine u x/y osi bespilotne letjelice s nagnutim rotorima. Za bilo kakvu složeniju misiju potreban nam je planer (globalni i lokalni). U tu svrhu implementiran je lokalni planer trajektorije iz znanstvenog članka. Kako bismo bili korak bliže stvarnom sustavu. MPC upravljanje je testirano u GAZEBO simulator. Kako bi se što više približili realnim uvjetima implementiran je GAZEBO plugin otpora zraka uz ostale potrebne dijelove kako bi se uspješno provela sama simulacija.
Abstract (english)
MPC is "powerful" controller if it is in hand of expert. Great power in hands of expert comes from huge number of DOF where its tiny details can be optimized or used for better control and we known that with big power comes big responsibility to justify use of "expensive" type of control. In this master thesis we process the problem of using electrical energy from battery "as little as possible" for control of moving masses and successfully accomplish desire mission of MORUS project. For this purpose we use MPC control to get the most out of it. For lack of MPC optimizer we modified algorithms for neural network so that we can use them for optimizing MPC cost function at every discrete time step. One of the hardest problem in MPC control is finding "optimal" control matrices. For this purpose we use GA and PSO algorithms to find "optimal" matrices for desire problem. In this thesis we implement linear and nonlinear MPC for roll, pitch, yaw as well as x, y and z axes control and compare pros and cons for using nonlinear versus linear MPC. At the very end we implement linear MPC for linear x/y velocity of non-tilting UAV. For executing any "complex" mission we need trajectory planner. For that purpose we implement local trajectory planner from article. To get one step closer to real system we implement simulation in GAZEBO environment. To create real conditions we implement drag GAZEBO plugin.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:997862
Study programmeTitle: Electrical Engineering and Information Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka elektrotehnike i informacijske tehnologije (magistar/magistra inženjer/inženjerka elektrotehnike i informacijske tehnologije)
Type of resourceText
File originBorn digital
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Created on2020-01-07 21:44:55