Samoučeće modelsko prediktivno upravljanje zasnovano na slijedu skupova upravljivih stanja

master's thesis

• Title (english): Selflearning Model Predictive Control Based on the Sequence of Controllable Sets
• Author: Luka Petrović
• Mentor: Jadranko Matuško (mentor)
• Committee member: Jadranko Matuško (predsjednik povjerenstva)
• Committee member: Šandor Ileš (član povjerenstva)
• Committee member: Marija Seder (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2017-07-20, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Electrical Engineering

Abstract

U radu je opisan koncept modelskog prediktivnog upravljanja zasnovanog na slijedu skupova upravljivih stanja na jediničnom horizontu. Opisani postupak primjenjiv je na sustave s parametarskom nesigurnošću, ali se integracijom postupaka učenja nepoznatih parametara sustava poboljšavaju performanse upravljanja, a da se pri tome ne kompromitira zadovoljenje ograničenja sustava. Razvijeni postupak samoučećeg prediktivnog upravljanja provjeren je simulacijski kao i eksperimentalno na labora- torijskom sustavu rotacijskog inverznog njihala.

Abstract (english)

In this thesis the concept of model predictive control based on the sequence of one-step controllable sets is described. The described approach is applicable to the systems with parametric uncertainties, but with integration of a parameter learning algorithms control performance can be improved, while at the same time having all the system constraints satisfied. For parameter learning the constrained least squares method is used. The developed self-learning algorithm is validated through simulation as well as experimentally on a laboratory rotary inverted pendulum system.

• Keywords: modelsko prediktivno upravljanje; MPC; LPV sustavi; skupovi upravljivih stanja; estimacija parametara; metoda najmanjih kvadrata
• Keywords (english): model predictive control; MPC; LPV systems; control invariant sets; parameter learning; constrained least squares
• Language: english
• URN:NBN: urn:nbn:hr:168:329189
• Study programme: Title: 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 resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2019-03-13 23:56:34