Digitální knihovnaUPCE
 

Fakulta elektrotechniky a informatiky / Faculty of Electrical Engineering and Informatics

Stálý URI pro tuto komunituhttps://hdl.handle.net/10195/3847

Práce obhájené před rokem 2008 jsou uloženy pouze v kolekci Vysokoškolské kvalifikační práce

Procházet

Autor: search.filters.author.Honc, DanielAutor: search.filters.author.Novotný, AlešPředmět: search.filters.subject.prediktivní řízení

Search Results

Nyní se zobrazuje 1 - 2 z 2
  • Náhled
    Konferenční objektpeer-reviewedpostprintOtevřený přístup
    Application of Model Predictive Controller to Magnetic Levitation
    (IEEE (Institute of Electrical and Electronics Engineers), 2023) Novotný, Aleš; Honc, Daniel
    Model Predictive Control (MPC) is an advanced process control method that is widely used for controlling both linear and under some modifications for non-linear systems. The aim of this work is to show a way how to apply MPC to a non-linear Magnetic Levitation System (MLS) and its capability of stabilization and closed-loop performance. This work is a continuation of the previous article where the laboratory plant CE 152 MLS was identified, and a non-linear model was designed. This paper proposes a control circuit consisting of linearized discretized non-linear MLS model, Extended Kalman Filter (EKF) algorithm for state estimation and linear MPC. The results are verified in simulation and real-world experiment.
  • Náhled
    Konferenční objektpeer-reviewedpostprint (accepted version)Omezený přístup
    Explanation of the predictive controller and the effect of its tuning on the control quality
    (Springer Nature Switzerland AG, 2022) Honc, Daniel; Novotný, Aleš; Kupka, Libor
    The Model Predictive Control (MPC) concept and its realization is explained together with some real-world laboratory application examples. Usually, future control errors and control action changes are penalized in the cost function (objective) by predictive controllers. The cost function can be seen as a function of future control actions utilizing the process model in a form of the predictor. A derivation of the predictors for the transfer function (external) and state-space (internal) model is indicated in the paper. An analytical solution to the given optimization problem is possible in an unconstrained case. A quadratic programming strategy must be used in case of the occurrence of the process constraints that should be respected by the controller. The authors apply both algorithms to two types of dynamical systems – to proportional (stable) system and to integrating (unstable) system and they demonstrate the influence of the penalization parameters (weights) in the cost function on the control quality. The authors aim to high-light the MPC strategy and its potential, and on the other hand, mention some bottlenecks and risks associated with model-optimisation-based methods.