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

Filtry

20172

Nastavení

Autor: search.filters.author.Honc, DanielPředmět: search.filters.subject.model predictive controlPředmět: search.filters.subject.řízení

Search Results

Nyní se zobrazuje 1 - 2 z 2
  • Náhled
    Konferenční objektpeer-reviewedpostprintOtevřený přístup
    Predictive Control of Nonlinear Plant Using Piecewise-Linear Neural Model
    (IEEE (Institute of Electrical and Electronics Engineers), 2017) Honc, Daniel; Doležel, Petr; Gago, Lumír
    A special form of a predictive controller is presented in this paper. Based on previous authors' work, a piecewise-linear neural model of nonlinear plant to be controlled is adopted to local linearization. The linearized model is then used for control action evaluation using a predictive controller. Although the linearization using piecewise-linear neural network is simple and efficient, it provides the model in a nonstandard form. Therefore, the proposed predictive controller is designed in order to handle that nonstandard model without any customization. At the end of the paper, the illustrative example demonstrates the main features of the introduced solution.
  • Náhled
    Konferenční objektpeer-reviewedpostprintOtevřený přístup
    Optimal control with disturbance estimation
    (Nottingham Trent University, 2017) Dušek, František; Honc, Daniel; Kizhakke Illom, Rahul Sharma
    The paper deals with a very common situation in many control systems and this is the fact that, for zero control action, the controlled variable is nonzero. This is often caused by the existence of another process input which is uncontrolled. Classic controllers do not take into account the second input, so deviation variables are considered or some feedforward controller is used to compensate the variable. The authors propose a solution, that the process is considered as a system with two inputs and single output (TISO). Here, the uncontrolled input is estimated with the state observer and the controller is designed as the multivariable controller. A Linear-quadratic (LQ) state-feedback control and model predictive control (MPC) of simple thermal process simulations are provided to demonstrate the proposed control strategy.