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.Kizhakke Illom, Rahul SharmaPředmět: search.filters.subject.prediktivní řízení

Search Results

Nyní se zobrazuje 1 - 2 z 2
  • Náhled
    Konferenční objektpeer-reviewedpostprintOtevřený přístup
    Comparitive study of predictive controllers for trajectory tracking of non-holonomic mobile robot
    (IEEE (Institute of Electrical and Electronics Engineers), 2017) Kizhakke Illom, Rahul Sharma; Dušek, František; Honc, Daniel
    The paper deals with predictive control of non-holonomic mobile robot. The basic nonlinear kinematic equation is linearized into two different linear time varying models based on frame of reference-world coordinates and local coordinate of mobile robot. The non-linear model predictive control is applied to the trajectory tracking problem of a non-holonomic mobile robot with these models. The control law is derived from a cost function which penalizes the state tracking error, control effort and terminal state deviation error. Various simulation experiments are conducted and a comparative analysis has been made with respect to state-of-the-art approaches.
  • 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.