Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "manipulators" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-3 z 3
    Tytuł:
    Stiffness control of robot manipulators in the operational space using fuzzy mapping of dynamic functions
    Autorzy:
    Bertol, D. W.
    Barasuol, V.
    Martins, N. A.
    De Pieri, E. R.
    Powiązania:
    https://bibliotekanauki.pl/articles/971046.pdf
    Data publikacji:
    2013
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    stiffness control
    fuzzy mapping
    robot manipulators
    operational space
    trajectory tracking
    Opis:
    In this paper a stiffness control strategy based on the fuzzy mapped nonlinear terms of the robot manipulator dynamic model is proposed. The proposed stiffness controller is evaluated on a research robot manipulator performing a task in the operational space. Tests attempted to achieve fast motion with reasonable accuracy associated with lower computational load compared to the non-fuzzy approach. The stability analysis is presented to conclude about the mapping error influence and to obtain precondition criteria for the gains adjustment to face the trajectory tracking problem. Simulation results that supported the implementation are presented, followed by experiments and results obtained. These tests are conducted on a robot manipulator with SCARA configuration to illustrate the feasibility of this strategy.
    Źródło:
    Control and Cybernetics; 2013, 42, 3; 639-661
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Constrained Output Iterative Learning Control
    Autorzy:
    Yovchev, Kaloyan
    Delchev, Kamen
    Krastev, Evgeniy
    Powiązania:
    https://bibliotekanauki.pl/articles/229181.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    constrained output systems
    convergence analysis
    iterative learning control
    robot manipulators
    Opis:
    Iterative Learning Control (ILC) is a well-known method for control of systems performing repetitive jobs with high precision. This paper presents Constrained Output ILC (COILC) for non-linear state space constrained systems. In the existing literature there is no general solution for applying ILC to such systems. This novel method is based on the Bounded Error Algorithm (BEA) and resolves the transient growth error problem, which is a major obstacle in applying ILC to non-linear systems. Another advantage of COILC is that this method can be applied to constrained output systems. Unlike other ILC methods the COILC method employs an algorithm that stops the iteration before the occurrence of a violation in any of the state space constraints. This way COILC resolves both the hard constraints in the non-linear state space and the transient growth problem. The convergence of the proposed numerical procedure is proved in this paper. The performance of the method is evaluated through a computer simulation and the obtained results are compared to the BEA method for controlling non-linear systems. The numerical experiments demonstrate that COILC is more computationally effective and provides better overall performance. The robustness and convergence of the method make it suitable for solving constrained state space problems of non-linear systems in robotics.
    Źródło:
    Archives of Control Sciences; 2020, 30, 1; 157-176
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Iterative learning control with sampled-data feedback for robot manipulators
    Autorzy:
    Delchev, K.
    Boiadjiev, G.
    Kawasaki, H.
    Mouri, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/229323.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    sampled-data systems
    iterative learning control
    robot manipulators
    convergence analysis
    Opis:
    This paper deals with the improvement of the stability of sampled-data (SD) feedback control for nonlinear multiple-input multiple-output time varying systems, such as robotic manipulators, by incorporating an off-line model based nonlinear iterative learning controller. The proposed scheme of nonlinear iterative learning control (NILC) with SD feedback is applicable to a large class of robots because the sampled-data feedback is required for model based feedback controllers, especially for robotic manipulators with complicated dynamics (6 or 7 DOF, or more), while the feedforward control from the off-line iterative learning controller should be assumed as a continuous one. The robustness and convergence of the proposed NILC law with SD feedback is proven, and the derived sufficient condition for convergence is the same as the condition for a NILC with a continuous feedback control input. With respect to the presented NILC algorithm applied to a virtual PUMA 560 robot, simulation results are presented in order to verify convergence and applicability of the proposed learning controller with SD feedback controller attached.
    Źródło:
    Archives of Control Sciences; 2014, 24, 3; 299-319
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies