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Wyszukujesz frazę "Martins, M." wg kryterium: Wszystkie pola

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    Wyświetlanie 1-2 z 2
    Tytuł:
    Design of an adaptive fuzzy variable structure compensator for the nonholonomic mobile robot in trajectory tracking task
    Autorzy:
    Begnini, M.
    Bertol, W.
    Martins, N. A.
    Powiązania:
    https://bibliotekanauki.pl/articles/971054.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    nonholonomic mobile robot
    trajectory tracking
    variable structure control
    uncertainties and disturbances
    fuzzy compensator
    Lyapunov theory
    Opis:
    A robust adaptive kinematic control strategy, based on the methodology of variable structure control is considered in this paper. Because the dynamics of mobile robots is subject to uncertainties and disturbances, a fuzzy compensator is adopted to estimate them. In order to minimize the tracking errors and to attenuate the chattering phenomenon, an adaptation law for the fuzzy compensator is obtained by Lyapunov stability theory so as to asymptotically stabilize the control system as well as guarantee the convergence of the tracking errors. In terms of comparison with the boundary layer variable structure controller, simulations and experiments verify the feasibility and effectiveness of the proposed kinematic control strategy for the nonholonomic mobile robots under the incidence of uncertainties and disturbances.
    Źródło:
    Control and Cybernetics; 2018, 47, 3; 239-275
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Trajectory tracking of a wheeled mobile robot with uncertainties and disturbances: proposed adaptive neural control
    Autorzy:
    Martins, N. A.
    Alencar, M.
    Lombardi, W. C.
    Bertol, D. W.
    Pieri, E. R.
    Filho, H. F.
    Powiązania:
    https://bibliotekanauki.pl/articles/971045.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    wheeled mobile robot
    trajectory tracking
    kinematic control
    variable structure control
    dynamic control
    sliding mode theory
    neural networks
    Lyapunov theory
    Opis:
    This paper analyses a trajectory tracking control problem for a wheeled mobile robot, Rusing integration of a kinematic neural controller (KNC) and a torque neural controller (TNC), in which both the kinematic and dynamic models contain uncertainties and disturbances. The proposed adaptive neural controller (PANC) is composed of the KNC and the TNC and is designed with use of a modeling technique of Gaussian radial basis function neural networks (RBFNNs). The KNC is a variable structure controller, based on the sliding mode theory and is applied to compensate for the disturbances of the wheeled mobile robot kinematics. The TNC is an inertia-based controller composed of a dynamic neural controller (DNC) and a robust neural compensator (RNC) applied to compensate for the wheeled mobile robot dynamics, bounded unknown disturbances, and neural network modeling errors. To minimize the problems found in practical implementations of the classical variable structure controllers (VSC) and sliding mode controllers (SMC), and to eliminate the chattering phenomenon, the nonlinear and continuous KNC and RNC of the TNC are applied in lieu of the discontinuous components of the control signals that are present in classical forms. Additionally, the PANC neither requires the knowledge of the wheeled mobile robot kinematics and dynamics nor the timeconsuming training process. Stability analysis, convergence of the tracking errors to zero, and the learning algorithms for the weights are guaranteed based on the Lyapunov method. Simulation results are provided to demonstrate the effectiveness of the proposed approach.
    Źródło:
    Control and Cybernetics; 2015, 44, 1; 47-98
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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