Wyniki wyszukiwania - BazTech

1

Design of an adaptive fuzzy variable structure compensator for the nonholonomic mobile robot in trajectory tracking task

Begnini M., Bertol W., Martins N. A.

Control and Cybernetics

|

2018

|

Vol. 47, no. 3

239--275

EN

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.

2

Trajectory tracking of a wheeled mobile robot with uncertainties and disturbances: proposed adaptive neural control

Martins N. A., Alencar M., Lombardi W. C., Bertol D. W., Pieri E. R., Filho H. F.

Control and Cybernetics

|

2015

|

Vol. 44, no. 1

47--98

EN

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.

3

Stiffness control of robot manipulators in the operational space using fuzzy mapping of dynamic functions

Bertol D. W., Barasuol V., Martins N. A., De Pieri E. R.

Control and Cybernetics

|

2013

|

Vol. 42, no. 3

639--661

EN

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.