The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform

P.h. Meckl - One of the best experts on this subject based on the ideXlab platform.

  • Intelligent feedforward control and Payload estimation for a two-link robotic manipulator
    IEEE ASME Transactions on Mechatronics, 2003
    Co-Authors: P.h. Meckl
    Abstract:

    Conventional model-based computed torque control fails to produce a good trajectory tracking performance in the presence of Payload uncertainty and modeling error. The challenge is to provide accurate dynamics information to the controller. A new control architecture that incorporates a neural-network, fuzzy logic and a simple proportional-derivative (PD) controller is proposed to control an articulated robot carrying a variable Payload. An off-line trained feedforward (multilayer) neural network takes Payload Mass estimates from a fuzzy-logic Mass estimator as one of the inputs to represent the inverse dynamics of the articulated robot. The effectiveness of the proposed architecture is demonstrated by experiment on a two-link planar manipulator with changing Payload Mass. Experimental results show that this control architecture achieves excellent tracking performance in the presence of Payload uncertainty.

  • Experimental implementation of neural network controller for robot undergoing large Payload changes
    [1993] Proceedings IEEE International Conference on Robotics and Automation, 1993
    Co-Authors: J.d. Yegerlehner, P.h. Meckl
    Abstract:

    A robot controller based on artificial neural networks (ANNs) is presented which is capable of compensating for changing Payload Masses. Two different feedforward (multilayer) neural networks are used to generate the inverse dynamics and to estimate the Payload Mass of a two-link planar manipulator. The inverse dynamics ANN receives the same input signals as a conventional computed torque controller as well as the Payload Mass estimate. By using a separate ANN to generate the Payload Mass estimate, both ANNs can be trained off-line. The proposed neural network architecture is implemented on actual hardware using a neurocomputer. Experimental results indicate that the ANN-based controller is able to capture the nonlinear dynamics of the actual manipulator. The ANN Mass estimator responds very quickly to changing Payloads.

  • ICRA (2) - Experimental implementation of neural network controller for robot undergoing large Payload changes
    [1993] Proceedings IEEE International Conference on Robotics and Automation, 1993
    Co-Authors: J.d. Yegerlehner, P.h. Meckl
    Abstract:

    A robot controller based on artificial neural networks (ANNs) is presented which is capable of compensating for changing Payload Masses. Two different feedforward (multilayer) neural networks are used to generate the inverse dynamics and to estimate the Payload Mass of a two-link planar manipulator. The inverse dynamics ANN receives the same input signals as a conventional computed torque controller as well as the Payload Mass estimate. By using a separate ANN to generate the Payload Mass estimate, both ANNs can be trained off-line. The proposed neural network architecture is implemented on actual hardware using a neurocomputer. Experimental results indicate that the ANN-based controller is able to capture the nonlinear dynamics of the actual manipulator. The ANN Mass estimator responds very quickly to changing Payloads. >

Aaron M. Dollar - One of the best experts on this subject based on the ideXlab platform.

  • Stability of small-scale UAV helicopters and quadrotors with added Payload Mass under PID control
    Autonomous Robots, 2012
    Co-Authors: Paul E. I. Pounds, Daniel R. Bersak, Aaron M. Dollar
    Abstract:

    The application of rotorcraft to autonomous load carrying and transport is a new frontier for Unmanned Aerial Vehicles (UAVs). This task requires that hovering vehicles remain stable and balanced in flight as Payload Mass is added to the vehicle. If Payload is not loaded centered or the vehicle properly trimmed for offset loads, the robot will experience bias forces that must be rejected. In this paper, we explore the effect of dynamic load disturbances introduced by instantaneously increased Payload Mass and how those affect helicopters and quadrotors under Proportional-Integral-Derivative flight control. We determine stability bounds within which the changing Mass-inertia parameters of the system due to the acquired object will not destabilize these aircraft with this standard flight controller. Additionally, we demonstrate experimentally the stability behavior of a helicopter undergoing a range of instantaneous step Payload changes.

G.r. Heppler - One of the best experts on this subject based on the ideXlab platform.

Dennis S. Bernstein - One of the best experts on this subject based on the ideXlab platform.

N.e. Cotter - One of the best experts on this subject based on the ideXlab platform.

  • Neural network robotic control of unknown Mass Payloads
    IJCNN-91-Seattle International Joint Conference on Neural Networks, 1991
    Co-Authors: M.k. Branson, N.e. Cotter
    Abstract:

    Summary form only given. The authors discuss a neural-network-based controller suitable for position control of a one-degree-of-freedom robotic manipulator with unknown Mass Payload. A feedforward neural network (NN) is utilized to learn the dynamics of the manipulator and provide a drive signal, based on NN inputs, to a proportional-derivative controller used to stabilize the plant. The NN estimates the Payload Mass implicitly using readily available state information during training and operation. Computer simulations are used to assess the NN controller performance and to compare the performance to that of the linear controller. Specifically, the NN controller is trained on one trajectory for three different Payload Masses using the measured actuator torque at a given state as an estimation of the Payload Mass for input to the neural network.