Constrained relations between two coordinated industrial robots for motion control
International Journal of Robotics Research
Neural networks for control
Introduction to artificial neural systems
Introduction to artificial neural systems
Object handling using two arms without grasping
International Journal of Robotics Research
Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
Impedance Control with On-Line Neural Network Compensator for Dual-Arm Robots
Journal of Intelligent and Robotic Systems
Impedance Control with On-Line Neural Network Compensator for Dual-Arm Robots
Journal of Intelligent and Robotic Systems
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A controller design strategy of dual-arm robots is proposed in this paper. The controller consists of a central controller and three force controllers. The central controller is used to calculate each arm’s force command according to the desired object motion. A force controller is used in each arm to track the commanding force. Another force controller is used to track the desired contact force between the manipulated object and its environment. The force controller can be partitioned into three parts. The computed torque method is used to linearize and decouple the dynamics of a manipulator. An impedance controller is then added to regulate the mechanical impedance between the manipulator and its environment. In order to track a reference force signal, an on-line neural network is used to compensate the effect of unknown parameters of the manipulator and environment. The simulation results are reported to show the performance of the neural network compensator.