Real Time Collision Detection and Identification for Robotic Manipulators

Discrete-time repetitive optimal control: Robotic manipulators

This paper proposes a discrete-time repetitive optimal control of electrically driven robotic manipulators using an uncertainty estimator. The proposed control method can be used for performing repetitive motion, which covers many industrial applications of robotic manipulators. This kind of control law is in the class of torque-based control in which the joint torques are generated by permanen...

GMDH Type Neural Networks and Their Application to the Identification of the Inverse Kinematics Equations of Robotic Manipulators (RESEARCH NOTE)

Optimal discrete-time control of robot manipulators in repetitive tasks

Optimal discrete-time control of linear systems has been presented already. There are some difficulties to design an optimal discrete-time control of robot manipulator since the robot manipulator is highly nonlinear and uncertain. This paper presents a novel robust optimal discrete-time control of electrically driven robot manipulators for performing repetitive tasks. The robot performs repetit...

Automatic formulation of falling multiple flexible-link robotic manipulators using 3×3 rotational matrices

In this paper, the effect of normal impact on the mathematical modeling of flexible multiple links is investigated. The response of such a system can be fully determined by two distinct solution procedures. Highly nonlinear differential equations are exploited to model the falling phase of the system prior to normal impact; and algebraic equations are used to model the normal collision of this ...

Neural Network Sensitivity to Inputs and Weights and its Application to Functional Identification of Robotics Manipulators

Neural networks are applied to the system identification problems using adaptive algorithms for either parameter or functional estimation of dynamic systems. In this paper the neural networks' sensitivity to input values and connections' weights, is studied. The Reduction-Sigmoid-Amplification (RSA) neurons are introduced and four different models of neural network architecture are proposed and...