Motion Control Of Robot Manipulators Research Articles

Compliant motion control for robot manipulators

Compliant motion control for robot manipulators

Motion Control of Robotic Manipulator Based on Motor Program Learning

A paradigm of learning motion control for robotic manipulators is proposed, which can be referred to a mathematical modelling of learning and generation of motor programs in the central nervous system. It differs from conventional classical and modern control techniques. It stands for the repeatability of operating a given objective system and the possibility of improving the command input on the basis of previous actual operation data. Hence, adequate conditions on the repeatability and invariance of robot dynamics are assumed, but any precise description on the dynamics is not required. It is shown that a better performance is realized at every attempt of operation of the robot if the input command is updated by a simple learning law, provided that a desired motion is given a-priori and the actual motion can be measured at every operation. In addition, a novel idea of the use of knowledge acquired already by learning is presented. According to this, if a certain set of several input command signals is in advance obtained by the learning scheme, any other new desired motion can be approximately realized by a combination of those input signals without iterating operarion of the robot.

Implementation of Adaptive Techniques for Motion Control of Robotic Manipulators

This paper is concerned with the digital implementation and experimental evaluation of two adaptive controllers for robotic manipulators. The first is a continuous time model reference adaptive controller, and the second is a discrete time adaptive controller. The primary purpose of these adaptive controllers is to compensate for inertial variations due to changes in configuration and payload, as well as disturbances, such as Coulomb friction and/or gravitational forces. Experimental results are obtained from a laboratory test stand, which emulates an one-axis direct drive robot arm with variable inertia, as well as a Toshiba TSR-500V industrial robot. Experimental results from the test stand indicate that these adaptive control schemes are promising for the control of direct drive robot arms. Friction forces arising from the harmonic gear of the Toshiba robot were detrimental if not properly compensated. Because of a high gearing ratio, the advantage of adaptive control for the Toshiba arm could be shown only by detuning the controller.

Robust linear compensator design for nonlinear robotic control

The motion control of robotic manipulators is investigated using a recently developed approach to linear multivariable control known as the stable factorization approach. Given a nominal model of the manipulator dynamics, the control scheme consists of an approximate feedback linearizing control followed by a linear compensator design based on the stable factorization approach. Using a multiloop version of the small gain theorem, robust trajectory tracking is shown under the assumption that the deviation of the model from the true system satisfies certain norm inequalities. In turn, these norm inequalities lead to quantifiable bounds on the tracking error.