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This paper proposes a neural network based self-organing control concept for a robotic manipulator. The end-effector position and orientation control loop is closed using visual data to generate the necessary manipulator control inputs. The objective is to move the end-effector to a place, where the manipulator can easily grip a given object. Instead of processing inverse kinematics, the nonlinear...
A new methodology using the theory of variable structure systems is developed for accurate tracking control of robotic manipulators. The proposed method, based on a sliding mode controller, produces a discontinuity control input so that the nonlinear interactions and influence caused by unknown parameter variations can be depressed whereas the resulting control law is simple and easy to apply to on-line...
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