Improvement of a Robotic Manipulator Model Based on Multivariate Residual Modeling

Abstract

A new method is presented for extending a dynamic model of a six degrees of freedom robotic manipulator. A non-linear multivariate calibration of input-output training data from several typical motion trajectories is carried out with the aim of predicting the model systematic output error at time (t + 1) from known input reference up till and including time (t). A new partial least squares regression (PLSR) based method, nominal PLSR with interactions was developed and used to handle, unmodelled non-linearities. The performance of the new method is compared with least squares (LS). Different crossvalidation schemes were compared in order to assess the sampling of the state space based on conventional trajectories. The method developed in the paper can be used as fault monitoring mechanism and early warning system for sensor failure. The results show that the suggested methods improves trajectory tracking performance of the robotic manipulator by extending the initial dynamic model of the manipulator.

Current trend in control of artificial intelligence for health robotic manipulator

“…Robots can be managed using integrated sensors or computer programs in the form of algorithms. Robot is an evolution of an individual algorithm consisting of an element from a collection of robotic manipulator segments (shape and joint boundaries) [78]- [80] Based on the results of research that has been carried out by researchers for the optimal design of robotic manipulators, it can be seen the evolution of artificial intelligence control algorithms for manipulator robots [81]. The evolution of algorithms for industrial robot design aims to minimize joint torque across a given trajectory.…”

Development of a spherical parallel manipulator for brain surgery applications: preliminary study on the dynamic analysis and verification

“…Case studies were carried out and evaluated on a 3R planar and PUMA 560 serial robot manipulators by considering their kinematic and dynamic constraints. Acquisition of system characteristics or behaviors during end effector trajectory tracking becomes superior by the derived model of manipulator kinematics and dynamics [3]. Several approaches [4] are available to characterize dynamics of robot manipulators as virtual work principle [5], Newton-Euler and Lagrange's formulations.…”