Artificial Intelligence Techniques Applied to Control and Identify Parameters in variable DOF Robotic Manipulators for Industrial Automation
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Abstract
An industrial robot manipulator must be able to translate its static collection of joint angles into position coordinates before calculating the end effect's appropriate positioning with forward kinematics.A robotic manipulator has now become an integral part of industrial automation, greatly reducing the need for human labour, increasing the accuracy of work as well as reducing the time required to accomplish tasks.Each rotation must be controlled using servomotor feedback. MATLAB® Robotics Toolbox is used to verify the kinematic model for the activation of three revolving joints.The purpose of this paper is to provide an overview of an end-effect-based DC servomotor platform with intelligent controller for five degrees of freedom (5-DOF). A DenavitHarterberg (DH) representation was used to model forward and inverse kinematics. PID controllers with fuzzy logic are used to implement various blurring strategies. Simulated results from MATLAB show that PID produces better transient parameters than a conventional PID.Compared to the Fuzzy logic controller, PID has a better overshoot performance than FLC and both controllers can achieve the desired output when subjected to steady state responses, although FLC outperforms PID.