Nonlinear PID-based Analog Neural Network Control for a Two Link Rigid Robot Manipulator and Determining the Maximum Load Carrying Capacity
Hadi Razmi1, Atabak Mashhadi Kashtiban2
1Hadi Razmi, Department of Engineering, East Tehran Branch, Islamic Azad University, Tehran, Iran.
2Atabak Mashhadi Kashtiban, Department of Engineering, Khameneh Branch, Islamic Azad University, Tabriz, Iran.
Manuscript received on February 15, 2012. | Revised Manuscript received on February 20, 2012. | Manuscript published on March 05, 2012. | PP: 228-234 | Volume-2 Issue-1, March 2012. | Retrieval Number: A0424022112/2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: An adaptive controller of nonlinear PID-based analog neural networks is developed for the point to point and orientation-tracking control of a two link rigid robot manipulator. In each case, the maximum load carrying capacity of robot manipulator subject to accuracy and actuators constraints is obtained. In comparison with conventional PID method, the use of neural network controller can increase maximum load carrying capacity of robot manipulators. A superb mixture of a conventional PID controller and a neural network, which has powerful capability of continuously online learning, adaptation and tackling nonlinearity, brings us the novel nonlinear PID-based analog neural network controller. Computer simulations were carried out in two axes manipulator and the effectiveness of the proposed control algorithm was demonstrated through the experiments, which suggests its superior performance and increasing the maximum load carrying capacity of this manipulator.
Keywords: Analog neural network, Adaptive control, Maximum load carrying capacity, Nonlinear PID control.