Computational intelligence method for optimal rotary design system

• Main Author: P.Saminathan, Kantan
• Format: Thesis
• Language: English; English; English
• Published: 2008
• Subjects: QA Mathematics; QA76 Computer software
• Online Access: http://eprints.uthm.edu.my/7334/1/24p%20KANTAN%20P.SAMINATHAN.pdf; http://eprints.uthm.edu.my/7334/2/KANTAN%20P.SAMINATHAN%20COPYRIGHT%20DECLARATION.pdf; http://eprints.uthm.edu.my/7334/3/KANTAN%20P.SAMINATHAN%20WATERMARK.pdf; http://eprints.uthm.edu.my/7334/

The application of computational intelligence techniques to the field of industrial robot control is discussed. The core ideas behind using computation, evolutionary computation and fuzzy logic techniques are presented, along with a selection of specific real-world applications. The practical advantages and disadvantages relative to more traditional approaches are made clear. The objective of this project was to investigate and compare different algorithms for the calculation of velocity from position information. The best algorithm was applied to a small robot arm system which consists of a controller (PC software), analog-to-digital and digital-to-analog converter PC card, power amplifier, DC motor, gear train and external load. Generally in robotic systems a velocity calculation is difficult or impossible to implement because of noise. Here in the project, fuzzy logic will be used to filter the noise from the position data before calculating velocity. The purpose of this research is to design fuzzy logic feedback controller to position the rotational system with one flexible joint. The system produces oscillations that need to be dampen. Here the PD (without) controller, ON-OFF controller, Linear Quadratic Regulator controller (LQR) and Fuzzy Logic controller (sugeno method) are being used to solve the mentioned oscillatory problem. In order to control the overall Rotary Flexible Joint System, the Fuzzy Logic controller (FLC) is designed base upon the coefficients of the existing LQR controller. Comparison between four controllers was being made through simulation and experiment and the results showed that the fuzzy controller performed better than the other controllers.