Abstract
Forward kinematics (FK) mapping for parallel mechanisms is difficult as it involves highly coupled nonlinear motions. Since FK is a key element in closed loop position and force control of parallel manipulators, an accurate prediction of end pose from the available link lengths is essential. This paper proposes a modified Sugeno Inference System (SIS) to accurately predict the forward mapping between joint and Cartesian coordinates. The SIS algorithm has been implemented on a new parallel robotic device, designed for flexible ankle rehabilitation treatments. To increase the accuracy of the SIS, antecedent fuzzy variables are tuned using a modified genetic algorithm and consequent crisp membership functions are trained with a gradient descent algorithm. It has been found that the proposed system provides better accuracy and is also time efficient, which makes it a better candidate for real time control applications.
Original language | English |
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Pages | 236-241 |
Number of pages | 6 |
Publication status | Published - Dec 1 2008 |
Event | 39th International Symposium on Robotics, ISR 2008 - Seoul, Korea, Republic of Duration: Oct 15 2008 → Oct 17 2008 |
Other
Other | 39th International Symposium on Robotics, ISR 2008 |
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Country | Korea, Republic of |
City | Seoul |
Period | 10/15/08 → 10/17/08 |
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction
- Software