Abstract
In this study, trajectory tracking fuzzy logic controller (TTFLC) is proposed for the speed control of a pneumatic motor (PM). A third order trajectory is defined to determine the trajectory function that has to be tracked by the PM speed. Genetic algorithm (GA) is used to find the TTFLC boundary values of membership functions (MF) and weights of control rules. In addition, artificial neural networks (ANN) modelled dynamic behaviour of PM is given. This ANN model is used to find the optimal TTFLC parameters by offline GA approach. The experimental results show that designed TTFLC successfully enables the PM speed track the given trajectory under various working conditions. The proposed approach is superior to PID controller. It also provides simple and easy design procedure for the PM speed control problem.
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References
Anonymous 1999 Air-motor advantage. Machine design, Nov 18, 124
Baba A F 2004 Fuzzy logic controller. Nuclear Eng. Inter. 49: 36–38
Bäck T 1993 Optimal mutation rates in genetic search. Proceeding of the Fifth International Conference on Genetic Algorithms (CL: Morgan Kaufmann)
Chillari S, Guccione S, Muscato G2001 An experimental comparison between several pneumatic position control methods. Proceeding of the 40th IEEE Conference on Decision and Control Orlando FL
GAST Air Motors manual for 2AM series http://www.gastmfg.com
Hagan M T, Demuth H B, Beale M 1996 Neural network design (Boston, MA: PWS Publishing) 11·2–11·25
Herrera F, Lozano M, Verdegay J L 1994 Applying genetic algorithms in fuzzy optimization problems. Fuzzy Systems and A.I. Reports and Letters 3(1): 39–52
Jang J S R, Sun C T, Mizutani E 1997 Neuro-Fuzzy and Soft Computing (NJ: Prentice Hall) 82
Jihong W, Junsheng P, Philip R M, Zongmao Z 1998 Modelling study and servo-control of air motor systems. Int. J. Control 71(3): 459–476
Linkens D A, Nyongesa M 1995 Genetic algorithms for fuzzy control Part 1: Offline systems development and applications. IEE Proc. Control Theory Applications 142
Marumo R, Tokhi M O 2004 Neural-model reference control of an air motor. IEEE Africon 467
Marumo R, Tokhi M O 2004 Intelligent modelling and control of a pneumatic motor. CCECE 2004-CCGEI 2004, Niagara Falls
Pandian R S, Takemura F, Hayakawa Y, Kawamura S 1999 Control performance of an air motor. Proceeding of the 1999 IEEE International Conference on Robotics and Automation, Detroit, MI 518–524
Ronco E, Gawthrop P J 1997 Neural networks for modelling and control. Centre for System and Control Department of Mechanical Engineering University of Glasgow, Technical Report csc97008
Ruihua L, Weixiang S, Qingyu Y 2004 Multi-region fuzzy tracking control for a pneumatic servo squeezing forces system. Proceedings of the 5 World Congress on Intelligent Control and Automation, June 15–19, Hangzhou, P.R. China
Shibata S, Jindai M, Shimizu A 2000 Neuro-Fuzzy control for pneumatic servo system. IECON 2000 26th Annual Conference of the IEEE 3: 1761–1766
Zhang G, Patuwo B E, Hu MY 1998 Forecasting with artificial neural networks: The state-of-the-art. Int. J. Forecasting 14: 35–62
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Safak, C., Topuz, V. & Fevzi Baba, A. Pneumatic motor speed control by trajectory tracking fuzzy logic controller. Sadhana 35, 75–86 (2010). https://doi.org/10.1007/s12046-010-0007-z
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DOI: https://doi.org/10.1007/s12046-010-0007-z