INTELLIGENT TRACKING CONTROL USING PSO-BASED INTERVAL TYPE-2 FUZZY LOGIC FOR A MIMO MANEUVERING SYSTEM
AbstractAir vehicle modeling like the helicopter is very challenging assignment because of the highly nonlinear effects, effective cross-coupling between its axes, and the uncertainties and complexity in its aerodynamics. The Twin Rotor Mutli-Input Multi-Output System (TRMS) represents in its behavior a helicopter. TRMS has been widely used as an apparatus in Laboratories for experiments of control applications. The system consists of two degrees of freedom (DOF) model; that is yawing and pitching. This paper discusses the design of Four Interval Type-2 fuzzy logic controllers (IT2FLC) for yaw and pitch axes and their cross-couplings of a twin rotor MIMO system. The objectives of the designed controllers are to maintain the TRMS position within the pre-defined desired trajectories when exposed to changes during its maneuver. This must be achieved under uncertain or unknown dynamics of the system and due to external disturbances applied on the yaw and pitch angles. The coupling effects are determined as the uncertainties in the nonlinear TRMS. A PSO algorithm is used to tune the Inputs and output gains of the four Proportional-Derivative (PD) Like IT2FLCs to enhance the tracking characteristics of the TRMS model. Simulation results show the substantial enhancement in the performance using PSO-Based Interval Type-2 fuzzy logic controllers compared with that of using IT2FLCs only. The maximum percentage of enhancements reaches about 33% and the average percentage of enhancements is about 17.1%. They also show the proposed controller effectiveness improving time domain characteristics and the simplicity of the controllers.
2. Juang, J. G.; Tu K. T; Liu W. K., Hybrid intelligent PID control for MIMO system, â€ž Proceeding of the 13th International Conference of Neural Information Processingâ€, Hong Kong, China, pp. 654-663, 2006.
3. Aldebrez, F. M.; Alam M. S.; Tokhi M. O., Hybrid Control for Tracking Performance of a Flexible System, â€ž Proceedings of the 8th International conference on Climbing and walking Robots and the Support Technologies for Mobile Machinesâ€, London, pp. 543-550, 2006.
4. Juang J. G.; Lin R. W.; Liu W. K., Comparison of classical and intelligent control for a MIMO system, â€žApplied Mathematics and computationâ€, Vol. 205, No. 2, pp. 778-791, 2008.
5. Patel A. A.; Pithadiya P.M.; Kannad H. V., Control of Twin Rotor MIMO System (TRMS) Using PID Controller, â€ž Proceedings of National Conference on Emerging Trends in Computer & Electrical Engineeringâ€, 2015.
6. Liu C. S.; Chen L. R., Ting C. S.; Hwang J. C.; Wu S. L., Improvement Twin Rotor MIMO System Tracking and Transient Response Using Fuzzy control Technology, â€žJournal of Aeronautics and Aviationâ€, Vol. 43, pp. 37-44, 2011.
7. Mahmoud T. S.; Marhaban M. H.; Hong T. S.; Sokchoo N., ANFIS Controller with Fuzzy Subtractive Clustering Method to Reduce Coupling Effects in Twin Rotor MIMO system (TRMS) with Less Memory and Time Usage, â€žProceeding of International Conference on Advanced Computer Controlâ€, Singapore, pp. 19-23, 2009.
8. Boubakir A.; Boudjema F.; Labiod S., A Neuro-Fuzzy Sliding Mode Controller using Nonlinear Sliding Surface Applied to the coupled Tanks System, â€žInt. Journal of Automation and Computingâ€, Vol. 6, No. 1, pp. 72-80, 2009.
9. Mahmoud T.S.; Hong T. S.; Marhaban M. H., Investigation of Using Neuro-Fuzzy and Self-Tuning Fuzzy controller to improve Pitch Angle Response of Twin Rotor MIMO System, â€žCanadian Aeronautical Space Journalâ€, Vol. 56, No. 2, pp. 45- 52, 2010.
10. Toha S. F.; Tokhi M. O., Real-Coded Genetic Algorithm for Parametric Modeling of a TRMS, â€žProceeding of the IEEE Congress on Evolutionary Computation (CEC â€™09)â€, pp. 2022-2028, May 2009.
11. Toha S. F.; Abd Latiff I.; Mohamed M.; Kokhi M. O, Parametric Modeling of a TRMS using Dynamic Spread Factor Particle Swarm Optimization, â€žProceedings of the 11th International Conference on Computer Modeling and Simulation (UKSIM â€˜09)â€, pp. 95-100, March 2009.
12. Allouani F.; Boukhetala D.; Boudjema F., Ant Colony Optimization Based Fuzzy Sliding Controller for the Twin Rotor MIMO System, â€žInt. Journal of Sciences and Technologies of Automatic Control & computer Engineering IJ-STAâ€, Vol. 5, No. 2, pp. 1660-1677-Dec. 2011.
13. Hashim H. A.; Abido M. A., Fuzzy Controller Design using Evolutionary Techniques for Twin Rotor MIMO System: A comparative Study, â€žHindawi Publishing Corporation- Computational Intelligence and Neuroscienceâ€, Vol. 2015, No. 49, 11 pages, January 2015.
14. Castillo O.; Type-2 Fuzzy Logic in Intelligent Control Applications, Studies in Fuzziness and Soft Computing, â€žSoft Computingâ€, Springer-Verlag, 2012
15. Kumbasar T.; Dodurka M.; Yesil E.; Sakalli A. The Simplest Interval Type-2 Fuzzy PID Controller Structural Analysis, â€žProceedings of IEEE International Conference on Fuzzy Systems, pp. 626-633, 2014.
16. Zeghlache S.; Kara K.; Saigaa D., Type-2 Fuzzy Logic Control of a 2-DOF Helicopter, â€žCentral European Journal of Engineeringâ€, Vol. 4, No. 3, pp. 303-315, 2014.
17. Maouche D.; Eker I., Adaptive Type-2 in Control of 2-DOF Helicopter, â€žInt. Journal of Electronics and Electrical Engineeringâ€, Vol. 5, No. 2, pp. 99-105, April 2017.
18. Elrahman M. F.; Imam A.; Taifor A., Fuzzy Control for A Twin Rotor Multi-Input Multi-Output System (TRMS), â€žSudan Engineering Society Journalâ€, Vol. 55, No. 53, pp. 19-25, September 2009.
19. Mendel J. M., â€žUncertain rule-based Fuzzy Logic Systems: Introduction and New Directionsâ€, NJ: Prentice Hall PTR, 2001.
20. Hassan M. Y.; Kothapalli G., Interval Type-2 Fuzzy Position Control of Electro-hydraulic Actuated Robotic Excavator, â€žInternational Journal of Mining Science and Technologyâ€, Vol. 22 , pp. 437â€“445, 2012.
21. . Kothapalli G,; Hassan M. Y., Compensation of Load Variation Using Fuzzy Controller for Hydraulic Actuated Front end Loader, â€žEmirates Journal for Engineering Researchâ€, Vol. 17, No. 1, pp. 1-8, 2012.
22. Wu, D.; Mendel J. M., Approaches for Reducing the Computational Cost of Interval Type-2 Fuzzy Logic Systems: Overview and Comparisons, â€žInformation Sciencesâ€, Vol. 21, pp. 80-90, 2013.
23. El-Nagar A. M.; El-Bardini M., Derivation and Stability Analysis of the Analytical Structures of the Interval Type-2 Fuzzy PID Controller, â€žApplied Soft Computingâ€, Vol. 24, pp. 704-716, 2014.
24. Kennedy, J.; Eberhart R., Particle Swarm Optimization, â€žIEEE Transactions on Evolutionary Computationâ€, Washington, USA, pp. 1942-1948, 1995.
25. Yang X.; Yuan J.; Mao H., A Modified Particle Swarm Optimizer with Dynamic Adaptation, â€žElsevier â€“Applied Mathematics and Computationâ€, Vol. 189, Issue 2, pp. 1205â€“1213, 15 June, 2007.
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