Barış BIDIKLI

346352

A Self-Tuning PID Control Method for Multi-Input-Multi-Output Nonlinear Systems

DOI: 10.26650/electrica.2018.79181In this study, an artificial neural network (ANN) model-based self-tuning PID control method is proposed for the control of multi-input-multi-output (MIMO) nonlinear systems. A single layer, feed-forward ANN structure is trained via input and output data randomly collected from the system and classified as learning, test, and validation data to obtain the system model. The obtained model is utilized in an adaptive PID control scheme in conjunction with two different optimization methods for PID tuning and control. Using this scheme, PID parameters can be tuned to their optimum values and the system can be controlled simultaneously. The performance of the proposed method is demonstrated via experimental studies. 
Anahtar Kelimeler:

Multi-input-multi-output nonlinear systems, proportional-integral-derivative tuning, proportional-integral-derivative control

A Self-Tuning PID Control Method for Multi-Input-Multi-Output Nonlinear Systems

DOI: 10.26650/electrica.2018.79181In this study, an artificial neural network (ANN) model-based self-tuning PID control method is proposed for the control of multi-input-multi-output (MIMO) nonlinear systems. A single layer, feed-forward ANN structure is trained via input and output data randomly collected from the system and classified as learning, test, and validation data to obtain the system model. The obtained model is utilized in an adaptive PID control scheme in conjunction with two different optimization methods for PID tuning and control. Using this scheme, PID parameters can be tuned to their optimum values and the system can be controlled simultaneously. The performance of the proposed method is demonstrated via experimental studies. 
Keywords:

Multi-input-multi-output nonlinear systems, proportional-integral-derivative tuning, proportional-integral-derivative control,

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  • 1. T. Marchetti, M. Barolo, L. Jovanovic, H. Zisser, D. E. Seborg, “An improved PID switching control strategy for Type 1 Diabetes”, IEEE Trans on Biomed Eng, vol. 55, no. 3, pp. 857-865, 2008
  • 2. T. J. Ren, T. C. Chen and C. J. Chen, “Motion control for a two-wheeled vehicle using a self-tuning PID controller”, Control Engineering Practice, vol. 16, no. 3, pp. 365-375, 2008.
  • 3. J. Ye, “Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot”, Neurocomputing, vol. 71, no. 7-9, pp. 1561-1565, 2008.
  • 4. B. Allaoua, B. Gasbaoui, B. Mebarki, “Setting up PID DC motor speed control alteration parameters using particle swarm optimization strategy”, Leonaro Electronic J. of Practices and Technologies, no. 14, pp. 19-32, 2009.
  • 5. R. Kandiban, R. Arulmozhiyal, “Design of adaptive fuzzy PID controller for speed control of BLDC motor”, International Journal of Soft Computing and Engineering, vol. 2, no. 1, pp. 386-391, 2012.
  • 6. U. K. Bansal and R. Narvey, “Speed control of DC motor using fuzzy PID controller”, Advance in Electronic and Electric Engineering, vol. 3, no. 9, pp. 1209-1220, 2013.
  • 7. J. M. Zheng, S. Zhao, S. Wei, “Application of self-tuning fuzzy PID controller for a SRM direct drive volume control hydraulic press”, Control Engineering Practice, vol. 17, no. 12, pp. 1398-1404, 2009.
  • 8. Ş. Çetin, A. V. Akkaya, “Simulation and hybrid fuzzy-PID control for positioning of a hydraulic system”, Nonlinear Dynamics, vol. 61, no. 3, pp. 465-476, 2010.
  • 9. S. Soyguder, M. Karakose, H. Alli, “Design and simulation of self-tuning PID-type fuzzy adaptive control for an expert HVAC system”, Expert Systems with Applications, vol. 36, no. 3, pp. 4566-4573, 2009.
  • 10. R. Wai, J. Lee, K. Chuang, “Real-time PID control strategy for maglev transportation system via particle swarm optimization”, IEEE Transactions on Industrial Electronics, vol. 58, no. 2, pp. 629-646, 2011.
  • 11. J. Li, Y. Li, “Dynamic analysis and PID control for a quadrotor”, IEEE International Conference on Mechatronics and Automation China, 2011.
  • 12. I. Sadeghzadeh, A. Mehta, A. Chamseddine, Y. Zhang, “Active fault tolerant control of a quadrotor UAV based on gainscheduled PID control”, IEEE Canadian Conference on Electrical and Computer Engineering, Montreal, QC, Camada, 2012.
  • 13. F. Goodarzi, D. Lee, T. Lee, “Geometric nonlinear PID control of a quadrotor UAV on SE(3),” European Control Conference, Zurich, Switzerland, 2013.
  • 14. L. B. Prasad, B. Tyagi, H. O. Gupta, “Modelling and simulation for optimal control of nonlinear inverted pendulum dynamical system using PID controller and LQR”, Asia Modelling Symposium, Bali, Indonesia, 2012.
  • 15. L. B. Prasad, B. Tyagi, H. O. Gupta, “Optimal control of nonlinear inverted pendulum system using PID controller and LQR: Performance analysis without and with disturbance input”, International Journal of Automation and Computing, vol. 11, no. 6, pp. 661- 670 2014.
  • 16. W. Yu, J. Rosen, “Neural PID control of robot manipulators with application to an upper limb exoskeleton”, IEEE Trans Cybern, vol. 43, no. 2, pp. 673-684 2013.
  • 17. A. Kumar, V. Kumar, “Evolving and interval type-2 fuzzy PID controller for the redundant robotic manipulator”, Expert Systems with Applications, vol. 73, pp. 161-177, 2017.
  • 18. Y. Su, C. Zheng, “PID control for global finite-time regulation of robotic manipulators,” International Journal of Systems Science, vol. 48, no. 3, pp. 547-558, 2017.
  • 19. M. A. Khosravi, H. D. Taghirad, “Robust PID control of fully-constrained cable driven parallel robots”, Mechatronics, vol. 24, no. 2, pp. 87-97, 2014.
  • 20. K. Ou, Y. Wang, Z. Li, Y. Shen, D. Xuan, “Feedforward fuzzy-PID control for air flow regulation of PEM fuel cell system,” International Journal of Hydrogen Energy, vol. 40, no. 35, pp. 11686-11695, 2015.
  • 21. H. Tang, Y. Li, “Feedforward nonlinear PID control of a novel micromanipulator using Presiach hysteresis compensator,” Robotics and Computer-Integrated Manufacturing, vol. 34, pp. 124-132, 2015.
  • 22. M. Taherkhorsandi, M. J. Mahmoodabadi, M. Talebipour, K. K. Castillo-Villar, “Pareto design of an adaptive robust hybrid of PID and sliding control for a biped robot via genetic algorithm optimization”, Nonlinear Dynamics, vol. 79, pp. 251-263, 2015.
  • 23. Z. Civelek, M. Lüy, E. Çam, N. Barışçı, “Control of pitch angle of wind turbine by fuzzy pid controller”, Intelligent Automation & Soft Computing, vol. 22, no. 3, pp. 463-471, 2016.
  • 24. J. Song, W. Cheng, Z. Xu, S. Yuan, M. Liu, “Study on PID temperature control performance of a novel PTC material with room temperature Curie point”, International Journal of Heat and Mass Transfer, vol. 95, pp. 1038-1046, 2016.
  • 25. A. M. Simonovic, N. N. Jovanovic, N. S. Lukic, N. D. Zoric, S. N. Stupar, S. S. Ilic, “Experimental studies on active vibration control of smart plate using a modified PID controller with optimal orientation of piezoelectric actuator,” Journal of Vibration and Control, vol. 22, no. 1, pp. 2619-2631, 2016.
  • 26. R. Rout, B. Subudhi, “Inverse optimal self-tuning PID control design for an autonomous underwater vehicle”, International Journal of Systems Science, vol. 48, no. 2, pp. 367-375, 2017.
  • 27. K. J. Aström, T. Hagglund, C. C. Hang, W. K. Ho, “Automatic tuning and adaptation for PID controllers - a survey,” Control Engineering Practice, vol. 1, no. 4, pp. 699-714, 1993.
  • 28. P. Cominos, N. Munro, “PID controllers: recent tuning methods and design to specification,” Control Theory and Applications, vol. 149, no. 1, pp. 46-53, 2002.
  • 29. K. Li, “PID tuning for optimal closed-loop performance with specified gain and phase margins”, IEEE Transactions on Control, Systems Technology, vol. 21, no. 3, pp. 1024-1030, 2013.
  • 30. B. B. Alagoz, A. Ates, C. Yeroglu, “Auto-tuning of PID controller according to fractional-order reference model approximation for DC rotor control”, Mechatronics, vol. 23, pp. 789-797, 2013.
  • 31. A. T. Azar, F. E. Serrano, “Robust IMC-PID tuning for cascade control systems with gain and phase margin specifications”, Neural Computing and Applications, vol. 25, pp. 983-995, 2014.
  • 32. C. Y. Jin, H. R. Kyung, S. W. Sung, J. Lee, I. B. Lee, “PID auto-tuning using new model reduction method and explicit PID tuning rule for a fractional order plus time delay model,” Journal of Process Control, vol. 24, pp. 113-128, 2014.
  • 33. Ş. Yavuz, L. Malgaca, H. Karagülle, “Analysis of active vibration control of multi-degree-of-freedom flexible systems by Newmark method”, Simulation, Modelling and Practice Theory, vol. 69, pp. 136-148, 2016.
  • 34. S. Iplikci, “A comparative study on a novel model-based PID tuning and control mechanism for nonlinear systems”, International Journal of Robust and Nonlinear Control, vol. 20, no. 13, pp. 1483-1501, 2010.
  • 35. J. Nocedal, S. J. Wright, Numerical Optimization, Springer: New York, 1999.
  • 36. P. Venkatamaran, Applied Optimization with MATLAB Programming, Wiley-Interscience: New York, 2002.
Electrica-Cover
  • ISSN: 2619-9831
  • Başlangıç: 2001
  • Yayıncı: İstanbul Üniversitesi-Cerrahpaşa
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