Adaptive MIMO fuzzy PID controller based on peak observer
DOI:
https://doi.org/10.11121/ijocta.2023.1247Keywords:
Adaptive Control, Fuzzy PID Controller, MIMO Fuzzy PID Controller, Peak Observer, Peak Observer based OptimizationAbstract
In this paper, a novel peak observer based adaptive multi-input multi-output (MIMO) fuzzy proportional-integral-derivative (PID) controller has been introduced for MIMO time delay systems. The adaptation mechanism proposed by Qiao and Mizumoto [1] for single-input single-output (SISO) systems has been enhanced for MIMO system adaptive control. The tracking, stabilization and disturbance rejection performances of the proposed adaptation mechanism have been evaluated for MIMO systems by comparing with non-adaptive fuzzy PID and classical PID controllers. The obtained results indicate that the introduced adjustment mechanism for MIMO fuzzy PID controller can be successfully deployed for MIMO time delay systems.
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References
Qiao, W.Z. & Mizumoto, M. (1996). PID type fuzzy controller and parameters adaptive method. Fuzzy Sets and Systems, 78(1), 23–35.
Chou, C.H. & Lu, H.C. (1994). A heuristic self-tuning fuzzy controller. Fuzzy Sets and Systems, 61(3), 249–264.
Jung, C.H. Ham, C.S. & Lee, K.I. (1995). A real-time self-tuning fuzzy controller through scaling factor adjustment for the steam generator of NPP. Fuzzy Sets and Systems, 74(1), 53–60.
Maeda, M. & Murakami, S. (1992). A Self-tuning fuzzy controller. Fuzzy Sets and Systems, 51(1), 29–40.
Mudi, R.K. & Pal, N.R. (1999). A robust self-tuning scheme for PI- and PD-type fuzzy controllers. IEEE Transactions on Fuzzy Systems, 7(1), 2–16.
Zheng, L. (1992). A practical guide to tune of proportional and integral (PI) like fuzzy controllers. IEEE International Conference on Fuzzy Systems, 633–640
Chung, H.Y. Chen, B.C. & Lin, J.J. (1998). A PI-type fuzzy controller with self-tuning scaling factors. Fuzzy Sets and Systems, 93(1), 23–28.
Chao, C.T. & Teng, C.C. (1997). A PD-like self-tuning fuzzy controller without steady-state error. Fuzzy Sets and Systems, 87(2), 141–154.
Woo, Z.H., Chung, H.Y. & Lin, J.J. (2000). A PID type fuzzy controller with self-tuning scaling factors. Fuzzy Sets and Systems, 115(2), 321–326.
Hu, B., Mann, G.K.I. & Gosine, R.G. (1999). New methodology for analytical and optimal design of fuzzy PID controllers. IEEE Transactions on Fuzzy Systems, 7(5), 521–539.
Ketata, R., Geest, D.D. & Titli, A. (1995). Fuzzy controller: design, evaluation, parallel and hierarchical combination with a PID controller. Fuzzy Sets and Systems, 71(1), 113–129.
Kim, B.J. & Chung, C.C. (2001). Design of fuzzy PID controller for tracking control. Journal of Institute of Control, 7(7), 1–7.
Kien, C.V., Son, N.N. & Anh, H.P.H. (2020). Adaptive MIMO fuzzy controller for double coupled tank system optimizing by jaya algorithm. 5th International Conference on Green Technology and Sustainable Development (GTSD), Ho Chi Minh City, Vietnam, 495–499.
Cherrat, N., Boubertakh, H. & Arioui, H. (2018). Adaptive fuzzy PID control for a class of uncertain MIMO nonlinear systems with dead-zone inputs’ Nonlinearities. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 42, 21–39.
Gil, P., Lucena, C., Cardoso, A. & Palma, L.B. (2015). Gain tuning of fuzzy PID controllers for MIMO systems: a performance-driven approach. IEEE Transactions on Fuzzy Systems, 23(4), 757– 768.
Yordanova, S., Yankov, V. & Jain, L. (2017). MIMO fuzzy logic supervisor-based adaptive control using the example of coupled-tanks levels control. International Journal of Innovative Computing, Information and Control, 13(2), 453–470.
Pinto, M.B.B., Mota, J.G.R. & Almeida, O.M. (2010). PID self-adjustable fuzzy logic MIMO case: method and application. 9th IEEE/IAS International Conference on Industry Applications, Sao Paulo, Brazil, 1–6.
Yesil, E., Guzelkaya, M. & Eksin, I. (2003). Fuzzy PID controllers: an overview. 3rd Triennial ETAI International Conference on Applied Automatic Systems, Ohrid, Macedonia, 1–8.
Kumar, V., Nakra, B.C. & Mittal, A. (2011). A review of classical and fuzzy PID controllers. International Journal of Intelligent Control and Systems, 16(3), 170–181.
Guzelkaya, M., Eksin, I. & Yesil, E. (2003). Self-tuning of PID-type fuzzy logic controller coefficients via relative rate observer. Engineering Applications of Artificial Intelligence, 16(3), 227– 236.
Karasakal, O., Yesil, E., Guzelkaya, M. & Eksin, I. (2003). An implementation of peak observer based self-tuning fuzzy PID-type controller on PLC. 3rd International Conference on Electrical and Electronics Engineering, ELECO, Bursa, Turkey, 1–5.
Yesil, E., Kumbasar, T., Dodurka, M. & Sakalli, A. (2014). Peak observer based self-tuning of type- 2 fuzzy PID controller. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Rhodes, Greece, 487– 497.
Ucak, K. & Gunel, G. O. (2017). Generalized self-tuning regulator based on online support vector regression. Neural Computing and Applications, 28, S775–S801.
Ucak, K. & Gunel, G. O. (2017). Fuzzy PID type STR based on SVR for nonlinear systems. 10th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey, 764– 768.
Arslanturk, B. & Ucak, K. (2022). An improved adaptive fuzzy PID controller based on peak observer for nonlinear dynamical systems. International ONLINE Conference on Mathematical Advances and Applications (ICOMAA-2022), Istanbul, Turkey, 85–91.
Bouallegue, S., Haggege, J., Ayadi, M. & Benrejeb, M. (2012). PID-type fuzzy logic controller tuning based on particle swarm optimization. Engineering Applications of Artificial Intelligence, 25(3), 484–493.
Karaman, K., Bekaroglu, Y.T., Soylemez, M.T., Ucak, K. & Gunel, G. O. (2015). Controlling 3- DOF helicopter via fuzzy PID controller. 9th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey, 869– 873
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