A neural multicontroller for strongly nonlinear systems

References

• Abdelkader, A., Boussada, M., Nouri, A. S., Hammouri, H., & Fiaty, K. (2015). Nonlinear high ga in observers for a semi continuous stirred tank reactor. In A. Amira, B. Moez, S. N. Ahmed, H. Hassan, & F. Koffi F (Eds.), 2015 IEEE 12th International Multi-conference on Systems, Signals & Devices (SSD15) (pp. 1–7). IEEE.
   Google Scholar
• Ahmadi, M., Rikhtehgar, P., & Haeri, M. (2020). A multi-model control of nonlinear systems: A cascade decoupled design procedure based on stability and performance. Transactions of the Institute of Measurement and Control, 42(7), 1271–1280. https://doi.org/https://doi.org/10.1177/0142331219888368
   Web of Science ®Google Scholar
• Al-Akhras, M. A., Aly, G. M., & Green, R. J. (1995). Multi-model neural network-based intelligent controller. In M. A. Al-Akhras, G. M. Aly, & R. J. Green (Eds.), Proceedings of IEEE/IAS International Conference on Industrial Automation and Control (pp. 49–53). IEEE.
   Google Scholar
• Allaoui, M., Messaoud, A., Dehri, K., & Ben Abdennour, R. (2017). Multimodel repetitive–predictive control of nonlinear systems: Rejection of unknown non-stationary sinusoidal disturbances. International Journal of Control, 90(7), 1478–1494. https://doi.org/https://doi.org/10.1080/00207179.2016.1210233
   Web of Science ®Google Scholar
• Atig, A., Druaux, F., Lefebvre, D., Abderrahim, K., & Ben Abdennour, R. (2010a). Neural emulator and controller with decoupled adaptive rates for nonlinear systems: Application to chemical reactors. International Journal on Sciences and Techniques of Automatic Control and Computer Engineering (IJ-STA), 4(2), 1298–1319.
   Google Scholar
• Atig, A., Druaux, F., Lefebvre, D., Abderrahim, K., & Ben Abdennour, R. (2010b). A new neural adaptive control based on neural emulation of complex square MIMO systems. International Review of Automatic Control, 3(6), 612–623.
   Google Scholar
• Bahri, N., Atig, A., Ben Abdennour, R., Druaux, F., & Lefebvre, D. (2012). Multimodel and neural emulators for non-linear systems: Application to an indirect adaptive neural control. International Journal of Modelling, Identification and Control, 17(4), 348–359. https://doi.org/https://doi.org/10.1504/IJMIC.2012.051086
   Google Scholar
• Bahri, N., Atig, A., Ben Abdennour, R., Druaux, F., & Lefebvre, D. (2014). Multivariable adaptive neural control based on multimodel emulator for nonlinear square MIMO systems. In N. Bahri, A. Atig, R. Ben Abdennour, F. Druaux, & D. Lefebvre (Eds.), 2014 IEEE 11th International Multi-conference on Systems, Signals & Devices (SSD14) (pp. 1–6). IEEE.
   Google Scholar
• Bahri, N., Messaoud, A., & Ben Abdennour, R. (2011). A multimodel emulator for nonlinear system controls. International Journal on Sciences and Techniques of Automatic Control and Computer Engineering (IJ-STA), 5(1), 1500–1515.
   Google Scholar
• Baruch, I., Flores, J., & Garrido, R. (2001). A fuzzy neural recurrent multi-model for systems identification and control. In I. S. Baruch, J. M. Flores, & R. Garrido (Eds.), 2001 European Control Conference (ECC) (pp. 3540–3545). IEEE.
   Google Scholar
• Baruch, I. S., Lopez, R. B., Guzman, J.-L. O., & Flores, J. M. (2008). A fuzzy-neural multi-model for nonlinear systems identification and control. Fuzzy Sets and Systems, 159(20), 2650–2667. https://doi.org/https://doi.org/10.1016/j.fss.2008.01.027
   Web of Science ®Google Scholar
• Ben Atia, S., Messaoud, A., & Ben Abdennour, R. (2015). Decoupled multimodel predictive control based on multi-observer for discrete-time uncertain nonlinear systems. In S. Ben Atia, A. Messaoud, & R. Ben Abdennour (Eds.), IEEE 12th International Multi-conference on Systems, Signals & Devices (SSD15) (pp. 1–8). IEEE.
   Google Scholar
• Boussaada, M., Fiaty, K., & Gilles, G. (2002). A moving state estimator of an olive oil waste esterification in a semi-batch reactor: Experimental validation. In M. Boussaada, K. Fiaty, & G. Gilles (Eds.), ISCRE Hong Kong.
   Google Scholar
• Dehri, K., Messaoud, A., & Ben Abdennour, R. (2021). A discrete repetitive sliding mode multicontrol for non-linear systems. International Journal of Systems Science, 52(9), 1–19. https://doi.org/https://doi.org/10.1080/00207721.2020.1870017
   Google Scholar
• Druaux, F., Leclercq, E., & Lefebvre, D. (2004). Adaptive neural network control for uncertain or unknown nonlinear systems. In F. Druaux, E. Leclercq, & D. Lefebvre (Eds.), Proceedings of IEEE MMAR (pp. 1309–1314).
   Google Scholar
• Fabri, S. G., & Kadirkamanathan, V. (2001). Multiple model dual adaptive control of jump nonlinear systems. In S. G. Fabri & V. Kadirkamanathan (Eds.), Functional adaptive control (pp. 187–212). Springer.
   Google Scholar
• Farhat, Y., Atig, A., Zribi, A., & Ben Abdennour, R. (2020). A new learning rate tuning for nonlinear system emulation. In F. Yassin, A. Asma, Z. Ali, & B. A. Ridha (Eds.), 2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA) (pp. 35–39). IEEE.
   Google Scholar
• Farhat, Y., Rhili, F. E., Atig, A., Zribi, A., & Ben Abdennour, R. (2021). Stability analysis strategy for the adaptive neural control system: A practical validation via a transesterification reactor. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 45(4), 1–15. https://doi.org/https://doi.org/10.1007/s40998-021-00434-9
   Google Scholar
• Hu, Y.-H., Zhao, L., Wu, L.-B., Zhao, N.-N., & Zhang, Y.-J. (2020). Adaptive event-triggered fuzzy tracking control of nonlinear systems with dead-zones and unmeasurable states. International Journal of Systems Science, 51(16), 1–18. https://doi.org/https://doi.org/10.1080/00207721.2020.1814445
   Google Scholar
• Khezami, N., Guillaud, X., & Braiek, N. B. (2009). Multimodel LQ controller design for variable-speed and variable pitch wind turbines at high wind speeds. In N. Khezami, X. Guillaud, & N. Benhadj Braiek (Eds.), 6th International Multi-conference on Systems, Signals and Devices (pp. 1–6). IEEE.
   Google Scholar
• Kong, J., Niu, B., Wang, Z., Zhao, P., & Qi, W. (2021). Adaptive output-feedback neural tracking control for uncertain switched MIMO nonlinear systems with time delays. International Journal of Systems Science, 52(13), 1–18. https://doi.org/https://doi.org/10.1080/00207721.2021.1909775
   Google Scholar
• Kovacic, Z., & Bogdan, S. (2018). Fuzzy controller design: theory and applications. CRC Press.
   Google Scholar
• Kumpati, S. N., & Kannan, P. (1990). Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1(1), 4–27. https://doi.org/https://doi.org/10.1109/72.80202
   PubMedGoogle Scholar
• Leclercq, E., Druaux, F., Lefebvre, D., & Zerkaoui, S. (2005). Autonomous learning algorithm for fully connected recurrent networks. Neurocomputing, 63(1–3), 25–44. https://doi.org/https://doi.org/10.1016/j.neucom.2004.04.007
   Google Scholar
• Li, H., Ren, K., Li, S., & Dong, H. (2020). Adaptive multi-model switching predictive active power control scheme for wind generator system. Energies, 13(6), 1329–1336. https://doi.org/https://doi.org/10.3390/en13061329
   Web of Science ®Google Scholar
• Ma, F., & Hanna, M. A. (1999). Biodiesel production: A review. Bioresource Technology, 70(1), 1–15. https://doi.org/https://doi.org/10.1016/S0960-8524(99)00025-5
   Web of Science ®Google Scholar
• Mejdi, S., Messaoud, A., & Ben Abdennour, R. (2019). Sensor fault tolerant controller for nonlinear systems using an uncoupled state multiobserver. In S. Mejdi, A. Messaoud, & R. Ben Abdennour (Eds.), International Conference on Advanced Systems and Emergent Technologies (IC_ASET) (pp. 40–46). IEEE.
   Google Scholar
• Mejdi, S., Messaoud, A., & Ben Abdennour, R. (2020). Fault tolerant multicontrollers for nonlinear systems: A real validation on a chemical process. International Journal of Applied Mathematics and Computer Science, 30(1), 61–74. https://doi.org/https://doi.org/10.34768/amcs-2020-0005
   Web of Science ®Google Scholar
• Messaoud, A., Ben Atia, S., & Ben Abdennour, R. (2019). An unknown input multiobserver based on a discrete uncoupled multimodel for uncertain nonlinear systems: Experimental validation on a transesterification reactor. ISA Transactions, 93(12), 302–311. https://doi.org/https://doi.org/10.1016/j.isatra.2019.03.016
   PubMedGoogle Scholar
• Messaoud, A., Ltaief, M., & Ben Abdennour, R. (2009). Supervision based on partial predictors for a multimodel generalised predictive control: Experimental validation on a semi-batch reactor. International Journal of Modelling, Identification and Control, 6(4), 333–340. https://doi.org/https://doi.org/10.1504/IJMIC.2009.024740
   Google Scholar
• Orjuela, R., Maquin, D., & Ragot, J. (2006). Nonlinear system identification using uncoupled state multiple-model approach. In R. Orjuela, D. Maquin, & J. Ragot (Eds.), 4th Workshop on Advanced Control and Diagnosis, ACD'20-06, Nancy, France.
   Google Scholar
• Rhili, F. E., Atig, A., & Ben Abdennour, R. (2018). A new strategy for neural emulator learning rate tuning. In F. E. Rhili, A. Atig, & R. Ben Abdennour (Eds.), 2018 15th International Multi-conference on Systems, Signals & Devices (SSD) (pp. 952–957). IEEE.
   Google Scholar
• Rhili, F. E., Atig, A., & Ben Abdennour, R. (2019). Adaptive neural control using fuzzy adaptive parameter for nonlinear processes. In S. Ben Atia, A. Messaoud, & R. Ben Abdennour (Eds.), 2019 International Conference on Advanced Systems and Emergent Technologies (IC_ASET) (pp. 47–52). IEEE.
   Google Scholar
• Sahin, M., & Yavrucuk, I. (2019). Performance comparison of two turbine blade pitch controller design methods based on equilibrium and frozen wake assumptions. In R. F. Ezzahra, A. Asma, & B. A. Ridha (Eds.), 10th Ankara International Aerospace Conference.
   Google Scholar
• Si, W., Qi, L., Hou, N., & Dong, X. (2019). Finite-time adaptive neural control for uncertain nonlinear time-delay systems with actuator delay and full-state constraints. International Journal of Systems Science, 50(4), 726–738. https://doi.org/https://doi.org/10.1080/00207721.2019.1567869
   Web of Science ®Google Scholar
• Sibtain, D., Murtaza, A. F., Ahmed, N., Sher, H. A., & Gulzar, M. M. (2021). Multi control adaptive fractional order PID control approach for PV/wind connected grid system. International Transactions on Electrical Energy Systems, 31(4), e12809. https://doi.org/https://doi.org/10.1002/etep.v31.4
   Web of Science ®Google Scholar
• Tang, W., Qi, Y., Long, W., & Gao, H. (2020). Neural networks-based multiple model control of a class of nonlinear systems with unknown parameters. In W. Tang, Y. Qi, W. Long, & H. Gao (Eds.), 2020 Chinese Automation Congress (CAC) (pp. 3738–3742). IEEE.
   Google Scholar
• Williams, R. J. (1990). Adaptive state representation and estimation using recurrent connectionist networks. Neural Networks for Control, 30, 97–114. https://doi.org/https://doi.org/10.7551/mitpress%2F4939.003.0008
   Google Scholar
• Zeng, W., Jiang, Q., Du, S., Hui, T., Liu, Y., & Li, S. (2021). Design of the flexible switching controller for small pwr core power control with the multi-model. Nuclear Engineering and Technology, 53(3), 851–859. https://doi.org/https://doi.org/10.1016/j.net.2020.07.037
   Web of Science ®Google Scholar
• Zerkaoui, S., Druaux, F., Leclercq, E., & Lefebvre, D. (2009). Stable adaptive control with recurrent neural networks for square MIMO nonlinear systems. Engineering Applications of Artificial Intelligence, 22(4), 702–717. https://doi.org/https://doi.org/10.1016/j.engappai.2008.12.005
   Web of Science ®Google Scholar
• Zhai, J.-Y., Fei, S.-M., & Zhang, K.-J. (2006). A discrete-time system adaptive control using multiple models and rbf neural networks. In J. Y. Zhai, S. M. Fei, and K. J. Zhang International Symposium on Neural Networks (pp. 881–887). Springer.
   Google Scholar
• Zhao, T., & Duan, G. (2020). Design of globally stabilising switching control law for a type of nonlinear systems. International Journal of Systems Science, 51(16), 1–12. https://doi.org/https://doi.org/10.1080/00207721.2020.1819466
   Google Scholar