Runge-Kutta MIMO NARMA-L2 Controller
AbstractThis paper introduces a novel control architecture which combines NARMA-L2 control method with Runge Kutta based system modeling. Control law is developed employing NARMA-L2 model via Taylor expansion. Jacobian information required for the controller is constituted by making use of RK model of plant to be controlled. To evaluate performance, simulations are carried out on the Three Tank System. Acquired results illustrate thatintroduced controller has substantially good performance on MIMO nonlinear systems.
M. Majstorovic, I. Nikolic, J. Radovic, and G. Kvascev, “Neural network control approach for a two-tank system,” 2008 9th Symp. Neural Netw. Appl. Electr. Eng., vol. 2, no. 1, pp. 2–5, 2008.
J. O. Pedro, O. T. C. Nyandoro, and S. John, “Neural network based feedback linearisation slip control of an anti-lock braking system,” Proc. 2009 7th Asian Control Conf. ASCC 2009, no. Lmi, pp. 1251–1257, 2009.
O. De Jesus, A. Pukrittayakamee, and M. Hagan, “A comparison of neural network control algorithms,” in International Joint Conference on Neural Networks, 2001, vol. 1, pp. 521–526.
M. T. Hagan, H. B. Demuth, and O. De Jesus, “An introduction to the use of neural networks in control systems,” Int. J. Robust Nonlinear Control, vol. 12, no. 11, pp. 959–985, Sep. 2002.
A. Pukrittayakamee, O. De Jesus, and M. T. Hagan, “Smoothing the Control Action for NARMA-L2 Controllers,” in 45th Midwest Symposium on Circuits and Systems, 2002, vol. 3, pp. 37–40.
Wahyudi, S. S. Mokri, and A. A. Shafie, “Real Time Implementation of NARMA L2 Feedback Linearization and Smoothed NARMA L2 Controls of a Single Link Manipulator,” in International Conference on Computer and Communication Engineering, 2008, pp. 691–697.
A. Akbarimajd and S. Kia, “NARMA-L2 Controller for 2-DoF Underactuated Planar Manipulator,” in International Conference on Control, Automation, Robotics and Vision (ICARCV 2010), 2010, no. December, pp. 195–200.
T. Vesselenyi, S. Dzițac, I. Dzițac, and M.-J. Manolescu, “Fuzzy and Neural Controllers for a Pneumatic Actuator,” Int. J. Comput. Commun. Control, vol. 2, no. 4, p. 375, 2007.
K. S. Narendra and S. Mukhopadhyay, “Adaptive control using neural networks and approximate models,” IEEE Trans. Neural Networks, vol. 8, no. 3, pp. 475–85, Jan. 1997.
S. Iplikci, “Runge-Kutta model-based adaptive predictive control mechanism for non-linear processes,” Trans. Inst. Meas. Control, vol. 35, no. 2, pp. 166–180, 2013.
K. Srakaew, J. Kananai, and R. Chancharoen, “Trajectory Control of a Nonlinear Dynamical System using NARMA L2 Neurocontroller,” J. Comput. Inf. Technol. Comput. Inf. Technol.
K. Uçak and G. Ö. Günel, “A Novel Adaptive NARMA-L2 Controller Based on Online Support Vector Regression for Nonlinear Systems,” Neural Process. Lett., vol. 44, no. 3, pp. 857–886, 2016.
K. Uçak, and G. Ö. Günel, “Online Support Vector Regression Based Adaptive NARMA-L2 Controller for Nonlinear Systems,” Neural Processing Letters, vol. 53, pp. 405–428, 2021.
X. Yuan, Y. Wang, and L. Wu, “Composite feedforward-feedback controller for generator excitation system,” Nonlinear Dyn., vol. 54, no. 4, pp. 355–364, Jan. 2008.
M. Majstorovic, I. Nikolic, J. Radovic, and G. Kvascev, “Neural network control approach for a two-tank system,” in Symposium on Neural Network Applications in Electrical Engineering, 2008, vol. 2, no. 1, pp. 203–206.
K. Uçak, “A Runge – Kutta neural network-based control method for nonlinear MIMO systems,” Soft Comput., vol. 23, no. 17, pp. 7769–7803, 2019.
M. Cetin and S. Iplikci, “A novel auto-tuning PID control mechanism for nonlinear systems,” ISA Trans., vol. 58, pp. 292–308, 2015.
S. Beyhan, “Runge-Kutta model-based nonlinear observer for synchronization and control of chaotic systems,” ISA Trans., vol. 52, no. 4, pp. 501–509, 2013.
S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics. The MIT Press, 2005.
S. Iplikci, “A support vector machine based control application to the experimental three-tank system,” ISA Trans., vol. 49, no. 3, pp. 376–386, 2010.
D. Theilliol, H. Noura, and J.-C. Ponsart, “Fault diagnosis and accommodation of a three-tank system based on analytical redundancy,” ISA Trans., vol. 41, no. 3, pp. 365–382, 2002.
A. Gmbh, DTS200 Laboratory Setup Three - Tank - System. amira GmbH, 2000.
K. Uçak, “A Novel Model Predictive Runge–Kutta Neural Network Controller for Nonlinear MIMO Systems,” Neural Processing Letters, vol. 51, pp. 1789–1833, 2020.
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