Neural Network Based Control of a Two-Mass Drive System
DOI:
https://doi.org/10.18201/ijisae.2019252787Keywords:
Neural network control, particle swarm optimization, robust control, two-mass drive systemAbstract
In this paper, two-mass drive system is modelled and speed control of the two-mass system is presented. The speed control of the system offers the challenge due to handle torsional vibrations. In the control structure, Particle Swarm Optimization (PSO) based conventional Proportional-Integral-Derivative (PID) controller and single-layer, feed-forward Neural Network controller with back-propagation learning algorithm are proposed. NN control is investigated to show the effectiveness of the control performance compared with the designed PID control. In order to have a fair comparison, PSO method is used to determine the optimum PID parameters and NN controller is designed with online learning algorithm. In the NN learning, back-propagation, which is the most preferred method, is adapted. Simulation studies are performed in different two parts to examine the performance of the proposed controller. In the first part, the controllers are tested for different step references and comparative results of the optimized PID and NN controllers are illustrated. In the second part, the effect of load torque is explored with proposed NN control method. According to the obtained simulation results, it can be seen that the designed NN controller provides better performances without and with load speeds.Downloads
References
G. Zhang, “Speed control of two-inertia system by PI/PID control,” IEEE Transactions on Industrial Electronics, vol. 47, no. 3, pp. 603-609, 2000.
T. Orlowsak-Kowalska and K. Szabat, “Optimization of fuzzy-logic speed controller for DC drive system with elastic joints,” IEEE Transactions on Industry Applications, vol. 40, no. 4, pp. 1138-1144, 2004.
T. Orlowska-Kowalska and K. Szabat, “Control of the drive system with stiff and elastic couplings using adaptive neuro-fuzzy approach,” IEEE Transactions on Industrial Electronics, vol. 54, no. 1, pp. 228-240, 2007.
T. M. O’Sullivan, C. M. Bingham and N. Schofield, “Enhanced servo-control performance of dual-mass systems,” IEEE Transactions on Industrial Electronics, vol. 54, no. 3, pp. 1387-1398, 2007.
K. Erenturk, “Gray-fuzzy control of a nonlinear two-mass system,” Journal of the Franklin Institute, vol. 347, pp. 1171-1185, 2010.
J.J.E. Slotine and W. Li, “Applied nonlinear control,” Englewood Cliffs, NJ: Prentice-Hall, 1991.
A. V. Topalov and O. Kaynak, “Neural network modeling and control of cement mills using a variable structure systems theory based on-line learning mechanism,” Journal of Process Control, vol. 14, no. 5, pp. 581-589, 2004.
D.E. Rumelhart, G.E. Hinton, and R.J. Williams, “Learning internal representations by error propagation,” Parallel Distributed Process, vol. 1, pp. 318-362, 1986.
M. Moreira and E. Fiesler, “Neural networks with adaptive learning rate and momentum terms,” EPFL Technical Report-82307, Idiap, Martigny, Ssitzerland, October, 1995.
V.V. Phansalkar and P.S. Sastry, “Analysis of the back-propagation algorithm with momentum,” IEEE Transactions on Neural Networks, vol. 5, no. 3, pp. 505-506, May, 1994.
S.M. Giriraj-Kumar, D. Jayaraj and A. R. Kishan, “PSO based tuning of a PID controller for a high performance drilling machine,” International Journal of Computer Applications, vol. 1, no. 19, pp. 12-18, 2010.
A. Oi, C. Nakazawa and T. Matsui, “Development of PSO-based PID tuning method,” International Conference on Control, Automation and Systems, Seoul, Korea, pp. 1917-1920, 2008.
G. Ozmen Koca, S. Dogan, “Optimization of control parameters of 2-dof twin-rotor mimo system,” ICENS International Conference on Engineering and Natural Science, Skopje, Macedonia, pp. 15-19, May, 2015.
F. Gao and H. Tong, “Differential evolution: an efficient method in optimal PID tuning and on–line tuning”, International Conference on Complex Systems and Applications, Wuxi, China, pp. 785-789, 2006.
M.I. Solihin, L. F. Tack and M. L. Kean, “Tuning of PID controller using particle swarm optimization (PSO),” International Conference on Advanced Science, Engineering and Information Technology, Malaysia, pp. 458-461, 2011.
T.M. O’Sullivan, C.M. Bingham, N. Schofield, “Enhanced servo-control performance of dual-mass systems,” IEEE Transactions on Industrial Electronics, vol. 54, no.3, pp. 1387–1399, June, 2007.
T. Orlowska-Kowalska, K. Szabat, “Neural-network application for mechanical variables estimation of a two-mass drive system,” IEEE Transactions on Industrial Electronics, vol. 54, no. 3, pp. 1352–1364, 2007.
K. Szabat, T. Tran-Van and M. Kaminski, “A modified fuzzy luenberger observer for a two-mass drive system,” IEEE Transactions on Industrial Informatics, vol. 11, no. 2, pp. 531-539, April, 2015.
S. Haykin, “Neural networks a comprehensive foundation,” Prentice Hall PTR, 1994.
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