Real-time model predictive control using a self-organizing neural network

IEEE Transactions on Neural Networks and Learning Systems

Volumn 24, Issue 9, 2013, Pages 1425-1436

  Han, Hong Gui ^a   Wu, Xiao Long ^a   Qiao, Jun Fei ^a  

^a BEIJING UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Fast gradient method; model predictive control (MPC); optimal control; real time; self organizing radial basis function neural network (SORBFNN)

Indexed keywords

DISTURBANCE REJECTION PERFORMANCE; FAST GRADIENT METHODS; OPTIMAL CONTROLS; RADIAL BASIS FUNCTION NEURAL NETWORKS; REAL-TIME; SELF-ORGANIZING NEURAL NETWORK; STEADY STATE PERFORMANCE; UNIFORMLY ULTIMATELY BOUNDED;

CONTROL; GRADIENT METHODS; MODEL PREDICTIVE CONTROL; NEURAL NETWORKS; NONLINEAR SYSTEMS; OPTIMAL CONTROL SYSTEMS;

PREDICTIVE CONTROL SYSTEMS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; DECISION SUPPORT SYSTEM; HUMAN; NONLINEAR SYSTEM; PREDICTIVE VALUE; REACTION TIME; TIME;

ALGORITHMS; COMPUTER SIMULATION; DECISION SUPPORT TECHNIQUES; HUMANS; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; PREDICTIVE VALUE OF TESTS; REACTION TIME; TIME FACTORS;

EID: 84882889068     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2013.2261574     Document Type: Article

References (52)

1. R. Stefan, N. J. Colin, and M. Manfred, "Computational complexity certification for real-time MPC with input constraints based on the fast gradient method," IEEE Trans. Autom. Control, vol. 57, no. 6, pp. 1391-1402, Jun. 2012.
2. C. H. Lu, "Design and application of stable predictive controller using recurrent wavelet neural networks," IEEE Trans. Ind. Electron., vol. 56, no. 9, pp. 3733-3742, Sep. 2009.
3. H. G. Han, J. F. Qiao, and Q. L. Chen, "Model predictive control of dissolved oxygen concentration based on a self-organizing RBF neural network," Control Eng. Pract., vol. 20, no. 4, pp. 465-476, Apr. 2012.
4. H. Peng, K. Nakano, and H. Shioya, "Nonlinear predictive control using neural nets-based local linearization ARX model-Stability and industrial application," IEEE Trans. Control Syst. Technol., vol. 15, no. 1, pp. 130-143, Jan. 2007. (Pubitemid 46061030)
5. D. Q. Mayne, J. B. Rawlings, C. V. Rao, and P. O. M. Scokaert, "Constrained model predictive control: Stability and optimality," Automatica, vol. 36, no. 6, pp. 789-814, Jun. 2000. (Pubitemid 30587683)
6. K. Graichen, "A fixed-point iteration scheme for real-time model predictive control," Automatica, vol. 48, no. 7, pp. 1300-1305, Jul. 2012.
7. M. Rubagotti, D. M. Raimondo, A. Ferrara, and L. Magni, "Robust model predictive control with integral sliding mode in continuous-time sampled-data nonlinear systems," IEEE Trans. Autom. Control, vol. 56, no. 3, pp. 556-570, Mar. 2011.
8. R. Scattolini, "Architectures for distributed and hierarchical model predictive control-A review," J. Process. Control, vol. 19, no. 5, pp. 723-731, May 2009.
9. Y. Wang and S. Boyd, "Fast model predictive control using online optimization," IEEE Trans. Control Syst. Technol., vol. 18, no. 2, pp. 267-278, Mar. 2010.
10. D. Angeli, R. Amrit, and J. B. Rawlings, "On average performance and stability of economic model predictive control," IEEE Trans. Autom. Control, vol. 57, no. 7, pp. 1615-1626, Jul. 2012.
11. D. M. Pena and P. D. Christofides, "Lyapunov-based model predictive control of nonlinear systems subject to data losses," IEEE Trans. Autom. Control, vol. 53, no. 9, pp. 2076-2089, Oct. 2008.
12. A. Alessio, D. Barcelli, and A. Bemporad, "Decentralized model predictive control of dynamically coupled linear systems," J. Process. Control, vol. 21, no. 5, pp. 705-714, May 2011.
13. Y. D. Ma, F. Borrelli, B. Hencey, B. Coffey, S. Bengea, and P. Haves, "Model predictive control for the operation of building cooling systems," IEEE Trans. Control Syst. Technol., vol. 20, no. 3, pp. 796-803, May 2012.
14. C. H. Lu and C. C. Tsai, "Adaptive predictive control with recurrent neural network for industrial processes: An application to temperature control of a variable-frequency oil-cooling machine," IEEE Trans. Ind. Electron., vol. 55, no. 3, pp. 1366-1375, Mar. 2008.
15. P. Cortes, A. Wilson, S. Kouro, J. Rodriguez, and H. Abu-Rub, "Model predictive control of multilevel cascaded H-bridge inverters," IEEE Trans. Ind. Electron., vol. 57, no. 8, pp. 2691-2699, Aug. 2010.
16. F. Borrelli, M. Baotic, J. Pekar, and G. Stewart, "On the computation of linear model predictive control laws," Automatica, vol. 46, no. 6, pp. 1035-1041, Jun. 2010.
17. C. Muller, D. E. Quevedo, and G. C. Goodwin, "How good is quantized model predictive control with horizon one?" IEEE Trans. Autom. Control, vol. 56, no. 11, pp. 2623-2638, Nov. 2011.
18. L. Magni and R. Scattolini, "Model predictive control of continuoustime nonlinear systems with piecewise constant control," IEEE Trans. Autom. Control, vol. 49, no. 6, pp. 900-907, Jun. 2004.
19. D. E. Miller and N. Mansouri, "Model reference adaptive control using simultaneous probing, estimation, and control," IEEE Trans. Autom. Control, vol. 55, no. 9, pp. 2014-2029, Sep. 2010.
20. K. I. Kouramas, N. P. Faisca, C. Panos, and E. N. Pistikopoulos, "Explicit/multi-parametric model predictive control (MPC) of linear discrete-time systems by dynamic and multi-parametric programming," Automatica, vol. 47, no. 8, pp. 1638-1645, Aug. 2011.
21. J. F. Qiao and H. G. Han, "A repair algorithm for RBF neural network and its application to chemical oxygen demand modeling," Int. J. Neural Syst., vol. 20, no. 1, pp. 63-74, Jan. 2010.
22. H. G. Han and J. F. Qiao, "Adaptive dissolved oxygen control based on dynamic structure neural network," Appl. Soft Comput., vol. 11, no. 4, pp. 3812-3820, Apr. 2011.
23. U. Yuzgec, Y. Becerikli, and M. Turker, "Dynamic neural-network-based model-predictive control of an industrial baker's yeast drying process," IEEE Trans. Neural Netw., vol. 19, no. 7, pp. 1231-1242, Jul. 2008.
24. M. Han, J. C. Fan, and J. Wang, "A dynamic feedforward neural network based on Gaussian particle swarm optimization and its application for predictive control," IEEE Trans. Neural Netw., vol. 22, no. 9, pp. 1457-1458, Sep. 2011.
25. P. Kittisupakorn, P. Thitiyasook, M. A. Hussain, and W. Daosud, "Neural network based model predictive control for a steel pickling process," J. Process. Control, vol. 19, no. 4, pp. 579-590, Apr. 2009.
26. M. A. Hosen, M. A. Hussain, and F. S. Mjalli, "Control of polystyrene batch reactors using neural network based model predictive control (NNMPC): An experimental investigation," Control Eng. Pract., vol. 19, no. 4, pp. 454-467, Apr. 2011.
27. H. Yu, T. T. Xie, S. Paszczynski, and B. M. Wilamowski, "Advantages of radial basis function networks for dynamic system design," IEEE Trans. Ind. Electron., vol. 58, no. 12, pp. 5438-5440, Dec. 2012.
28. C. Wang, M. Wang, T. F. Liu, and D. J. Hill, "Learning from ISSmodular adaptive NN control of nonlinear strict-feedback systems," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 10, pp. 1539-1550, Oct. 2012.
29. K. Dalamagkidis, K. P. Valavanis, and L. A. Piegl, "Nonlinear model predictive control with neural network optimization for autonomous autorotation of small unmanned helicopters," IEEE Trans. Control Syst. Technol., vol. 19, no. 4, pp. 818-831, Jul. 2011.
30. H. A. Kingravi, G. Chowdhary, P. A. Vela, and E. N. Johnson, "Reproducing kernel hilbert space approach for the online update of radial bases in neuro-adaptive control," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 7, pp. 1130-1141, Jul. 2012.
31. H. G. Han and J. F. Qiao, "Prediction of activated sludge bulking based on a self-organizing RBF neural network," J. Process. Control, vol. 22, no. 6, pp. 1103-1112, Jul. 2012.
32. M. M. Ignacio Juan, "Robust GRBF static neurocontroller with switch logic for control of robot manipulators," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 7, pp. 1053-1064, Jul. 2012.
33. D. S. Huang and J. X. Du, "A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks," IEEE Trans. Neural Netw., vol. 19, no. 12, pp. 2099-2115, Dec. 2008.
34. M. Z. Hou and X. L. Han, "Constructive approximation to multivariate function by decay RBF neural network," IEEE Trans. Neural Netw., vol. 21, no. 9, pp. 1517-1523, Sep. 2009.
35. J. F. Qiao and H. G. Han, "Identification and modeling of nonlinear dynamical systems using a novel self-organizing RBF-based approach," Automatica, vol. 48, no. 8, pp. 1729-1734, Aug. 2012.
36. M. Cannon, J. Buerger, B. Kouvaritakis, and S. Rakovic, "Robust Tubes in nonlinear model predictive control," IEEE Trans. Autom. Control, vol. 56, no. 8, pp. 1942-1947, Aug. 2011.
37. N. Z. Melanie, N. J. Colin, and M. Manfred, "Real-time suboptimal model predictive control using a combination of explicit MPC and online optimization," IEEE Trans. Autom. Control, vol. 56, no. 7, pp. 1524-1534, Jul. 2011.
38. M. A. Muller and F. Allgoer, "Improving performance in model predictive control: Switching cost functional under average dwell-time," Automatica, vol. 48, no. 2, pp. 402-409, Feb. 2012.
39. B. Alberto, O. Alberto, P. Tomaso, and S. Marco, "Ultra-fast stabilizing model predictive control via canonical piecewise affine approximations," IEEE Trans. Autom. Control, vol. 56, no. 12, pp. 2883-2897, Dec. 2011.
40. M. Cannon, B. Kouvaritakis, S. V. Rakovic, and Q. F. Cheng, "Stochastic Tubes in model predictive control with probabilistic constraints," IEEE Trans. Autom. Control, vol. 56, no. 1, pp. 194-200, Jan. 2011.
41. Y. J. Zhang, T. Y. Chai, H. Wang, J. Fu, L. Y. Zhang, and Y. G. Wang, "An adaptive generalized predictive control method for nonlinear systems based on ANFIS and multiple models," IEEE Trans. Fuzzy Syst., vol. 18, no. 6, pp. 1070-1082, Dec. 2010.
42. J. F. Zhang and X. H. Xia, "A model predictive control approach to the periodic implementation of the solutions of the optimal dynamic resource allocation problem," Automatica, vol. 47, no. 2, pp. 358-362, Feb. 2011.
43. T. Dierks and S. Jagannathan, "Online optimal control of affine nonlinear discrete-time systems with unknown internal dynamics by using timebased policy update," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 7, pp. 1118-1129, Jul. 2012.
44. H. G. Han, Q. L. Chen, and J. F. Qiao, "An efficient self-organizing RBF neural network for water quality predicting," Neural Netw., vol. 24, no. 7, pp. 717-725, Aug. 2011.
45. H. G. Han and J. F. Qiao, "An adaptive computation algorithm for RBF neural network," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 2, pp. 342-347, Feb. 2012.
46. V. Chandramouli, G. Brion, T. R. Neelakantan, and S. Lingireddy, "Backfilling missing microbial concentrations in a riverine database using artificial neural networks," Water Res., vol. 41, no. 1, pp. 217-227, Jan. 2007. (Pubitemid 44828096)
47. C. Panchapakesan, M. Palaniswami, D. Ralph, and C. Manzie, "Effects of moving the centers in an RBF network," IEEE Trans. Neural Netw., vol. 13, no. 6, pp. 1299-1307, Nov. 2002. (Pubitemid 35428520)
48. M. Bortman and M. Aladjem, "A growing and pruning method for radial basis function networks," IEEE Trans. Neural Netw., vol. 20, no. 6, pp. 1039-1045, Jun. 2009.
49. R. A. Silver, "Neuronal arithmetic," Nature Rev. Neurosci., vol. 11, no. 7, pp. 474-489, Jul. 2010.
50. R. M. Ransohoff and A. E. Cardona, "The myeloid cells of the central nervous system parenchyma," Nature, vol. 468, pp. 253-262, Nov. 2010.
51. S. H. Zak, Systems and Control. New York, NY, USA: Oxford Univ. Press, 2003.
52. J. J. Slotine and W. Li, Applied Nonlinear Control. Englewood Cliffs, NJ, USA: Prentice-Hall, 1991.