Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using lyapunov stability criterion

ISA Transactions

Volumn 67, Issue , 2017, Pages 407-427

  Kumar, Rajesh ^a   Srivastava, Smriti ^a   Gupta, J R P ^a  

^a NETAJI SUBHAS INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Adaptive control; Diagonal recurrent neural network; Inverted pendulum; Lyapunov stability criterion; Multi layer feed forward neural network; Nonlinear dynamical systems; Robotic manipulator

Indexed keywords

CONTROL SYSTEM STABILITY; CONTROLLERS; DYNAMICAL SYSTEMS; FLEXIBLE MANIPULATORS; NONLINEAR DYNAMICAL SYSTEMS; PENDULUMS; RECURRENT NEURAL NETWORKS; ROBOTICS; STABILITY CRITERIA;

ADAPTIVE CONTROL; DIAGONAL RECURRENT NEURAL NETWORKS; INVERTED PENDULUM; LYAPUNOV STABILITY CRITERION; MULTILAYER FEEDFORWARD NEURAL NETWORKS; ROBOTIC MANIPULATORS;

ADAPTIVE CONTROL SYSTEMS;

ARTICLE; FEED FORWARD NEURAL NETWORK; NERVE CELL; RECURRENT NEURAL NETWORK; SIMULATION;

EID: 85010878278     PISSN: 00190578     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isatra.2017.01.022     Document Type: Article

References (62)

1. [1] Antsaklis, P.J., Neural networks for control systems. IEEE Trans Neural Netw 1:2 (1990), 242–244.
2. [2] Wang, T., Gao, H., Qiu, J., A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control. IEEE Trans Neural Netw Learn Syst 27:2 (2016), 416–425.
3. [3] Wei, Q., Liu, D., Neural-network-based adaptive optimal tracking control scheme for discrete-time nonlinear systems with approximation errors. Neurocomputing 149 (2015), 106–115.
4. [4] Hunt, K.J., Sbarbaro, D., Żbikowski, R., Gawthrop, P.J., Neural networks for control systemsa survey. Automatica 28:6 (1992), 1083–1112.
5. [5] Jia C, Li X, Wang K, Ding D. Adaptive control of nonlinear system using online error minimum neural networks, ISA transactions.
6. [6] Cetin, M., Iplikci, S., A novel auto-tuning pid control mechanism for nonlinear systems. ISA Trans 58 (2015), 292–308.
7. [7] Narendra, K.S., Parthasarathy, K., Identification and control of dynamical systems using neural networks. IEEE Trans Neural Netw 1:1 (1990), 4–27.
8. [8] Ku C-C, Lee KY. Diagonal recurrent neural network based control using adaptive learning rates. In: Proceedings of the 31st IEEE Conference on Decision and Control, 1992, IEEE, 1992, pp. 3485–90.
9. [9] Singh, M., Srivastava, S., Gupta, J., Handmandlu, M., Identification and control of a nonlinear system using neural networks by extracting the system dynamics. IETE J Res 53:1 (2007), 43–50.
10. [10] Li, J., Li, S., Chen, X., Yang, J., Rbfndob-based neural network inverse control for non-minimum phase mimo system with disturbances. ISA Trans 53:4 (2014), 983–993.
11. [11] Huang, Y., Na, J., Wu, X., Liu, X., Guo, Y., Adaptive control of nonlinear uncertain active suspension systems with prescribed performance. ISA Trans 54 (2015), 145–155.
12. [12] Savran, A., Tasaltin, R., Becerikli, Y., Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks. ISA Trans 45:2 (2006), 225–247.
13. [13] Bloch, G., Denoeux, T., Neural networks for process control and optimization: two industrial applications. ISA Trans 42:1 (2003), 39–51.
14. [14] Akpan, V.A., Hassapis, G.D., Nonlinear model identification and adaptive model predictive control using neural networks. ISA Trans 50:2 (2011), 177–194.
15. [15] Srivastava, S., Singh, M., Hanmandlu, M., Jha, A.N., New fuzzy wavelet neural networks for system identification and control. Appl Soft Comput 6:1 (2005), 1–17.
16. [16] Srivastava, S., Singh, M., Hanmandlu, M., Jha, A., Modeling of non-linear systems by fwnns and their intelligent control. Int J Adapt control signal Process 19:7 (2005), 505–530.
17. [17] Zhou, Y., Hahn, J., Mannan, M.S., Fault detection and classification in chemical processes based on neural networks with feature extraction. ISA Trans 42:4 (2003), 651–664.
18. [18] Mathiyalagan, K., Park, J.H., Sakthivel, R., Synchronization for delayed memristive bam neural networks using impulsive control with random nonlinearities. Appl Math Comput 259 (2015), 967–979.
19. [19] Patra, J.C., Van den Bos, A., Modeling of an intelligent pressure sensor using functional link artificial neural networks. ISA Trans 39:1 (2000), 15–27.
20. [20] Haykin, S., Network, N., A comprehensive foundation. Neural Netw, 2, 2004.
21. [21] Sharma, R., Kumar, V., Gaur, P., Mittal, A., An adaptive pid like controller using mix locally recurrent neural network for robotic manipulator with variable payload. ISA Trans 62 (2016), 258–267.
22. [22] Zhou, L., Zhang, Y., Global exponential periodicity and stability of recurrent neural networks with multi-proportional delays. ISA Trans 60 (2016), 89–95.
23. [23] Williams, R.J., Zipser, D., A learning algorithm for continually running fully recurrent neural networks. Neural Comput 1:2 (1989), 270–280.
24. [24] Xu, J., Li, C., He, X., Huang, T., Recurrent neural network for solving model predictive control problem in application of four-tank benchmark. Neurocomputing 190 (2016), 172–178.
25. [25] Liu, P., Zeng, Z., Wang, J., Multistability analysis of a general class of recurrent neural networks with non-monotonic activation functions and time-varying delays. Neural Netw 79 (2016), 117–127.
26. [26] Ku, C.-C., Lee, K.Y., Edwards, R., Improved nuclear reactor temperature control using diagonal recurrent neural networks. IEEE Trans Nucl Sci 39:6 (1992), 2298–2308.
27. [27] Lee K, Choi T, Ku C, Park J. Short-term load forecasting using diagonal recurrent neural network. In: Proceedings of the IEEE Second International Forum on Applications of Neural Networks to Power Systems, 1993 ANNPS'93; 1993, pp. 227–32.
28. [28] Funahashi, K.-i., Nakamura, Y., Approximation of dynamical systems by continuous time recurrent neural networks. Neural Netw 6:6 (1993), 801–806.
29. [29] Puskorius G, Feldkamp L. Practical considerations for kalman filter training of recurrent neural networks. In: Proceedings of the IEEE International Conference on Neural Networks, 1993, IEEE, 1993, pp. 1189–95.
30. [30] Condarcure TA, Sundareshan MK. A learning automaton approach to trajectory learning and control system design using dynamic recurrent neural networks. In: Proceedings of the 1994 IEEE International Conference on Neural Networks, 1994. IEEE World Congress on Computational Intelligence, Vol. 4, IEEE, 1994, pp. 2684–9.
31. [31] Ku, C.-C., Lee, K.Y., Diagonal recurrent neural networks for dynamic systems control. IEEE Trans neural Netw 6:1 (1995), 144–156.
32. [32] Jin, L., Nikiforuk, P.N., Gupta, M.M., Approximation of discrete-time state-space trajectories using dynamic recurrent neural networks. IEEE Trans Autom Control 40:7 (1995), 1266–1270.
33. [33] Zhu R, Chai T, Shao C. Robust nonlinear adaptive observer design using dynamic recurrent neural networks. in: American Control Conference, 1997. In: Proceedings of the 1997, Vol. 2, IEEE; 1997, pp. 1096–100.
34. [34] Tsoi, A.C., Back, A., Discrete time recurrent neural network architectures: a unifying review. Neurocomputing 15:3 (1997), 183–223.
35. [35] Chow, T.W., Fang, Y., A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics. IEEE Trans Ind Electron 45:1 (1998), 151–161.
36. [36] Gao, F., Wang, F., Li, M., A simple nonlinear controller with diagonal recurrent neural network. Chem Eng Sci 55:7 (2000), 1283–1288.
37. [37] Lee, C.-H., Teng, C.-C., Identification and control of dynamic systems using recurrent fuzzy neural networks. IEEE Trans fuzzy Syst 8:4 (2000), 349–366.
38. [38] Barabanov, N.E., Prokhorov, D.V., Stability analysis of discrete-time recurrent neural networks. IEEE Trans Neural Netw 13:2 (2002), 292–303.
39. [39] Wang X, Peng G. Modeling and control for pneumatic manipulator based on dynamic neural network. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2003, Vol. 3, IEEE; 2003, pp. 2231–6.
40. [40] Cao, J., Wang, J., Global asymptotic stability of a general class of recurrent neural networks with time-varying delays. IEEE Trans Circuits Syst I: Fundam Theory Appl 50:1 (2003), 34–44.
41. [41] Liu S, Du Y, Li W, Zheng X-l. Sonar array servo system based on diagonal recurrent neural network. In: Proceedings of the IEEE International Conference Mechatronics and Automation, 2005, Vol. 4, IEEE; 2005, pp. 1912–7.
42. [42] Liu C-Y, Li K, Song Z-Y, Song X-L, Wang H-F. Variable flow heating control system based on neural network decoupling. In: Proceedings of the 2007 IEEE International Conference on Grey Systems and Intelligent Services, IEEE; 2007, pp. 860–4.
43. [43] Amiri M, Menhaj MB, Yazdanpanh MJ. A neural-network-based controller for a single-link flexible manipulator: comparison of ffnn and drnn controllers. In: Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), IEEE; 2008, pp. 1686–91.
44. [44] Guo B, Hu L, Bai Y. Pmsm servo system based on dynamic recurrent neural networks pid controller. In: Proceedings of the Power Electronics and Motion Control Conference (IPEMC), 2012 7th International, Vol. 4, IEEE, 2012, pp. 2417–21.
45. [45] Lv Y, Ma J, He DC, Gao X. Diagonal recurrent neural network-based electro-hydraulic servo system control. In: Proceedings of the Applied Mechanics and Materials, Vol. 336, Trans Tech Publ, 2013, pp. 581–4.
46. [46] Han, S.-I., Lee, J.-M., Recurrent fuzzy neural network backstepping control for the prescribed output tracking performance of nonlinear dynamic systems. ISA Trans 53:1 (2014), 33–43.
47. [47] Wang, Z., Zhou, T., Mao, Y., Chen, Q., Adaptive recurrent neural network control of uncertain constrained nonholonomic mobile manipulators. Int J Syst Sci 45:2 (2014), 133–144.
48. [48] Wang, L., Meng, Z., Sun, Y., Guo, L., Zhou, M., Design and analysis of a novel chaotic diagonal recurrent neural network. Commun Nonlinear Sci Numer Simul 26:1 (2015), 11–23.
49. [49] Shuang F, Junzheng W, Wei S, Guangrong C. The research of loading model of eddy current dynamometer based on drnn with double hidden layers. In: Proceedings of the 27th Chinese Control and Decision Conference (2015 CCDC), IEEE, 2015, pp. 2485–2490.
50. [50] Wei, X., Wang, P., Zhao, F., Design of a decoupled ap1000 reactor core control system using digital proportional-integral-derivative (pid) control based on a quasi-diagonal recurrent neural network (qdrnn). Nucl Eng Des 304 (2016), 40–49.
51. [51] Yang W, Zhang D, Fu Y. Research of a diagonal recurrent neural network and artificial neural networks. In: Proceedings of the Computer, Consumer and Control (IS3C), 2016 International Symposium on, IEEE, 2016, pp. 374–7.
52. [52] Han, H.-G., Zhang, L., Hou, Y., Qiao, J.-F., Nonlinear model predictive control based on a self-organizing recurrent neural network. IEEE Trans Neural Netw Learn Syst 27:2 (2016), 402–415.
53. [53] Ljung L. System identification. in: Signal Analysis and Prediction, Springer, 1998, pp. 163–73.
54. [54] Narendra, K.S., Parthasarathy, K., Gradient methods for the optimization of dynamical systems containing neural networks. IEEE Trans Neural Netw 2:2 (1991), 252–262.
55. [55] Ogata K. Discrete-time control systems, Vol. 2, Prentice Hall Englewood Cliffs, NJ, 1995.
56. [56] Narendra, K.S., Parthasarathy, K., Neural networks and dynamical systems. Int J Approx Reason 6:2 (1992), 109–131.
57. [57] Stephanopoulos G, et al. Chemical process control: an introduction to theory and practice.
58. [58] Paul, R.P., Robot manipulators: mathematics, programming, and control: the computer control of robot manipulators. Richard Paul, 1981.
59. [59] Sciavicco, L., Siciliano, B., Modelling and control of robot manipulators. 2012, Springer Science& Business Media.
60. [60] Yang G-B, Donath M. Dynamic model of a one-link robot manipulator with both structural and joint flexibility. In: Proceedings of the 1988 IEEE International Conference on Robotics and Automation, 1988, IEEE; 1988, pp. 476–81.
61. [61] Lilly, J.H., Fuzzy control and identification. 2011, John Wiley & Sons.
62. [62] Sanxiu W, Shengtao J. Adaptive friction compensation of robot manipulator. in: Electronics and Signal Processing, Springer; 2011, pp. 127–134.