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A new method of applying data engine technology to realize neural network control. (English) Zbl 07257163
Summary: This paper presents a novel diagonal recurrent neural network hybrid controller based on the shared memory of real-time database structure. The controller uses Data Engine (DE) technology, through the establishment of a unified and standardized software architecture and real-time database in different control stations, effectively solves many problems caused by technical standard, communication protocol, and programming language in actual industrial application: the advanced control algorithm and control system co-debugging difficulties, algorithm implementation and update inefficiency, and high development and operation and maintenance costs effectively fill the current technical gap. More importantly, the control algorithm development uses a unified visual graphics configuration programming environment, effectively solving the problem of integrated control of heterogeneous devices; and has the advantages of intuitive configuration and transparent data processing process, reducing the difficulty of the advanced control algorithms debugging in engineering applications. In this paper, the application of a neural network hybrid controller based on DE in motor speed measurement and control system shows that the system has excellent control characteristics and anti-disturbance ability, and provides an integrated method for neural network control algorithm in a practical industrial control system, which is the major contribution of this article.
68 Computer science
93 Systems theory; control
Full Text: DOI
[1] Namazov, M.; Basturk, O.; DC motor position control using fuzzy proportional-derivative controllers with different defuzzication methods; TJFS: 2010; Volume 1 ,36-54.
[2] Dotoli, M.; Fay, A.; Mariagrazia, M.; Seatzu, C.; Advanced control in factory automation: A survey; Int. J. Prod. Res.: 2017; Volume 55 ,1243-1259.
[3] Bauer, M.; Craig, I.K.; Economic assessment of advanced process control—A survey and framework; J. Process Control: 2008; Volume 18 ,2-18.
[4] Shao, G.D.; Latif, H.; Carla, M.V.; Denno, P.; Standards-based integration of advanced process control and optimization; J. Ind. Inf. Integr.: 2019; Volume 13 ,1-12.
[5] Mahmoud, M.S.; Sabih, M.; Elshafei, M.; Using OPC technology to support the study of advanced process control; ISA Trans.: 2015; Volume 55 ,155-167.
[6] Wu, M.L.; Wu, M.; Huang, J.; Intelligent control system of water level for boiler drum based on OPC and MATLAB; Proceedings of the 30th Chinese Control Conference (CCC): ; ,4461-4464.
[7] Huang, Y.; Yan, L.X.; Design of OPC Client Based on. NET for Advanced Control System; Adv. Mater. Res.: 2014; Volume 1037 ,334-338.
[8] Jain, L.C.; Seera, M.; Lim, C.P.; Balasubramaniam, P.; A review of online learning in supervised neural networks; Neural Comput. Appl.: 2014; Volume 25 ,491-509.
[9] Hou, Z.G.; Cheng, L.; Tan, M.; Decentralized robustad aptive control for the multi agent system consensus problem using neural networks; IEEE Trans. Syst. Man Cybern. Syst. Part B Cybern.: 2009; Volume 39 ,636-647.
[10] Zhang, H.W.; Lewis, F.L.; Adaptive cooperative tracking control of higher-order nonlinear systems with unknown dynamics; Automatica: 2012; Volume 48 ,1432-1439. · Zbl 1348.93144
[11] Das, A.; Lewis, F.L.; Cooperative adaptive control for synchronization of second-order systems with unknown nonlinearities; Int. J. Robust Nonlinear: 2011; Volume 21 ,1509-1524. · Zbl 1227.93006
[12] Chen, G.; Song, Y.; Cooperative tracking control of nonlinear multi-agent systems using self-structuring neural networks; IEEE Trans. Neural Netw.: 2014; Volume 25 ,1496-1507.
[13] Campolucci, P.; Uncini, A.; Piazza, F.; Rao, B.D.; On-line learning algorithms for locally recurrent neural networks; IEEE Trans. Neural Netw.: 1999; Volume 10 ,253-271.
[14] Kumar, R.; Smriti, S.; Gupta, J.R.P.; Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using lyapunov stability criterion; ISA Trans.: 2017; Volume 67 ,407-427.
[15] Mosavi, M.R.; A comparative study between performance of recurrent neural network and Kalman filter for DGPS corrections prediction, Signal Processing; Proceedings of the 2004 7th International Conference ICSP’04: ; ,356-359.
[16] Zheng, S.; Ni, W.D.; Research and Implementation of Dynamic Reconfiguration Technology in Distributed Control System; At. Energy Sci. Technol.: 2009; Volume 43 ,724-729.
[17] Zheng, S.; Zhang, W.; Liu, C.L.; Research on the configuration method of mobile robot and its realization; Proceedings of the IEEE Chinese Control and Decision Conference: ; ,2877-2883.
[18] Zheng, Y.L.; Zheng, S.; Cyber Security Risk Assessment for Industrial Automation Platform; Proceedings of the IEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing: ; ,341-344.
[19] Zheng, S.; Application of Platform Integration Control Technology in Ship; Proceedings of the China Command and Control Conference: ; ,77-82.
[20] Gupta, S.; Sharma, S.C.; Selection and application of advance control systems: PLC, DCS and PC-based system; J. Sci. Ind. Res.: 2005; Volume 64 ,249-255.
[21] Zhang, R.; Wu, S.; Gao, F.R.; State Space Model Predictive Control for Advanced Process Operation: A Review of Recent Development; New Results Insight. Ind. Eng. Chem. Res.: 2017; Volume 56 ,5360-5394.
[22] Dumitru, I.; Iulia, N.; Fagarasan, I.; A fuzzy PLC control system for a servomechanism; Intell. Control Syst.: 2010; Volume 43 ,69-74.
[23] Levin, A.U.; Narendra, K.S.; Control of nonlinear dynamical systems using neural networks—Part II: Observability, identification, and control; IEEE Trans. Neural Netw.: 1996; Volume 7 ,30-42.
[24] Ku, C.C.; Lee, K.Y.; Diagonal recurrent neural networks for dynamic systems control; IEEE Trans. Neural Netw.: 1995; Volume 6 ,144-156.
[25] Ku, C.C.; Lee, K.Y.; Nonlinear system identification using diagonal recurrent neural networks; Proceedings of the International Joint Conference on Neural Networks: ; ,839-844.
[26] Kazemy, A.; Hosseini, S.A.; Farrokhi, M.; Second order diagonal recurrent neural network; Proceedings of the IEEE International Symposium on Industrial Electronics: ; ,251-256.
[27] Ahmed-Ali, T.; Kenne, G.; Françoise, L.L.; Identification of nonlinear systems with time-varying parameters using a sliding-neural network observer; Neurocomputing: 2009; Volume 72 ,1611-1620.
[28] Elbuluk, M.E.; Malik, E.; Liu, T.; Iqbal, H.; Neural-network-based model reference adaptive systems for high-performance motor drives and motion controls; IEEE Trans. Ind. Appl.: 2002; Volume 38 ,879-886.
[29] Saad, D.; ; On-Line Learning in Neural Networks: Cambridge, UK 1998; ,59-62.
[30] Humphrey, G.B.; Maier, H.R.; Wu, W.; Mount, N.J.; Dandy, G.C.; Abrahart, R.J.; Dawson, C.W.; Improved validation framework and R-package for artificial neural network models; Environ. Model. Softw.: 2017; Volume 92 ,82-106.
[31] Liu, G.; Liang, J.; Lan, G.; Hao, Q.; Chen, M.; Convolution neutral network enhanced binary sensor network for human activity recognition; Proceedings of the 2016 IEEE SENSORS: ; .
[32] Menghal, P.M.; Jaya Laxmi, A.; Neural network based dynamic simulation of induction motor drive; Proceedings of the 2013 International Conference on Power, Energy and Control (ICPEC): ; ,566-571.
[33] Nguyen, T.L.; Adaptive dynamic programming-based design of integrated neural network structure for cooperative control of multiple MIMO nonlinear systems; Neurocomputing: 2017; Volume 237 ,12-24.
[34] Fernando, J.L.; Godpromesse, K.; Francoise, L.L.; A novel online training neural network-based algorithm for wind speed estimation and adaptive control of PMSG wind turbine system for maximum power extraction; Renew. Energy: 2016; Volume 86 ,38-48.
[35] García, J.; Palomo, F.R.; Luque, A.; Aracil, C.; Quero, J.M.; Carrión, D.; Gámiz, F.; Revilla, P.; Pérez-Tinao, J.; Moreno, M.; Reconfigurable distributed network control system for industrial plant automation; IEEE Trans. Ind. Electron.: 2004; Volume 51 ,1168-1180.
[36] Almeida, J.P.A.; Sinderen, M.V.; Pires, L.F.; Wegdam, M.; Platform-independent Dynamic Reconfiguration of Distributed Applications; Proceedings of the IEEE 10th International Workshop on Future Trends in Distributed Computing Systems (FTDCS 2004): ; ,286-291.
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