Neural Computing and Applications

, Volume 29, Issue 9, pp 585–596 | Cite as

A precise BP neural network-based online model predictive control strategy for die forging hydraulic press machine

  • Y. C. Lin
  • Dong-Dong Chen
  • Ming-Song Chen
  • Xiao-Min Chen
  • Jia Li
Original Article


The time variance and nonlinearity of forging processes pose great challenges to high-quality production. In this study, a one-step-ahead model predictive control (MPC) strategy based on backpropagation (BP) neural network is proposed for the precise forging processes. Two online updated BP neural networks, predictive neural network (PNN) and control neural network (CNN), are developed to accurately control the die forging hydraulic press machine. The PNN and CNN are utilized to predict the output (the velocity of upper die) and determine the input (the oil pressure of driven cylinders), respectively. The weights of neural networks are initially trained offline and then updated online according to an error backpropagation algorithm. In the proposed control strategy, only the input and output are required, which makes the forging process easy to be controlled. In addition, because of the generalized ability and adaptability of neural networks, the proposed predictive controller can well deal with the time variance and nonlinearity of forging process. Two forging experiments demonstrate the feasibility and effectiveness of the proposed strategy. Moreover, comparing the proposed MPC strategy with the traditional MPC approach and PID controller, it can be found that the proposed MPC strategy is the most effective control approach for the practical forging process.


BP neural networks Model predictive control Forging process 



This work was supported by the National Natural Science Foundation Council of China (Grant No. 51375502), the National Key Basic Research Program (Grant No. 2013CB035801), the Project of Innovation-driven Plan in Central South University (Grant No. 2016CX008), the Natural Science Foundation for Distinguished Young Scholars of Hunan Province (Grant No. 2016JJ1017), Program of Chang Jiang Scholars of Ministry of Education (No. Q2015140), and the Hunan Provincial Innovation Research Funds for Postgraduate (Grant No. CX2016B045), China.


  1. 1.
    Lin YC, Chen XM (2011) A critical review of experimental results and constitutive descriptions for metals and alloys in hot working. Mater Des 32(4):1733–1759CrossRefGoogle Scholar
  2. 2.
    Lin YC, Wu XY (2015) A new method for controlling billet temperature during isothermal die forging of a complex superalloy casing. J Mater Eng Perform 24(9):3549–3557MathSciNetCrossRefGoogle Scholar
  3. 3.
    Azari A, Poursina M, Poursina D (2014) Radial forging force prediction through MR, ANN, and ANFIS models. Neural Comput Appl 25(3–4):849–858CrossRefGoogle Scholar
  4. 4.
    Lu XJ, Huang MH (2012) System-decomposition-based multilevel control for hydraulic press machine. IEEE Trans Ind Electron 59(4):1980–1987CrossRefGoogle Scholar
  5. 5.
    Zhu PH, Zhang LH, Zhou R, Chen LH, Yu B, Xie QZ (2012) A novel sensitivity analysis method in structural performance of hydraulic press. Math Probl Eng 2012:1–21zbMATHGoogle Scholar
  6. 6.
    Lin YC, Chen MS, Zhong J (2008) Microstructural evolution in 42CrMo steel during compression at elevated temperatures. Mater Lett 62(14):2132–2135CrossRefGoogle Scholar
  7. 7.
    Bobbili R, Madhu V (2015) An investigation into hot deformation characteristics and processing maps of high-strength armor steel. J Mater Eng Perform 24(12):4728–4735CrossRefGoogle Scholar
  8. 8.
    Chen F, Cui ZS, Chen J (2014) Prediction of microstructural evolution during hot forging. Manuf Rev 1:6Google Scholar
  9. 9.
    Kumar SSS, Raghu T, Bhattacharjee PP, Rao GA, Borah U (2016) Strain rate dependent microstructural evolution during hot deformation of a hot isostatically processed nickel base superalloy. J Alloys Compd 681:28–42CrossRefGoogle Scholar
  10. 10.
    Lin YC, Li LT, Xia YC, Jiang YQ (2013) Hot deformation and processing map of a typical Al–Zn–Mg–Cu alloy. J Alloys Compd 550:438–445CrossRefGoogle Scholar
  11. 11.
    Kumar SSS, Raghu T, Bhattacharjee PP, Rao GA, Borah U (2015) Constitutive modeling for predicting peak stress characteristics during hot deformation of hot isostatically processed nickel-base superalloy. J Mater Sci 50:6444–6456CrossRefGoogle Scholar
  12. 12.
    Chen F, Liu J, Ou HG, Lu B, Cui ZS, Long H (2015) Flow characteristics and intrinsic workability of IN718 superalloy. Mater Sci Eng, A 642:279–287CrossRefGoogle Scholar
  13. 13.
    Kotkunde N, Srinivasan S, Krishna G, Gupta AK, Singh SK (2016) Influence of material models on theoretical forming limit diagram prediction for Ti-6Al-4 V alloy under warm condition. Trans Nonferrous Met Soc China 26:736–746CrossRefGoogle Scholar
  14. 14.
    Bobbili R, Madhu VGOGIAAK (2014) Neural network modeling to evaluate the dynamic flow stress of high strength armor steels under high strain rate compression. Def Technol 10(4):334–342CrossRefGoogle Scholar
  15. 15.
    Wen DX, Lin YC, Li HB, Chen XM, Deng J, Li LT (2014) Hot deformation behavior and processing map of a typical Ni-based superalloy. Mat Sci Eng A 591:183–192CrossRefGoogle Scholar
  16. 16.
    Pandian SR, Takemura F, Hayakawa Y, Kawamura S (2002) Pressure observer-controller design for pneumatic cylinder actuators. IEEE-ASME Trans Mech 7(4):490–499CrossRefGoogle Scholar
  17. 17.
    Zheng JM, Zhao SD, Wei SG (2009) Application of self-tuning fuzzy PID controller for a SRM direct drive volume control hydraulic press. Control Eng Pract 17(12):1398–1404CrossRefGoogle Scholar
  18. 18.
    Lin YC, Chen XM, Wen DX, Chen MS (2014) A physically-based constitutive model for a typical nickel-based superalloy. Comput Mater Sci 83:282–289CrossRefGoogle Scholar
  19. 19.
    Cho SJ, Lee JC, Jeon YH, Jeon JW (2009) The development of a position conversion controller for hydraulic press systems. In: IEEE international conference on robotics and biomimetics, pp 2019–2022Google Scholar
  20. 20.
    Zhou YC, Liu SJ, Liu ZW, Huang MH (2008) Hydraulic position holding system of a huge water press based on iterative learning control. Mech Sci Technol Aerosp Eng 27:1130–1133Google Scholar
  21. 21.
    Ho TH, Ahn KK (2012) Speed control of a hydraulic pressure coupling drive using an adaptive fuzzy sliding-mode control. IEEE-ASME Trans Mech 17(5):976–986CrossRefGoogle Scholar
  22. 22.
    Lu XJ, Li YB, Huang MH (2013) Operation-region-decomposition-based singular value decomposition/neural network modeling method for complex hydraulic press machines. Ind Eng Chem Res 52(48):17221–17228CrossRefGoogle Scholar
  23. 23.
    Dong JR, Zheng CY, Kan GY, Zhao M, Wen J, Yu J (2015) Applying the ensemble artificial neural network-based hybrid data-driven model to daily total load forecasting. Neural Comput Appl 26(3):603–611CrossRefGoogle Scholar
  24. 24.
    Das P, Banerjee I (2011) An hybrid detection system of control chart patterns using cascaded SVM and neural network–based detector. Neural Comput Appl 20(2):287–296CrossRefGoogle Scholar
  25. 25.
    Awan SM, Aslam M, Khan ZA, Saeed H (2014) An efficient model based on artificial bee colony optimization algorithm with Neural Networks for electric load forecasting. Neural Comput Appl 25(7–8):1967–1978CrossRefGoogle Scholar
  26. 26.
    Alberti N, Di Lorenzo R, Micari F, Teti R, Buonadonna P, Manzoni A (1998) Intelligent computation techniques for process planning of cold forging. J Intell Manuf 9(4):353–359CrossRefGoogle Scholar
  27. 27.
    Wu X, Shen J, Li Y, Lee KY (2014) Fuzzy modeling and stable model predictive tracking control of large-scale power plants. J Process Contr 24(10):1609–1626CrossRefGoogle Scholar
  28. 28.
    Rahmani R, Langeroudi NM, Yousefi R, Mahdian M, Seyedmahmoudian M (2014) Fuzzy logic controller and cascade inverter for direct torque control of IM. Neural Comput Appl 25(3–4):879–888CrossRefGoogle Scholar
  29. 29.
    Wong PK, Tam LM, Li K, Vong CM (2010) Engine idle-speed system modelling and control optimization using artificial intelligence. Proc Inst Mech Eng D J Automob Eng 224:55–72CrossRefGoogle Scholar
  30. 30.
    Rosillo R, Giner J, de la Fuente D (2014) The effectiveness of the combined use of VIX and support vector machines on the prediction of S&P 500. Neural Comput Appl 25(2):321–332Google Scholar
  31. 31.
    Ciccazzo A, Di Pillo G, Latorre V (2014) Support vector machines for surrogate modeling of electronic circuits. Neural Comput Appl 24(1):69–76CrossRefGoogle Scholar
  32. 32.
    Vazquez S, Leon J, Franquelo LG, Rodriguez J, Young H, Marquez A, Zanchetta P (2014) Model predictive control: a review of its applications in power electronics. IEEE Ind Electron Mag 8(1):16–31CrossRefGoogle Scholar
  33. 33.
    Dubljevic S (2010) Model predictive control of Kuramoto-Sivashinsky equation with state and input constraints. Chem Eng Sci 65(15):4388–4396CrossRefGoogle Scholar
  34. 34.
    Wong PK, Wong HC, Vong CM, Xie ZC, Huang SJ (2016) Model predictive engine air-ratio control using online sequential extreme learning machine. Neural Comput Appl 27(1):79–92CrossRefGoogle Scholar
  35. 35.
    Lee JH (2011) Model predictive control: review of the three decades of development. Int J Control Autom 9(3):415–424CrossRefGoogle Scholar
  36. 36.
    Camacho EF, Bordons C (2012) Model predictive control in the process industry. Springer, London, pp 3–15Google Scholar
  37. 37.
    Beal CE, Gerdes JC (2013) Model predictive control for vehicle stabilization at the limits of handling. IEEE Trans Control Syst Technol 21(4):1258–1269CrossRefGoogle Scholar
  38. 38.
    Hermansson AW, Syafiie S (2015) Model predictive control of pH neutralization processes: a review. Control Eng Pract 45:98–109CrossRefGoogle Scholar
  39. 39.
    Ellis M, Christofides PD (2014) Integrating dynamic economic optimization and model predictive control for optimal operation of nonlinear process systems. Control Eng Pract 22:242–251CrossRefGoogle Scholar
  40. 40.
    Bumroongsri P, Kheawhom S (2012) An ellipsoidal off-line model predictive control strategy for linear parameter varying systems with applications in chemical processes. Syst Control Lett 61(3):435–442MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Liu L, Huang B, Dubljevic S (2014) Model predictive control of axial dispersion chemical reactor. J Process Control 24(11):1671–1690CrossRefGoogle Scholar
  42. 42.
    Venayagamoorthy GK, Rohrig K, Erlich I (2012) One step ahead: short-term wind power forecasting and intelligent predictive control based on data analytics. IEEE Power Energy Mag 10(5):70–78CrossRefGoogle Scholar
  43. 43.
    Xiao ZH, Meng SL, Lu N, Malik OP (2015) One-step-ahead predictive control for hydroturbine governor. Math Probl Eng. doi: 10.1155/2015/382954 Google Scholar
  44. 44.
    Lim JS, Park C, Han J, Lee YI (2014) Robust Tracking control of a three-phase DC–AC inverter for UPS applications. IEEE T Ind Electron 61(8):4142–4151CrossRefGoogle Scholar
  45. 45.
    Wang T, Gao HJ, Qiu JB (2016) A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control. IEEE Trans Neural Net Learn 27(2):416–425MathSciNetCrossRefGoogle Scholar
  46. 46.
    Cheng L, Liu WC, Hou ZG, Yu JZ, Tan M (2015) Neural-network-based nonlinear model predictive control for piezoelectric actuators. IEEE Trans Ind Electron 62(12):7717–7727CrossRefGoogle Scholar
  47. 47.
    Daosud W, Jariyaboon K, Kittisupakorn P, Hussain MA (2016) Neural network based model predictive control of batch extractive distillation process for improving purity of acetone. Eng J 20(1):47–59CrossRefGoogle Scholar
  48. 48.
    Tan GZ, Hao HQ, Wang YD (2011) Real time turning flow estimation based on model predictive control. Inf Technol Artif Intell Conf 1:356–360Google Scholar

Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Y. C. Lin
    • 1
    • 2
    • 3
  • Dong-Dong Chen
    • 1
    • 2
  • Ming-Song Chen
    • 1
    • 2
  • Xiao-Min Chen
    • 1
    • 2
  • Jia Li
    • 1
    • 2
  1. 1.School of Mechanical and Electrical EngineeringCentral South UniversityChangshaChina
  2. 2.State Key Laboratory of High Performance Complex ManufacturingChangshaChina
  3. 3.Light Alloy Research InstituteCentral South UniversityChangshaChina

Personalised recommendations