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Longitudinal wind field prediction based on DDPG

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Abstract

Parafoil is a kind of flexible aircraft, which has strong load capacity and long flight time but is easily disturbed by wind field. In the homing stage of parafoil from a high-altitude wind field to a low-altitude wind field, the low-altitude wind field is unmeasurable, which has a bad effect on the parafoil trajectory planning. To solve this problem, longitudinal prediction of the low-altitude wind field is proposed by intelligent processing of the high-altitude wind field data estimated by the parafoil. Since spatial wind field has the characteristics of hierarchical recursion and dynamic change, a deep deterministic policy gradient prediction model with Elman neural network as the core is proposed in this paper. Finally, the prediction effect of high accuracy and low-level precision attenuation, which provide reference information for the parafoil track planning, is realized.

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References

  1. Prakash O, Ananthkrishnan N (2004) Aerodynamics, longitudinal stability and glide performance of parafoil/payload system preliminary project report. Ph.D. thesis, 08 2004

  2. Sun H, Sun QL, Chen ZQ, Tao J, Luo SZ, Zeng XY, Teng SH, Zhou P (2020) An optimal-multiphase homing methodology for powered parafoil systems. Optimal Control Appl Methods 41(03):2020

    MathSciNet  MATH  Google Scholar 

  3. Zhao L, Jianyi K (2009) Path planning of parafoil system based on particle swarm optimization. In: 2009 international conference on computational intelligence and natural computing, vol 1, pp 450–453

  4. Yi GX, Wang SY (2017) Study on adaptive homing algorithm for parafoil system under the condition of unknown wind field. Transducer & Microsystem Technologies

  5. Nitin M, Dineshkumar M, Arun Kishore WC (2013) Parafoil trajectory comparison for optimal control and proportional controller. In: 2013 International Conference on Control Communication and Computing (ICCC), pp 227–232

  6. Inken P (2008) Comparative in-flight and wind tunnel investigation of the development of natural and controlled disturbances in the laminar boundary layer of an airfoil. Exp Fluids 44:961–972

    Article  Google Scholar 

  7. Rodriguez L, Cobano JA, Ollero A (2016) Wind field estimation and identification having shear wind and discrete gusts features with a small UAS. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 5638–5644

  8. Tao J, Sun QL, Tan PL, Wu WN, He YP (2017) Homing control of parafoil systems in unknown wind environments. Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica 38(5):2017

    Google Scholar 

  9. Feng G, Zhang GB, Zhang MY, Zhang QB (2019) Study on trajectory planning of parafoil homing under variable wind field, pp 294–2944

  10. Luo SZ, Sun QL, Tan PL, Sun MW, Chen ZQ, He YP (2019) Soft landing control of unmanned powered parafoils in unknown wind environments. Proc Inst Mech Eng 233(3):949–968

    Article  Google Scholar 

  11. Gnana Sheela K (2011) Computing models for wind speed prediction in renewable energy systems. IJCA Spec Issue Comput Sci New Dimens Perspect 3:108–111

    Google Scholar 

  12. Guo ZH, Zhao WG, Lu HY, Wang JZ (2012) Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renew Energy 37(1):241–249

    Article  Google Scholar 

  13. Akaike H (1974) New look at statistical-model identification. IEEE Trans Autom Control AC19(6):716–723

    Article  MathSciNet  Google Scholar 

  14. Torres JL, Garcia A, Blas MD, Francisco AD (2005) Forecast of hourly average wind speed with ARMA models in Navarre (Spain). Sol Energy 79(1):65–77

    Article  Google Scholar 

  15. Zhou S, Yuan J, Song Z, Tang J, Kang L (2013) Wind signal forecasting based on system identification toolbox of MATLAB. https://doi.org/10.1109/ISDEA.2012.388

  16. Al-Yahyai S, Gastli A, Charabi Y (2013) Probabilistic wind speed forecast for wind power prediction using pseudo ensemble approach. In: 2012 IEEE international conference on Power and Energy (PECon), pp 127–132

  17. Shi WH, Chen J (2011) Reliability assessment of interconnected generation systems based on hourly wind speed probability model. Energy Procedia 12:819–827

    Article  Google Scholar 

  18. Cao Q, Ewing BT, Thompson MA (2012) Forecasting wind speed with recurrent neural networks. Eur J Oper Res 221(1):148–154

    Article  MathSciNet  Google Scholar 

  19. Lahouar A, Ben Hadj Slama J (2014) Wind speed and direction prediction for wind farms using support vector regression, pp 1–6

  20. Dowell J, Weiss S, Infield D (2014) Spatio-temporal prediction of wind speed and direction by continuous directional regime. In: 2014 international conference on Probabilistic Methods Applied to Power Systems (PMAPS), pp 1–5

  21. Damousis IG, Alexiadis MC, Theocharis JB, Dokopoulos PS (2004) A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation. IEEE Trans Energy Convers 19(2):352–361

    Article  Google Scholar 

  22. Razavi S, Tolson BA (2011) A new formulation for feedforward neural networks. IEEE Trans Neural Netw 22(10):1588–1598

    Article  Google Scholar 

  23. Samala R, Chan HP, Hadjiiski L, Helvie M, Richter C (2020) Generalization error analysis for deep convolutional neural network with transfer learning in breast cancer diagnosis. Phys Med Biol 65(03):2020

    Google Scholar 

  24. Ali M, Sarwar A, Sharma V, Suri J (2017) Artificial neural network based screening of cervical cancer using a hierarchical modular neural network architecture (HMNNA) and novel benchmark uterine cervix cancer database. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3246-7

    Article  Google Scholar 

  25. Mohandes M, Halawani T (1998) A neural networks approach for wind speed prediction. Renew Energy 13:345–354

    Article  Google Scholar 

  26. Hayashi M, Kermanshahi B (2001) Application of artificial neural network for wind speed prediction and determination of wind power generation output. In: Proceedings of ICEE

  27. Fonte PM, Silva GX, Quadrado JC (2005) Wind speed prediction using artificial neural networks. WSEAS Trans Syst 4:134–139

    Google Scholar 

  28. Sreelakshmi K, RamakanthKumar P (2008) Neural networks for short term wind speed prediction. World Acad Sci Eng Technol Int J Comput Electr Autom Control Inf Eng 42:721–725

    Google Scholar 

  29. Kanna B, Singh S (2013) Improved RNN and AWNN based wind power forecast using meteorological inputs

  30. Hou C, Han H, Liu ZJ, Su M (2019) A wind direction forecasting method based on z\_score normalization and long short\_term memory, pp 172–176

  31. Hinich MJ (2010) Time series analysis by state space methods. OUP Cat 47(3):373

    MathSciNet  Google Scholar 

  32. Fujita TT (1990) Downbursts: meteorological features and wind field characteristics. J Wind Eng Ind Aerodyn 36(1):75–86

    Article  Google Scholar 

  33. Chen Z, Li CG, Zhang ZT, Liao JH (2008) Model test study of wind field characteristics of long-span bridge site in mountainous valley terrain. J Exp Fluid Mech 22:54–59+67

  34. Iqbal U, Wah TY, Ur Rehman MH, Shah JH (2020) Prediction analytics of myocardial infarction through model-driven deep deterministic learning. Neural Comput Appl 32(1):1–20

    Google Scholar 

  35. Su RP, Wu FG, Zhao JS (2019) Deep reinforcement learning method based on DDPG with simulated annealing for satellite attitude control system, pp 390–395

  36. Ghosh S, Tudu B, Bhattacharyya N, Bandyopadhyay R (2017) A recurrent Elman network in conjunction with an electronic nose for fast prediction of optimum fermentation time of black tea. Neural Comput Appl 31(02):2019

    Google Scholar 

  37. Dou CX, Qi H, Luo W, Zhang YM (2018) Elman neural network based short-term photovoltaic power forecasting using association rules and kernel principal component analysis. J Renew Sustain Energy 10:043501

    Article  Google Scholar 

  38. Zheng YM, Chen ZQ, Huang ZY, Sun MW, Sun QL (2020) Active disturbance rejection controller for multi-area interconnected power system based on reinforcement learning. Neurocomputing 04:2020

    Google Scholar 

  39. Joseph W, Frederic M, Cameron B, Simon D (2020) Improved reinforcement learning with curriculum. Expert Syst Appl 158(113515):2020

    Google Scholar 

  40. Zhang Y, Zhang ZF, Yang QY, An D, Li DH, Li C (2020) EV charging bidding by multi-DQN reinforcement learning in electricity auction market. Neurocomputing 397(404–414):2020

    Google Scholar 

  41. Schulman J, Levine S, Moritz P, Jordan MI, Abbeel P (2017) Trust region policy optimization

  42. Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O (2017) Proximal policy optimization algorithms

  43. Sutton R, Barto A (1998) Reinforcement learning: an introduction

  44. Jin LQ, Tian DY, Zhang QX, Wang JJ (2020) Optimal torque distribution control of multi-axle electric vehicles with in-wheel motors based on DDPG algorithm. Energies 13:1331

    Article  Google Scholar 

  45. Tan P, Sun M, Sun Q, Chen Z (2020) Dynamic modeling and experimental verification of powered parafoil with two suspending points. IEEE Access PP(99):1

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61973172, 61973175 and 62003177), the key Technologies Research and Development Program of Tianjin (Grant No. 19JCZDJC32800), this project also funded by China Postdoctoral Science Foundation (Grant No. 2020M670633).

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Authors and Affiliations

  1. College of Artificial Intelligence, Nankai University, Tianjin, China

    Zhenping Yu, Panlong Tan, Qinglin Sun, Hao Sun & Zengqiang Chen

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  1. Zhenping Yu
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  2. Panlong Tan
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  3. Qinglin Sun
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  4. Hao Sun
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  5. Zengqiang Chen
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Correspondence to Panlong Tan.

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Yu, Z., Tan, P., Sun, Q. et al. Longitudinal wind field prediction based on DDPG. Neural Comput & Applic 34, 227–239 (2022). https://doi.org/10.1007/s00521-021-06356-1

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