Acknowledgement
This article was supported by Enterprise Science and Technology Commissioner Project of Tianjin (20YDTPJC00340), Tianjin University and Technology Development Fund Project (2018KJ227), and Innovation and Entrepreneurship Training Program (202110069054).
References
- Hou, C., Qiao, T., Qiao, M.: Research on audio-visual detection method for conveyor belt longitudinal tea. IEEE Access. 7, 120202-120213 (2019) https://doi.org/10.1109/access.2019.2937660
- Yasutomii, A.Y.: Localization of inspection device along belt conveyors with multiple branches using deep neural networks. IEEE Robot. Autom. Lett. 5(2), 2921-2928 (2020) https://doi.org/10.1109/lra.2020.2974709
- Ribeiro, R.G., Junior, J.R.C.: Unmanned aerial vehicle location routing problem with charging stations for belt conveyor inspection system in the mining industry. IEEE Trans. Intell. Transp. Syst. 21(10), 4186-4195 (2020) https://doi.org/10.1109/tits.2019.2939094
- de Almeida, A.T.: Energy-efficient motor systems in the industrial and in the services sectors in the European Union: characterisation, potentials, barriers and policies. Energy 28(07), 3673-3690 (2003) https://doi.org/10.1016/S0360-5442(02)00160-3
- He, D., Pang, Y., Lodewijks, G.: Determination of acceleration for belt conveyor speed control in transient operation. Int. J. Eng. Technol. 8(05), 485-485 (2012)
- Daijie, H., Pang, Y., Lodewijks, G.: Speed control of belt conveyors during transient operation. Powder Technol. 301(07), 622-631 (2016) https://doi.org/10.1016/j.powtec.2016.07.004
- Zhu, H., Zhu, W.D., Fan, W.: Dynamic modeling, simulation and experiment of power transmission belt drives: a systematic review. J. Sound Vib. 491(20), 1-41 (2021)
- Gao, Y., Qiao, T., Zhang, H., Yang, Y., Xiong, X.: A contactless measuring speed system of belt conveyor based on optical fow techniques. IEEE Access. 7, 121646-121654 (2019) https://doi.org/10.1109/access.2019.2937317
- Chen, W., Li, X.: Model predictive control based on reduced order models applied to belt conveyor system. ISA Trans. 65(11), 350-360 (2016) https://doi.org/10.1016/j.isatra.2016.09.007
- GENEVA ISO. Continuous mechanical equipment-belt conveyors with idlers-calculation of operating power and tensile forces [S]. ISO 5048, (1989).
- BERLIN, DIN. Continuous conveyors-belt conveyors for loose bulk materials-basis for calculation and dimensioning [S]. DIN 22101 (2002).
- TOKYO, JIS. Rubber belt conveyors with carrying idlers-calculation of operating power and tensile forces [S]. JIS B 8805, (1992).
- Luo, J., Shen, Y.: Energy efficiency optimization of belt conveyor for material problem [C]. In: Proceedings of the IEEE International Conference on Information and Automation. Lijiang, China, pp 122-127 (2015)
- Yunfei, M., Taiang, Y.: Optimal scheduling method for belt conveyor system in coal mine considering silo virtual energy storage. Appl. Energy. 275, 115368 (2020) https://doi.org/10.1016/j.apenergy.2020.115368
- Zhang, S., Xia, X.: Modeling and energy efficiency optimization of belt conveyors. Appl. Energy 88(9), 3061-3071 (2021) https://doi.org/10.1016/j.apenergy.2011.03.015
- Bo, Y., Tao, Y., Hongchun, S., Jun, D., Lin, J.: Robust sliding-mode control of Wind energy conversion systems for optimal power extraction via nonlinear perturbation observers. Appl. Energy. 210, 711-723 (2018) https://doi.org/10.1016/j.apenergy.2017.08.027
- Li, S.Z., Wang, H.P., Tian, Y., Aitouch, A., Klein, J.: Direct power control of DFIG wind turbine systems based on an intelligent proportional-integral sliding mode control. ISA Trans. 64, 431-439 (2016) https://doi.org/10.1016/j.isatra.2016.06.003
- Ebrahimkhani, S.: Robust fractional order sliding mode control of doubly-fed induction generator (DFIG)-based wind turbines. ISA Trans. 63, 343-354 (2016) https://doi.org/10.1016/j.isatra.2016.03.003
- Liu, Y., Wu, Q.H., Zhou, X.X., Jiang, L.: Perturbation observer based multiloop control for the DFIG-WT in multimachine power system. IEEE Trans. Power Syst. 29(6), 2905-2915 (2014) https://doi.org/10.1109/TPWRS.2014.2308900
- Bo, Y., Jingbo, W., Junting, W.: Robust fractional-order PID control of supercapacitor energy storage systems for distribution network applications: a perturbation compensation based approach. J. Clean. Prod. 279, 123362 (2020) https://doi.org/10.1016/j.jclepro.2020.123362
- Suleimenov, K.: Disturbance observer-based integral sliding mode control for wind energy conversion systems. Wind Energy 23(4), 1026-1047 (2020) https://doi.org/10.1002/we.2471
- Ning, G.: Finite control set model predictive control integrated with disturbance observer for battery energy power conversion system. J. Power Electron. 21(2), 342-353 (2021) https://doi.org/10.1007/s43236-020-00197-2
- Wei, Z., Hu, J., He, H., Li, Y., Xiong, B.: Load current and state-of-charge coestimation for current sensor-free lithium-ion battery. IEEE Trans. Power Electron. 36(10), 10970-10975 (2021) https://doi.org/10.1109/TPEL.2021.3068725
- Hu, J., He, H., Wei, Z., Li, Y.: Disturbance-immune and aging-robust internal short circuit diagnostic for lithium-ion battery. IEEE Trans. Ind. Electron. (2021). https://doi.org/10.1109/TIE.2021.3063968
- Shen, Y., Xia, X.: Adaptive parameter estimation for an energy model of belt conveyor with DC motor. Asian J. Control. 16(4), 1122-1132 (2014) https://doi.org/10.1002/asjc.776
- Wu, J., Wei, Z., Liu, K., Quan, Z., Li, Y.: Battery-involved energy management for hybrid electric bus based on expert-assistance deep deterministic policy gradient algorithm. IEEE Trans. Veh. Technol. 69(11), 12786-12796 (2020) https://doi.org/10.1109/tvt.2020.3025627
- Wei, Z., Quan, Z., Wu, J., Li, Y., Pou, J., Zhong, H.: Deep deterministic policy gradient-DRL enabled multiphysics-constrained fast charging of lithium-ion battery. IEEE Trans. Ind. Electron. (2021). https://doi.org/10.1109/TIE.2021.3070514
- Wang, S., Diao, R., Xu, C.: On multi-event co-calibration of dynamic model parameters using soft actor-critic. IEEE Trans. Power Syst. 36(01), 521-524 (2021) https://doi.org/10.1109/TPWRS.2020.3030164
- Qi, X.: Rotor resistance and excitation inductance estimation of an induction motor using deep-Q-learning algorithm. Eng. Appl. Artif. Intell. 72, 67-79 (2018) https://doi.org/10.1016/j.engappai.2018.03.018
- Xing, Qi., Qian, Z.: Data-driven induction motor parameters offine identifcation method based on actor-critic framework. Trans. China Electrotech. Soc. 34(09), 1875-1885 (2019)
- Yang, H., Xie, X.: An actor-critic deep reinforcement learning approach for transmission scheduling in cognitive internet of things systems. IEEE Syst. J. 14(02), 51-60 (2020) https://doi.org/10.1109/JSYST.2019.2891520
- Wang, S.: On multi-event co-calibration of dynamic model parameters using soft actor-critic. IEEE Trans. Power Syst. 36(01), 521-524 (2021) https://doi.org/10.1109/TPWRS.2020.3030164
- Han, M.: Actor-critic reinforcement learning for control with stability guarantee. IEEE Robot. Autom. Lett. 5(4), 6217-6224 (2020) https://doi.org/10.1109/lra.2020.3011351