Noise-rejection zeroing dynamics for control of industrial agitator tank

Abstract

Agitator tanks are widely used in industrial fields. Improvement in their efficiency is critical to achieving high productivity. That is to say, an agitator tank system should have a short response time to produce a desired reagent with an accurate solution concentration and a moderate liquid level. Therefore, a noise-rejection zeroing dynamics (NRZD) model for the control of the agitator tank based on a neural-dynamics method with anti-noise performance is proposed in this paper. The solution concentration and the liquid level of the agitator tank synthesized by the NRZD model are able to converge to the desired trajectories polluted with different noises. Then, theoretical analyses on the convergence and anti-noise performance of the agitator tank system equipped with the NRZD model are presented. Furthermore, to verify the superiority of the agitator tank system equipped with the NRZD model, we perform tracking trajectories simulations on solution concentration and the liquid level of the agitator tank with different noises. Moreover, the simulation results verify that the NRZD model is more effective than the existing models in the reagent preparation process.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  1. 1.

    Hu, Y., Li, C., Wang, X., Yang, Y., Zhu, H.: 1,3,4-Thiadiazole: synthesis, reactions, and applications in medicinal, agricultural, and materials chemistry. Chem. Rev. 114(10), 5572–5610 (2014)

    Article  Google Scholar 

  2. 2.

    Singh, J., Kaur, L., McCarthy, O.: Factors influencing the physico-chemical, morphological, thermal and rheological properties of some chemically modified starches for food applications-a review. Food Hydrocoll. 21(1), 1–22 (2017)

    Article  Google Scholar 

  3. 3.

    Alves, N., Mano, J.: Chitosan derivatives obtained by chemical modifications for biomedical and environmental applications. Int. J. Biol Macromol. 43(5), 401–414 (2008)

    Article  Google Scholar 

  4. 4.

    Pearse, M.: An overview of the use of chemical reagents in mineral processing. Miner. Eng. 18(2), 139–149 (2005)

    Article  Google Scholar 

  5. 5.

    Fang, J., Ling, X., Sang, Z.: Experimental and numerical studies of the flow field in a stirred tank equipped with multiple side-entering agitators. Chem. Eng. Technol. 34(10), 1619–1629 (2011)

    Article  Google Scholar 

  6. 6.

    Martin, M., Montes, F., Galán, M.: Bubbling process in stirred tank reactors I: Agitator effect on bubble size, formation and rising. Chem. Eng. Sci. 63(12), 3212–3222 (2008)

    Article  Google Scholar 

  7. 7.

    Xie, M., Zhou, G., Meng, S., Wang, B., Du, S.: Numerical simulation of flow property in polymer dissolution tank with inner-outer agitators. Chem. Eng. Sci. 40(10), 50–54 (2012)

    Google Scholar 

  8. 8.

    Nienow, A.: Stirring and stirred-tank reactors. Chem. Ing. Tech. 86(12), 2063–2074 (2015)

    Article  Google Scholar 

  9. 9.

    Wei, L., Jin, L., Yang, C., Chen, K., Li, W.: New noise-tolerant neural algorithms for future dynamic nonlinear optimization with estimation on hessian matrix inversion. IEEE Trans. Syst. Man Cybern. Syst. (2019). https://doi.org/10.1109/TSMC.2019.2916892

    Article  Google Scholar 

  10. 10.

    Xie, Z., Jin, L., Du, X., Xiao, X., Li, H., Li, S.: On generalized RMP scheme for redundant robot manipulators aided with dynamic neural networks and nonconvex bound constraints. IEEE Trans. Ind. Inf. 15(9), 5172–5181 (2019)

    Article  Google Scholar 

  11. 11.

    Jin, L., Xie, Z., Liu, M., Chen, K., Li, C., Yang, C.: Novel joint-drift-free scheme at acceleration level for robotic redundancy resolution with tracking error theoretically eliminated. IEEE/ASME Trans. Mech. (2020). https://doi.org/10.1109/TMECH.2020.3001624

    Article  Google Scholar 

  12. 12.

    Duan, K., Fong, S., Chen, C.P.: Multilayer neural networks-based control of underwater vehicles with uncertain dynamics and disturbances. Nonlinear Dyn. 100(9), 3555–3573 (2020)

    Article  Google Scholar 

  13. 13.

    Wu, L.B., Park, J.H., Xie, X.P., Ren, Y.W., Yang, Z.: Distributed adaptive neural network consensus for a class of uncertain nonaffine nonlinear multi-agent systems. Nonlinear Dyn. 100(2), 1243–1255 (2020)

    Article  Google Scholar 

  14. 14.

    Luo, X., Zhou, M., Li, S., You, Z., Xia, Y., Zhu, Q.: A non-negative latent factor model for large-scale sparse matrices in recommender systems via alternating direction method. IEEE Trans. Syst. Man Cybern. Syst. 27(3), 579–592 (2015)

    Google Scholar 

  15. 15.

    Luo, X., Zhou, M., Xia, Y., Zhu, Q., Ammari, A.C., Alabdulwahab, A.: Generating highly accurate predictions for missing QoS-data via aggregating non-negative latent factor models. IEEE Trans. Syst. Man Cybern. Syst. 27(3), 524–537 (2016)

    Google Scholar 

  16. 16.

    Luo, X., Zhou, M., Li, S., Xia, Y., You, Z., Zhu, Q., Leung, H.: Incorporation of efficient second-order solvers into latent factor models for accurate prediction of missing QoS data. IEEE Trans. Cybern. 48(4), 1216–1228 (2018)

    Article  Google Scholar 

  17. 17.

    Xie, Z., Jin, L., Luo, X., Li, S., Xiao, X.: A data-driven cyclic-motion generation scheme for kinematic control of redundant manipulators. IEEE Trans. Control Syst. Technol. (2019). https://doi.org/10.1109/TCST.2019.2963017

    Article  Google Scholar 

  18. 18.

    Liu, H., Chen, G.: Robust trajectory tracking control of marine surface vessels with uncertain disturbances and input saturations. Nonlinear Dyn. (2020). https://doi.org/10.1007/s11071-020-05701-8

    Article  Google Scholar 

  19. 19.

    Jin, L., Yan, J., Du, X., Xiao, X., Fu, D.: RNN for solving time-variant generalized sylvester equation with applications to robots and acoustic source localization. IEEE Trans. Ind. Inf. 16(10), 6359–6369 (2020)

    Article  Google Scholar 

  20. 20.

    Mallik, W., Santra, S.: Mitigation of vortex-induced vibration lock-in using time-delay closed-loop control. Nonlinear Dyn. 100(1), 1441–1456 (2020)

    Article  Google Scholar 

  21. 21.

    Xie, R., Gong, J., Wang, X.: A new probabilistic robust control approach for system with uncertain parameters. Asian J. Control 17(4), 1330–1341 (2015)

    MathSciNet  Article  Google Scholar 

  22. 22.

    Pradhan, S.K., Subudhi, B.: Position control of a flexible manipulator using a new nonlinear self-tuning PID controller. IEEE/CAA J. Autom. Sin. 7(1), 136–149 (2020)

    MathSciNet  Google Scholar 

  23. 23.

    Yu, X., Ding, P., Yang, F., Zou, C., Ou, L.: Stabilization parametric region of distributed PID controllers for general first-order multi-agent systems with time delay. IEEE/CAA J. Autom. Sin. (2019). https://doi.org/10.1109/JAS.2019.1911627

    Article  Google Scholar 

  24. 24.

    Khan, M.U., Kara, T.: Adaptive type-2 neural fuzzy sliding mode control of a class of nonlinear systems. Nonlinear Dyn. 101(4), 2283–2297 (2020)

    Article  Google Scholar 

  25. 25.

    Xiao, L.: Design and analysis of robust nonlinear neural dynamics for solving dynamic nonlinear equation within finite time. Nonlinear Dyn. 96(4), 2437–2447 (2019)

    Article  Google Scholar 

  26. 26.

    Wang, R., Kalnay, E., Balachandran, B.: Neural machine-based forecasting of chaotic dynamics. Nonlinear Dyn. 98(1), 2903–2917 (2019)

    Article  Google Scholar 

  27. 27.

    Xie, Z., Jin, L., Luo, X., Sun, Z., Liu, M.: RNN for repetitive motion generation of redundant robot manipulators: an orthogonal projection-based scheme. IEEE Trans. Neural Netw. Learn. Syst. (2020). https://doi.org/10.1109/TNNLS.2020.3028304

    Article  Google Scholar 

  28. 28.

    Jha, S.K., Bhasin, S.: Adaptive linear quadratic regulator for continuous-time systems with uncertain dynamics. IEEE/CAA J. Autom. Sin. 7(3), 833–841 (2020)

    MathSciNet  Article  Google Scholar 

  29. 29.

    Yang, X., Zhao, B.: Optimal neuro-control strategy for nonlinear systems with asymmetric input constraints. IEEE/CAA J. Autom. Sin. 7(2), 575–583 (2020)

    MathSciNet  Article  Google Scholar 

  30. 30.

    Qi, Y., Jin, L., Li, H., Li, Y., Liu, M.: Discrete computational neural dynamics models for solving time-dependent sylvester equations with applications to robotics and MIMO systems. IEEE Trans. Ind. Inf. 16(10), 6231–6241 (2020)

  31. 31.

    Zhang, J., Jin, L., Cheng, L.: RNN for perturbed manipulability optimization of manipulators based on a distributed scheme: a game-theoretic perspective. IEEE Trans. Neural Netw. Learn. Syst. (2020). https://doi.org/10.1109/TNNLS.2020.2963998

    MathSciNet  Article  Google Scholar 

  32. 32.

    Ding, Z.: Consensus disturbance rejection with disturbance observers. IEEE Trans. Ind. Electron. 62(9), 5829–5837 (2015)

    Article  Google Scholar 

  33. 33.

    Zhang, Y., Ding, Y., Qiu, B., Wen, J., Li, X.: ZD method based nonlinear and robust control of agitator tank. Asian J. Control 20(4), 1464–1479 (2018)

    MathSciNet  Article  Google Scholar 

  34. 34.

    Zhang, Z., Zheng, L., Qiu, T., Deng, F.: Varying-parameter convergent-differential neural solution to time-varying overdetermined system of linear equations. IEEE Trans. Autom. Control 65(2), 874–881 (2020)

    MathSciNet  Article  Google Scholar 

  35. 35.

    Qi, Y., Jin, L., Wang, Y., Xiao, Lin, Zhang, J.: Complex-valued discrete-time neural dynamics for perturbed time-dependent complex quadratic programming with applications. IEEE Trans. Neural Netw Learn Syst. 31(9), 3555–3569 (2020)

    MathSciNet  Article  Google Scholar 

  36. 36.

    Guo, D., Li, S., Stanimirovic, P.S.: Analysis and application of modified ZNN design with robustness against harmonic noise. IEEE Trans. Ind. Inf. 16(7), 4627–4638 (2019)

    Article  Google Scholar 

  37. 37.

    Jin, L., Li, S., Hu, B., Liu, M., Yu, J.: Noise-suppressing neural algorithm for solving time-varying system of linear equations: A control-based approach. IEEE Trans. Ind. Inf. 15(1), 236–246 (2018)

    Article  Google Scholar 

  38. 38.

    Jin, L., Zhang, Y., Li, S., Zhang, Y.: Noise-tolerant ZNN models for solving time-varying zero-finding problems: a control-theoretic approach. IEEE Trans. Autom. 62(2), 992–997 (2017)

    MathSciNet  Article  Google Scholar 

Download references

Funding

This study was funded by the National Key Research and Development Program of China under Grant 2017YFE0118900, by the research project of Huawei Mindspore Academic Award Fund of Chinese Association of Artificial Intelligence CAAIXSJLJJ-2020-009A, by the Team Project of Natural Science Foundation of Qinghai Province, China (No. 2020-ZJ-903), by the Key Laboratory of IoT of Qinghai (No. 2020-ZJ-Y16), by the Natural Science Foundation of Gansu Province, China, under Grant 20JR10RA639, by the Natural Science Foundation of Chongqing (China) under Grant cstc2020jcyj-zdxmX0028, by the Research and Development Foundation of Nanchong (China) under Grant 20YFZJ0018, by CAS “Light of West China” Program, and by the Project Supported by Chongqing Key Laboratory of Mobile Communications Technology under Grant cqupt-mct-202004.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Long Jin.

Ethics declarations

Conflict of interest

The authors declare that we have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Liu, M., Li, J., Liufu, Y. et al. Noise-rejection zeroing dynamics for control of industrial agitator tank. Nonlinear Dyn (2021). https://doi.org/10.1007/s11071-021-06233-5

Download citation

Keywords

  • Agitator tank system
  • Production efficiency
  • Noise rejection zeroing dynamics (NRZD)
  • Computer simulations