Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-22T23:33:32.029Z Has data issue: false hasContentIssue false
  • English
  • Français

Coordinated control of fuel flow rate and air flow rate of a supersonic heat-airflow simulated test system

Published online by Cambridge University Press:  19 March 2020

C. Cai Open the ORCID record for C. Cai [Opens in a new window] ,
L. Guo  and
J. Liu
C. Cai*
Affiliation:
School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan, Hebei, China
L. Guo
Affiliation:
School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan, Hebei, China
J. Liu
Affiliation:
School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan, Hebei, China
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The gas temperature of the supersonic heat airflow simulated test system is mainly determined by the fuel and air flow rates which enter the system combustor. In order to realise a high-quality control of gas temperature, in addition to maintaining the optimum ratio of fuel and air flow rates, the dynamic characteristics of them in the combustion process are also required to be synchronised. Aiming at the coordinated control problem of fuel and air flow rates, the mathematical models of fuel and air supply subsystems are established, and the characteristics of the systems are analysed. According to the characteristics of the systems and the requirements of coordinated control, a fuzzy-PI cross-coupling coordinated control strategy based on neural sliding mode predictive control is proposed. On this basis, the proposed control algorithm is simulated and experimentally studied. The results show that the proposed control algorithm has good control performance. It cannot only realise the accurate control of fuel flow rate and air flow rate, but also realise the coordinated control of the two.

Keywords

Supersonicfuel flow rateair flow rateneural sliding mode predictive controlcoordinated control
Type
Research Article
Information
The Aeronautical Journal , Volume 124 , Issue 1278 , August 2020 , pp. 1170 - 1189
Copyright
© The Author(s) 2020. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Cai, C., Ma, Q., Wu, D. and Fan, L.Design and implementation of the fuel supply for the high-temperature combustion System, Adv Mech Eng, 2017, 9, (1), pp 1–8.CrossRefGoogle Scholar
Cai, C., Yang, Y. and Liu, T.Coordinated control of fuel flow-rate for a high-temperature high-speed wind tunnel, Proc Inst Mech Eng Part G- J Aerosp Eng, 2016, 230, (13), pp 2504–2514.CrossRefGoogle Scholar
Fan, L., Cai, C. and Wu, B.Modeling and simulation of gas temperature in high speed hot air flow simulation test system, China Sci Paper, 2015, 10, (23), pp 2745–2748.Google Scholar
Matausek, M.R. and Micic, A.D.A modified Smith predictor for controlling a process with an integrator and long dead-time, IEEE Trans Autom Cont, 1996, 41, (8), pp. 1199–1203.CrossRefGoogle Scholar
Matausek, M.R. and Micic, A.D.On the modified Smith predictor for controlling a process with an integrator and long dead-time, IEEE Trans Autom Cont, 1999, 44, (8), pp 1603–1606.CrossRefGoogle Scholar
Astrom, K.J., Hang, C.C. and Lim, B.C.A new Smith predictor for controlling a process with an integrator and long dead-time, IEEE Trans Autom Cont, 1994, 39, (2), pp 343–345.10.1109/9.272329CrossRefGoogle Scholar
Dehlin, E.B.Designing and tuning digital controllers, Instr Contr Sys, 1968, 41, (7), pp 77–79.Google Scholar
Zhu, X.F.Dalin algorithm research, Chemical Autom Inst, 1989, 14, (5), pp 23–27.Google Scholar
Kwon, W.H., Lee, Y.S. and Han, S.H.General receding horizon control for linear time-delay systems, Automatic, 2004, 40, (9), pp 1603–1611.CrossRefGoogle Scholar
Rodrigues, J.A.D. and Maciel Filho, R.Application of a novel approach for DMC predictive controller design by response surface analysis in a fed-batch bioreactor, Comput Chem Eng, 1999, 23, (1), pp S293–S296.CrossRefGoogle Scholar
Rodrigues, J.A.D., Toledo, E.C.V. and Maciel Filho, R.A tuned approach of the predictive–adaptive GPC controller applied to a fed-batch bioreactor using complete factorial design, Comput Chem Eng, 2002, 26, (10), pp 1493–1500.CrossRefGoogle Scholar
Wu, L., Su, X. and Shi, P.Sliding mode control with bounded gain performance of Markovian jump singular time-delay systems, Automatic, 2012, 48, (8), pp 1929–1933.CrossRefGoogle Scholar
Khandekar, A.A., Malwatkar, G.M. and Patre, B.M.Discrete sliding mode control for robust tracking of higher order delay time systems with experimental application, ISA Trans, 2013, 52, (1), pp 36–44.CrossRefGoogle ScholarPubMed
Zhou, K., Pramod, P.K., Jakob, S. and Hans, H.N.Robust performance of systems with structured uncertainties in state space, Automatic, 1995, 31, (2), pp 249–255.CrossRefGoogle Scholar
Jabbari, F. and Schmitendorf, W.E.Robust linear controllers using observers, IEEE Auto Cont, 1991, 36, (12), pp 1509–1514.10.1109/9.106173CrossRefGoogle Scholar
Indranil, P., Saptarshi, D. and Amitava, G.Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay, ISA Trans, 2011, 50, (1), pp 28–36.Google Scholar
Jing, N.A., Ren, X. and Huang, H.Time-delay positive feedback control for nonlinear time-delay systems with neural network compensation, Acta Automatica Sinica, 2008, 34, (9), pp 1196–1203.Google Scholar
Kwon, O., Park, J.H., Lee, S.M. and Cha, E.J.Analysis on delay-dependent stability for neural networks with time-varying delays, Neurocomputing, 2013, 103, pp 114–120.10.1016/j.neucom.2012.09.012CrossRefGoogle Scholar
Utkal, M. and Ibrahim, K. Smith predictor with sliding mode control for processes with large dead times, J. Electric Eng, 2017, 68, (6), pp 463–469.Google Scholar
Utkal, M. and RubÉn, R.Smith predictor based sliding mode control for a class of unstable processes, Transactions of the Institute of Measurement and Control, 2017, 39, (5), pp 706–714.Google Scholar
Shen, W., Pan, Z., Li, M. and Peng, H.Lateral control method for wheel-footed robot based on sliding mode control and steering prediction, IEEE Access, 2018, 6, pp 58086–58095.CrossRefGoogle Scholar
Xu, Q.S. Digital integral terminal sliding mode predictive control of piezoelectric-driven motion system, IEEE Trans Ind Electron 2016, 63, (6), pp 3976–3984.CrossRefGoogle Scholar
Lee, T.H, Wang, Q.G. and Tan, K.K.Robust smith-predictor controller for uncertain delay systems, AIChE J, 1996, 42, (4), pp 1033–1040.10.1002/aic.690420415CrossRefGoogle Scholar
Stojic, M.R., Matijevic, F.S. and Draganovic, L.S.A robust Smith predictor modified by internal models for integrating process with dead time, IEEE Trans Autom Cont, 2001, 46, (8), pp 1293–1298.10.1109/9.940937CrossRefGoogle Scholar
Rincon, L., Coronado, E., Hendra, H. Expressive states with a robot arm using adaptive fuzzy and robust predictive controllers, 3rd International Conference on Control and Robotics Engineering, April, 2018, Nagoya, Japan.CrossRefGoogle Scholar
Wang, Y., PeÑa, D.M., Puig, V. and Cembrano, G.Robust economic model predictive control based on a periodicity constraint, Int J Robust Nonlinear Cont, 2019, 29, pp 3297–3309.Google Scholar
Wei, Q. and Wang, W.Research on fuzzy self-adaptive PI-Smith control in long time-delay system, J China UnivPosts Telecommun, 2011, 18, (5), pp 114–117.10.1016/S1005-8885(10)60112-4CrossRefGoogle Scholar
Marusak, P.M.Advantages of an easy to design fuzzy predictive algorithm in control systems of nonlinear chemical reactors, Appl Soft Comput, 2009, 9, (3), pp 1111–1125.10.1016/j.asoc.2009.02.013CrossRefGoogle Scholar
Huang, J., Lewis, F.L. and Liu, K.A neural net predictive control for telerobots with time delay. J Intell Robot Syst, 2000, 29, pp 1–25.CrossRefGoogle Scholar
Tan, Y.H. and Cauwenberghe, V.Neural network based on nonlinear smith predictor, Control Theory and Applications, 2000, 17, (3), pp 410–414.Google Scholar
Koren, Y.Cross-couple bixial computer control for manufacturing systems, J Dyn Syst Meas Cont, 1980, 102, (4), pp 265–272.CrossRefGoogle Scholar
Keron, Y. and Lo, C.C.Advanced controllers for feed drives, Ann CIRP, 1992, 41, pp 689–698.Google Scholar
Li, Y.Cai, C, Lee, K and Teng, F.A novel cascade temperature control system for a high-speed heat-airflow wind tunnel, IEEE/ASME Trans Mechatron 2013, 18(4), 1310–1319.CrossRefGoogle Scholar