Adaptive predictive control for torque applying system of high-powered test rig

Document Type : Article


1 - Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran. - Department of Automation Engineering, Sharif Technology Branch of ACECR, Tehran, Iran

2 Department of Automation Engineering, Sharif Technology Branch of ACECR, Tehran, Iran


This paper proposes the implementation of a novel predictive control scheme known as adaptive generalized predictive control (AGPC) in the actuation system of a high-powered test rig. Through the use of actuation system, the required torque for simulating different conditions can be applied to the tested gearboxes. The accurate and precise control of this system is of great importance as it affects the overall performance of the test rig. The considered actuation system in this investigation is electro-hydraulically driven with nonlinear and uncertain characteristics. The performance of the proposed control scheme in different conditions of the parametric uncertainty as well as presence of disturbances are evaluated and the results are discussed. The results confirmed the superior performance of the proposed scheme in different studied conditions.


1. Zhou, L., Duan, F., Corsar, M., et al. "A study on helicopter main gearbox planetary bearing fault diagnosis", Appl. Acoust., 147, pp. 4-14 (2019).
2. Defreyne, P., Dereyne, S., Stockman, K., et al. "An energy eciency measurement test bench for gearboxes", EEMODS, 2013, pp. 1-12 (2013).
3. Tri, N.M., Nam, D.N.C., Park, H.G., et al. "Trajectory control of an electro hydraulic actuator using an iterative backstepping control scheme", Mechatronics, 29, pp. 96-102 (2015).
4. Lee, W. and Chung, W.K. "Disturbance-observerbased compliance control of electro-hydraulic actuators with backdrivability", IEEE Robot. Autom. Lett., 4(2), pp. 1722-1729 (2019).
5. Huang, J., An, H., Yang, Y., et al. "Model predictive trajectory tracking control of electro-hydraulic actuator in legged robot with multi-scale online estimator", IEEE Access, 8, pp. 95918-95933 (2020).
6. Mohammed, S., Soon, C.C., Ghazali, R., et al. "An electro-hydraulic servo with intelligent control strategy", In MATEC Web of Conferences, 150, p. 1016 (2018).
7. Lu, X., Bai, Y., Fan, B., et al. "A hybrid offline/online modeling based tracking control for complex hydraulic driving processes", IEEE Access, 7, pp. 106102-106110 (2019).
8. Palermo, A., Toso, A., Heirman, G.H.K., et al. "Structural coupling and non-linear effects in the experimental modal analysis of a precision gear test rig", In International Gear Conference, 2014, pp. 1049-1059 (2014).
9. Mihailidis, A. and Nerantzis, I. "A new system for testing gears under variable torque and speed", Recent Patents Mech. Eng., 2(3), pp. 179-192 (2009).
10. Huang, J., An, H., Lang, L., et al. "A data-driven multi-scale online joint estimation of states and parameters for electro-hydraulic actuator in legged robot", IEEE Access, 8, pp. 36885-36902 (2020).
11. Yao, Z., Yao, J., and Sun, W. "Adaptive RISE control of hydraulic systems with multilayer neural-networks", IEEE Trans. Ind. Electron., 66(11), pp. 8638-8647 (2018).
12. Takloo, S.D., Mozafari, S., Rezazadehmohamadi, M., et al. "Fractional order PID control mechanism for helicopter gearbox test control with internal and external disturbance", Bull. la Societe R. des Sci. Liege, 86, special issue, pp. 127-138 (2017).
13. Maddahi, A., Sepehri, N., and Kinsner, W. "Fractional -order control of hydraulically powered actuators: Controller design and experimental validation", IEEE/ASME Trans. Mechatronics, 24(2), pp. 796-807 (2019).
14. Li, M., Shi, W., Wei, J., et al. "Parallel velocity control of an electro-hydraulic actuator with dual disturbance observers", IEEE Access, 7, pp. 56631-56641 (2019).
15. Heybroek, K. and Sjvberg, J. "Model predictive control of a hydraulic multichamber actuator: A feasibility study", IEEE/ASME Trans. Mechatronics, 23(3), pp. 1393-1403 (2018).
16. Rozali, S.M., Rahmat, M.F., Wahab, N.A., et al. "PID controller design for an industrial hydraulic actuator with servo system", In 2010 IEEE Student Conference on Research and Development (SCOReD), pp. 218-223 (2018).
17. Pan, Y. and Yu, H. "Composite learning from adaptive dynamic surface control", IEEE Trans. Automat. Contr., 61(9), pp. 2603-2609 (2015).
18. Yang, J., Na, J., and Gao, G. "Robust model reference adaptive control for transient performance enhancement", Int. J. Robust Nonlinear Control, 30(15), pp. 6207-6228 (2020).
19. Yang, J., Na, J., and Gao, G. "Robust adaptive control for unmatched systems with guaranteed parameter estimation convergence", Int. J. Adapt. Control Signal Process., 33(12), pp. 1868-1884 (2019).
20. Camacho, E.F. and Alba, C.B. "Model predictive control" In Springer Science & Business Media (2013).
21. Balaji, V. and Maheswari, E. "Model predictive control strategy for industrial process", Bull. Electr. Eng. Informatics, 1(3), pp. 191-198 (2012).
22. Alamirew, T., Balaji, V., and Gabbeye, N. "Comparison of PID controller with model predictive controller for milk pasteurization process", Bull. Electr. Eng. Informatics, 6(1), pp. 24-35 (2017).
23. Parvaresh, A. and Mardani, M. "Model predictive control of a hydraulic actuator in torque applying system of a mechanically closed-loop test rig for the helicopter gearbox", Aviation, 23(4), pp. 143-153 (2019).
24. Wang, D., Zhao, D., Gong, M., et al. "Research on robust model predictive control for electro-hydraulic servo active suspension systems", IEEE Access, 6, pp. 3231-3240 (2017).
25. Parvaresh, A. and Mardani, M. "Data-driven modelfree control of torque-applying system for a mechanically closed-loop test rig using neural networks", Stroj. Vestnik/Journal Mech. Eng., 66(5), (2020).
26. Raziei, S.A. and Jiang, Z. "Dynamic modeling and nonlinear model predictive control of hybrid actuator systems", In 2017 IEEE National Aerospace and Electronics Conference (NAECON), pp. 119-126 (2017).
27. Pereida, K. and Schoellig, A.P. "Adaptive model predictive control for high-accuracy trajectory tracking in changing conditions", In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7831-7837 (2018).
28. He, W.J., Zhang, H.T., Chen, Z., et al. "Generalized predictive control of temperature on an atomic layer deposition reactor", IEEE Trans. Control Syst. Technol., 23(6), pp. 2408-2415 (2015).
29. Gao, Z. "On the centrality of disturbance rejection in automatic control", ISA Trans., 53(4), pp. 850-857 (2014).
30. Kim, J.S. "Recent advances in adaptive MPC", In ICCAS 2010, pp. 218-222 (2010).
31. Heirung, T.A.N., Ydstie, B.E., and Foss, B. "Dual adaptive model predictive control", Automatica, 80, pp. 340-348 (2017).
32. Dong, N., Feng, Y., Han, X.S., et al. "An improved model-free adaptive predictive control algorithm for nonlinear systems with large time delay", In 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), pp. 60-64 (2018).
33. Guo, Y., Hou, Z., Liu, S., et al. "Data-driven modelfree adaptive predictive control for a class of MIMO nonlinear discrete-time systems with stability analysis", IEEE Access, 7, pp. 102852-102866 (2019).
34. Wang, Z. and Wang, J. "Ultra-local model predictive control: A model-free approach and its application on automated vehicle trajectory tracking", Control Eng. Pract., 101, p. 104482 (2020).
35. Clarke, D.W. "Application of generalized predictive control to industrial processes", IEEE Control Syst. Mag., 8(2), pp. 49-55 (1988).
36. Hyatt, P. and Killpack, M.D. "Real-time evolutionary model predictive control using a graphics processing unit", In 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pp. 569-576 (2017).
37. Soderstrom, T. and Stoica, P. "System identification", In Prentice-Hall International (1989).
38. Ishak, N., Tajjudin, M., Ismail, H., et al. "System identification and model validation of electro-hydraulic actuator for quarter car system", WSEAS Trans. Adv. Eng. Educ, 4, pp. 27-35 (2017).
39. Ljung, L., System Identification, Wiley Encycl. Electr. Electron. Eng., pp. 100-129 (1999).