Iranian Journal of Mechanical Engineering Transactions of ISME

Iranian Journal of Mechanical Engineering Transactions of ISME

Adaptive Data-driven Controller Design for a Control Simulator with a Common Single Main Rotor-single Tail Rotor Configuration

Authors
M. Abolfazl Mokhtari 1
Mohsen Rostmi 2
1 Assistant Professor, University of Imam Ali, Faculty of Flight and Engineering, Tehran, Iran,
2 Assistant Professor, University of Imam Ali, Faculty of Flight and Engineering, Tehran
10.30506/ijmep.2023.532943.1806
Abstract
Nowadays, industrial systems deal with a wide range of constraints. Input saturation and lack of system model are two types of these constraints. In this paper, a separate model free adaptive data-driven controller for a nonlinear system called a simulator with the conventional configuration of a main single-tailed propeller, twin rotor MIMO system (TRMS) in the presence of input saturation is presented. In the proposed controller, the design of the control input signal only depends on the input and output data of the system and the system model is not used. The purpose of this paper is to control the horizontal and vertical angles of the TRMS system. For this purpose, at first, using the model of TRMS that has been presented for this system in previous articles, a model of the relevant system is expressed and then only using the input and output data obtained from that model, control the defined angles with a separate model free adaptive data-driven control method. Finally, the performed simulations demonstrate the effectiveness of the proposed method for the TRMS, by using two difference reference signals named variable steps and sinusoidal.
Keywords
Adaptive Control
Data-Driven Control
TRMS

Subjects

[1] Mondal, S., and Mahanta, C., “Adaptive Second-order Sliding Mode Controller for a Twin Rotor Multi-input-multi-output System”, IET Control Theory and Applications, Vol. 6(14), pp. 2157-2167, DOI: 10.1049/iet-cta.2011.0478, (2012).
[2] Rahideh, A., Shaheed, M.H., and Huijberts, H.J.C., “Dynamic Modelling of a TRMS using Analytical and Empirical Approaches”, Control Engineering Practice, Vol. 16, pp. 241-259, DOI: https://doi.org/10.1016/j.conengprac.2007.04.008, (2008).
 
[3] Rahideh, A., and Shaheed, M.H., “Grey-box Modelling of a Non-linear Aerodynamic System using Genetic Algorithms”, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, Vol. 225, No. 8, pp. 863-873, DOI: https://doi.org/10.1177/0954410011403817, (2011).
 
[4] Chalupa, P., Přikryl, J., and Novák, J., “Modelling of Twin Rotor MIMO System”, Procedia Engineering, Vol. 100, pp. 249-258, DOI: https://doi.org/10.1016/j.proeng.2015.01.365, (2015).
 
[5] Huu, T.D., and Ismail, I.B., “Modelling of Twin Rotor MIMO System”, 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA), Ipoh, Malaysia, pp. 1-6, DOI: 10.1109/ROMA.2016.7847803, (2016).
 
[6] Martínez, M.I., Vivas, C., and Ortega, M.G., “A Multivariable Nonlinear H∞ Controller for a Laboratory Helicopter”, Proceedings of the 44th IEEE Conference on Decision and Control, Seville, Spain, December 12-15, pp. 4065-4070, DOI: 10.1109/CDC.2005.1582798, (2005).
 
[7] Juang, J.G., Huang, M.T., and Liu, W.K., “PID Control using Presearched Genetic Algorithms for a MIMO System”, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol. 38, No. 5, pp. 716-727, DOI: 10.1109/TSMCC.2008.923890, (2008).
 
[8] Tao, C.W., Taur, J.S., Chang, Y.H., and Chang, C.W., “A Novel Fuzzy Sliding and Fuzzy-integral-sliding Controller for the Twin-rotor Multi-input Multi-output System”, IEEE Transactions on Fuzzy Systems, Vol. 18, No. 5, pp. 893-905, DOI: 10.1109/TFUZZ.2010.2051447, (2010).
 
[9] DuĠescu, D.A., Radac, M.B., and Precup, R.E., “Model Predictive Control of a Nonlinear Laboratory Twin Rotor Aero-dynamical System”, IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), Herl'any, Slovakia, pp. 37-42, DOI: 10.1109/SAMI.2017.7880339, (2017).
 
[10] Amini, S., Ahi, B., and Haeri, M.,” Nested Saturation Control Based on the Extended State Observer: Twin Rotor MIMO System”, 5th International Conference on Control, Instrumentation and Automation (ICCIA), Shiraz, Iran, pp. 55-59, DOI: 10.1109/ICCIAutom.2017.8258653, (2017).
 
[11] Benner, P., Ohlberger, M., Cohen, A., and Willcox, K., "Model Reduction and Approximation: Theory and Algorithms", Society for Industrial and Applied Mathematics (SIAM), Philadelphia, USA, pp. 15, DOI: https://doi.org/10.1137/1.9781611974829, (2017).
 
[12] Tympakianaki, A., Koutsopoulos, H.N., and Jenelius, E., "Robust SPSA Algorithms for Dynamic OD Matrix Estimation", Procedia Computer Science, Vol. 130, pp. 57-64, DOI: https://doi.org/10.1016/j.procs.2018.04.012, (2018).
 
[13] Mišković, J., Karimi, L., and Bonvin, A., "Correlation-based Tuning of a Restricted Complexity Controller for an Active Suspension System", European Journal of Control, Vol. 9, pp. 77-83, DOI: https://doi.org/10.3166/ejc.9.77-83, (2003).
 
[14] Karimi, A., Mišković, L., and Bonvin, D., "Iterative Correlation‐based Controller Tuning", International Journal of Adaptive Control and Signal Processing, Vol. 18, No. 8, pp. 645-664,  
DOI: https://doi.org/10.1002/acs.825, (2004).
 
[15] Li, X., Chen, S.L., Ma, J., Teo, C.S., and Tan, K.K., "Data-driven Model-free Iterative Tuning Approach for Smooth and Accurate Tracking", IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Auckland, New Zealand, pp. 593-598, DOI: 10.1109/AIM.2018.8452239, (2018).
 
[16] Mircea-Bogdan, R., Precup, R.E., and Petriu, M., "Constrained Data-driven Model-free ILC-based Reference Input Tuning Algorithm", Acta Polytechnica Hungarica, Vol. 12. No. 1, pp. 137-160, DOI: 10.12700/APH.12.1.2015.1.9, (2015).
 
[17] Chi, R., Liu, X., Zhang, R., Hou, Z., and Huang, B., "Constrained Data-driven Optimal Iterative Learning Control", Journal of Process Control, Vol. 55, pp. 10-29, DOI: https://doi.org/10.1016/j.jprocont.2017.03.003, (2017).
 
[18] Rui, Z., Song, X., Wen, D., and Sun, C., "Optimal Tracking Control for a Class of Unknown Discrete-time Systems with Actuator Saturation via Data-based ADP Algorithm", Acta Automatica Sinica, Vol. 39, No. 9, pp. 1413-1420, DOI: https://doi.org/10.1016/S1874-1029(13)60070-1, (2013).
 
[19] Hui, Y., Chi, R., Huang, B., Hou, Z., and Jin, S., "Observer-based Sampled-data Model-free Adaptive Control for Continuous-time Nonlinear Nonaffine Systems with Input Rate Constraints", in IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 51, No. 12, pp. 7813-7822, DOI: 10.1109/TSMC.2020.2982491, (2021).
 
[20] Skelton, R.E., "Model Error Concepts in Control Design", International Journal of Control, Vol. 49, No. 5, pp. 1725-1753, DOI: 10.1080/00207178908559735, (1989).
 
[21] Helvoort, V., and Michiel, J.J., "Unfalsified Control: Data-driven Control Design for Performance Improvement", PhD Thesis, Eindhoven University of Technology, Netherlands, DOI: https://doi.org/10.6100/IR631347, (2007).
 
[22] Dibaji, S.M., Pirani, M., and Flamholz, D., Annaswamy, A.M., Johansson, K.H., and Chakrabortty, A., "A Systems and Control Perspective of CPS Security", Annual Reviews in Control, Vol. 47, pp. 394-411, DOI: https://doi.org/10.1016/j.arcontrol.2019.04.011, (2019).
 
[23] Bu, X., Wang, Q., Hou, Z. and Qian, W., "Data Driven Control for a Class of Nonlinear Systems with Output Saturation", ISA transactions, Vol. 81, pp. 1-7, DOI: https://doi.org/10.1016/j.isatra.2018.07.009, (2018).
 
[24] Hou, Z., Yuanming, Z., "Controller-dynamic-linearization-based Model Free Adaptive Control for Discrete-Time Nonlinear Systems", IEEE Transactions on Industrial Informatics, Vol. 9, No. 4, pp. 2301-2309, DOI: 10.1109/TII.2013.2257806, (2013).
[25] Bu, X., Hou, Z., Yu, Q., and Yang, Y., "Quantized Data Driven Iterative Learning Control for a Class of Nonlinear Systems with Sensor Saturation", IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-11, DOI: 10.1109/TSMC.2018.2866909, (2018).
 
[26] Bu, X., Qiao, Y., Hou, Z., and Yang, J., "Model Free Adaptive Control for a Class of Nonlinear Systems using Quantized Information", Asian Journal of Control, Vol. 20, No. 2, pp. 962-968, DOI: https://doi.org/10.1002/asjc.1610, (2018).
 
[27] Xu, D., Shi, Y., and Ji, Z., "Model-free Adaptive Discrete-time Integral Sliding-mode-constrained-control for Autonomous 4WMV Parking Systems", IEEE Transactions on Industrial Electronics, Vol. 65, No. 1, pp. 834-843, DOI: 10.1109/TIE.2017.2739680, (2018).
 
[28] Qiu, X., Wang, Y., Xie, X., and Zhang, H., "Resilient Model-free Adaptive Control for Cyber-physical Systems Against Jamming Attack", Neurocomputing, Vol. 413, pp. 422-430, DOI: https://doi.org/10.1016/j.neucom.2020.04.043, (2020).
 
[29] Yu, W., Wang, R., Bu, X., and Hou, Z., "Model Free Adaptive Control for a Class of Nonlinear Systems with Fading Measurements", Journal of the Franklin Institute, Vol. 357, No. 12, pp. 7743-7760, DOI: https://doi.org/10.1016/j.jfranklin.2020.05.041, (2020).
 
[30] Hou, Z., and Shuangshuang, X., "On Model Free Adaptive Control and Its Stability Analysis", IEEE Transactions on Automatic Control, Vol. 64, No. 11, pp. 4555-4569, DOI: 10.1109/TAC.2019.2894586, (2019).

  • Receive Date 05 July 2021
  • Revise Date 12 December 2021
  • Accept Date 24 April 2023