Experimental studies of linear quadratic regulator (LQR) cost matrices weighting to control an accurate take-off position of bicopter unmanned aerial vehicles (UAVs)
Controller design for airplane flight control is challenged to achieve an optimum result, particularly for safety purposes. The experiment evaluated the linear quadratic regulator (LQR) method to research the optimal gain of proportional-integral-derivative (PID) to hover accurately the bicopter model by minimizing error. The 3 degree of freedom (DOF) helicopter facility is a suitable bicopter experimental simulator to test its complex multiple input multiple output (MIMO) flight control model to respond to the challenge of multipurpose drone control strategies. The art of LQR setting is how to search for appropriate cost matrices scaling to optimize results. This study aims to accurately optimize take-off position control of the bicopter model by investigating LQR cost matrices variation in actual experiments. From the experimental results of weighted matrix variation on the bicopter simulator, the proposed LQR method has been successfully applied to achieve asymptotic stability of roll angle, although it yielded a significant overshoot. Moreover, the overshoot errors had good linearity to weighting variation. Despite that, the implementation of cost matrices is limited in the real bicopter experiment, and there are appropriate values for achieving an optimal accuracy. Moreover, the unstable step response of the controlled angle occurred because of excessive weighting.
G. Nugroho, Z. Taha, T. S. Nugraha, and H. Hadsanggeni, “Development of a Fixed Wing Unmanned Aerial Vehicle (UAV) for Disaster Area Monitoring and Mapping,” J. Mechatronics, Electr. Power, Veh. Technol., vol. 6, no. 2, pp. 83–88, 2015.
J. A. Prakosa, D. V. Samokhvalov, G. R. V. Ponce, and F. S. Al-Mahturi, “Speed control of brushless DC motor for quad copter drone ground test,” 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 2019.
J. A. Prakosa, A. Gusrialdi, E. Kurniawan, A. D. Stotckaia, H. Adinanta, and others, “Experimentally robustness improvement of DC motor speed control optimization by H-infinity of mixed-sensitivity synthesis,” Int. J. Dyn. Control, pp. 1–13, 2022.
I. K. Mohammed and A. I. Abdulla, “Elevation, pitch and travel axis stabilization of 3DOF helicopter with hybrid control system by GA-LQR based PID controller,” Int. J. Electr. Comput. Eng., vol. 10, no. 2, p. 1868, 2020.
G. Nugroho and D. Dectaviansyah, “Design, manufacture and performance analysis of an automatic antenna tracker for an unmanned aerial vehicle (UAV),” J. Mechatronics, Electr. Power, Veh. Technol., vol. 9, no. 1, pp. 32–40, 2018.
F. R. Triputra, B. R. Trilaksono, T. Adiono, R. A. Sasongko, and M. Dahsyat, “Nonlinear dynamic modeling of a fixed-wing unmanned aerial vehicle: A case study of Wulung,” J. Mechatronics, Electr. Power, Veh. Technol., vol. 6, no. 1, pp. 19–30, 2015.
J. Hu and H. Gu, “Survey on flight control technology for large-scale helicopter,” Int. J. Aerosp. Eng., 2017.
J. A. Prakosa, E. Kurniawan, H. Adinanta, S. Suryadi, and M. I. Afandi, “Kajian eksperimen teknik kontrol penerbangan posisi tinggal landas drone bikopter dengan metode PID,” J. Otomasi Kontrol dan Instrumentasi, vol. 12, no. 2, pp. 1–8, 2020.
J. Apkarian, M. Lévis, and C. Fulford, “Laboratory guide: 3 DOF helicopter experiment for LabVIEW users,” Markham: QUANSER, 2012.
O. Saleem, “An enhanced adaptive-LQR procedure for under-actuated systems using relative-rate feedback to dynamically reconfigure the state-weighting-factors,” J. Vib. Control, p. 10775463221078654, 2022.
U. M. Guzey, E. H. Copur, S. Ozcan, A. C. Arican, B. M. Kocagil, and M. U. Salamci, “Experiment of sliding mode control with nonlinear sliding surface design for a 3-DOF helicopter model,” in 2019 XXVII International Conference on Information, Communication and Automation Technologies (ICAT), pp. 1–6, 2019.
E. Kurniawan, H. Wang, B. H. Sirenden, J. A. Prakosa, H. Adinanta, and S. Suryadi, “Discrete-time modified repetitive sliding mode control for uncertain linear systems,” Int. J. Adapt. Control Signal Process., vol. 35, no. 11, pp. 2245–2258, 2021.
W. Xu, H. Peng, L. Yang, and X. Zhu, “Robust attitude control of a 3-DOF helicopter prototype subject to wind disturbance and communication delay,” Trans. Inst. Meas. Control, vol. 43, no. 13, pp. 3071–3081, 2021.
T. Dezhi and T. Xiaojun, “Design of UAV attitude controller based on improved robust LQR control,” in 2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 1004–1009, 2017.
X. Yang and X. Zheng, “Adaptive nn backstepping control design for a 3-DOF helicopter: Theory and experiments,” IEEE Trans. Ind. Electron., vol. 67, no. 5, pp. 3967–3979, 2019.
X. Zhu and D. Li, “Robust attitude control of a 3-DOF helicopter considering actuator saturation,” Mech. Syst. Signal Process., vol. 149, p. 107209, 2021.
M. N. Setiawan, E. R. Suryana, L. Parytta, and W. Andaro, “Pole placement and LQR implementation on longitudinal altitute holding control of wing in surface effect vehicle,” J. Mechatronics, Electr. Power, Veh. Technol., vol. 11, no. 2, pp. 86–94, 2020.
E. Joelianto, D. Christian, and A. Samsi, “Swarm control of an unmanned quadrotor model with LQR weighting matrix optimization using genetic algorithm,” J. Mechatronics, Electr. Power, Veh. Technol., vol. 11, no. 1, pp. 1–10, 2020.
S. Bai and P. Chirarattananon, “SplitFlyer Air: A Modular Quadcopter That Disassembles into Two Bicopters Mid-Air,” IEEE/ASME Trans. Mechatronics, 2022.
N. L. Manuel, N. Inanç, and M. Y. Erten, “Control of mobile robot formations using A-star algorithm and artificial potential fields,” J. Mechatronics, Electr. Power, Veh. Technol., vol. 12, no. 2, pp. 57–67, 2021.
J. A. Prakosa, A. V. Putov, and A. D. Stotckaia, “Measurement uncertainty of closed loop control system for water flow rate,” 2019 XXII International Conference on Soft Computing and Measurements (SCM), 2019.
P. Das, R. K. Mehta, and O. P. Roy, “Optimized methods for the pre-eminent performance of LQR control applied in a MIMO system,” Int. J. Dyn. Control, vol. 7, no. 4, pp. 1501–1520, 2019.
N. Van Chi, “Adaptive feedback linearization control for twin rotor multiple-input multiple-output system,” Int. J. Control. Autom. Syst., vol. 15, no. 3, pp. 1267–1274, 2017.
Y. Y. Nazaruddin, I. G. N. A. I. Mandala, and others, “Optimisasi pengontrol LQR menggunakan algoritma stochastic fractal search,” in Seminar Nasional Instrumentasi, Kontrol dan Otomasi, pp. 235–240, 2018.
A. Iannelli and R. S. Smith, “A multiobjective LQR synthesis approach to dual control for uncertain plants,” IEEE Control Syst. Lett., vol. 4, no. 4, pp. 952–957, 2020.
M. Farjadnasab and M. Babazadeh, “Model-free LQR design by Q-function learning,” Automatica, vol. 137, p. 110060, 2022.
L. Li, Y. Liu, Z. Yang, X. Yang, and K. Li, “Mean-square error constrained approach to robust stochastic iterative learning control,” IET Control Theory & Appl., vol. 12, no. 1, pp. 38–44, 2018.
Metrics powered by PLOS ALM
- There are currently no refbacks.
Copyright (c) 2022 Journal of Mechatronics, Electrical Power, and Vehicular Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.