Journal of Aeronautical Engineering

Journal of Aeronautical Engineering

Design of Constrained Predictive Controller by Considering Communication Delay for Quadrotor and Optimal Adjustment of Controller Parameters

Document Type : Original Article

Authors
Hojat Hesamipour 1
Saeed Nasrollahi 2
1 Master student - Electrical and Computer Complex -Malek Ashtar University- Tehran-Iran
2 Faculty of Electrical and Computer, Malek-Ashtar University of Technology, Iran.
10.22034/joae.2020.135886
Abstract
Designing the suitable autopilot for the quadrotors is very important in how the flight vehicle moves and follows the specified reference path. One of the suitable controllers for autopilot design is the predictive controller, which is the most well-known Generalized Predictive Controller method. In the autopilot design, constraints on inputs as well as communication delays must be taken into account, and if these two issues are not addressed in the controller design, the autopilot will not function properly and may even lead to instability. In this paper, a generalized predictive controller with consideration for delayed input data for a quadrotor autopilot is presented. Also, in order to determine the predictive control parameters, the Metaheuristic method, particle swarm optimization, has been used and these parameters have been optimally adjusted. In adjusting the controller parameters, the objective function is used based on the performance indicators such as settling time, peak time, overshoot, and steady-state error. By adjusting the weights of this function, the controller performance indicators can be determined. Also, the simulation results show that the controller performance based on the defined objective function is much improved compared to the cost function based on the integral of the error.
Keywords
quadrotor autopilot
constrained model predictive control
communication delay
particle swarm optimization algorithm
  • Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision agriculture, 13(6), 693-712.
  • Bouabdallah, S. (2007). Design and control of quadrotors with application to autonomous flying (No. THESIS). Epfl.
  • Ahn, Y. M., Block, D. J., & Sreenivas, R. S. (2015). Autonomous navigation and localization of a quadrotor in an indoor environment. Journal of Aerospace Information Systems, 12(12), 699-709.
  • Dydek, Z. T., Annaswamy, A. M., & Lavretsky, E. (2012). Adaptive control of quadrotor UAVs: A design trade study with flight evaluations. IEEE Transactions on control systems technology, 21(4), 1400-1406.
  • Chen, A. J., Sun, M. J., Wang, Z. H., Feng, N. Z., & Shen, Y. (2020). Attitude trajectory tracking of quadrotor UAV using super-twisting observer-based adaptive controller. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 0954410020966476.
  • Turkoglu, K., & Jafarov, E. M. (2007, May). Augmented optimal LQR control system design for the longitudinal flight dynamics of an UAV: Inner and outer loop concepts. In Proceedings of the 9th WSEAS International Conference on Automatic Control, Modeling & Simulation, Istanbul, Turkey.
  • Özbek, N. S., Önkol, M., & Efe, M. Ö. (2016). Feedback control strategies for quadrotor-type aerial robots: a survey. Transactions of the Institute of Measurement and Control, 38(5), 529-554.
  • Zulu, A., & John, S. (2016). A review of control algorithms for autonomous quadrotors. arXiv preprint arXiv:1602.02622.
  • Han, B., Zhou, Y., Deveerasetty, K. K., & Hu, C. (2018, August). A review of control algorithms for quadrotor. In 2018 IEEE International Conference on Information and Automation (ICIA) (pp. 951-956). IEEE.
  • Chiou, J. S., Tran, H. K., Shieh, M. Y., & Nguyen, T. N. (2016). Particle swarm optimization algorithm reinforced fuzzy proportional–integral–derivative for a quadrotor attitude control. Advances in Mechanical Engineering, 8(9), 1687814016668705.
  • Zareb, M., Nouibat, W., Bestaoui, Y., Ayad, R., & Bouzid, Y. (2020). Evolutionary autopilot design approach for UAV quadrotor by using GA. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 44(1), 347-375.
  • Mendoza-Soto, J. L., & Cortés, H. R. (2017, June). Generalized predictive control for trajectory tracking of a quadcopter vehicle. In 2017 international conference on unmanned aircraft systems (ICUAS) (pp. 206-212). IEEE.
  • Criado, R. M., & Rubio, F. R. (2015). Autonomous path tracking control design for a comercial quadcopter. IFAC-PapersOnLine, 48(9), 73-78.
  • Eren, U., Prach, A., Koçer, B. B., Raković, S. V., Kayacan, E., & Açıkmeşe, B. (2017). Model predictive control in aerospace systems: Current state and opportunities. Journal of Guidance, Control, and Dynamics, 40(7), 1541-1566.
  • Greatwood, C., & Richards, A. G. (2019). Reinforcement learning and model predictive control for robust embedded quadrotor guidance and control. Autonomous Robots, 43(7), 1681-1693.
  • Silveira, A., Silva, A., Coelho, A., Real, J., & Silva, O. (2020). Design and real-time implementation of a wireless autopilot using multivariable predictive generalized minimum variance control in the state-space. Aerospace Science and Technology, 105, 106053.
  • Mendoza-Soto, José Luis, Alvarez-Icaza, Luis, and Rodríguez-Cortés, Hugo. Constrained generalized predictive control for obstacle avoidance in a quadcopter. Robotica, 36(9):1363–1385, 2018.
  • Kawai, F., Ito, H., Nakazawa, C., Matsui, T., Fukuyama, Y., Suzuki, R., & Aiyoshi, E. (2007, October). Automatic tuning for model predictive control: Can particle swarm optimization find a better parameter?. In 2007 IEEE 22nd International Symposium on Intelligent Control (pp. 646-651). IEEE.
  • Smoczek, J., & Szpytko, J. (2016). Particle swarm optimization-based multivariable generalized predictive control for an overhead crane. IEEE/ASME Transactions on Mechatronics, 22(1), 258-268.
  • Xu, F., Chen, H., Gong, X., & Mei, Q. (2015). Fast nonlinear model predictive control on FPGA using particle swarm optimization. IEEE Transactions on Industrial Electronics, 63(1), 310-321.
  • Luo, B., Shao, Z., Xu, Z., Zhao, J., & Zhou, L. (2011, May). A new model predictive controller with swarm intelligence implemented on FPGA. In 2011 International Symposium on Advanced Control of Industrial Processes (ADCONIP) (pp. 427-432). IEEE.
  • KARAHAN, Oğuzhan and ATAŞLAR-AYYILDIZ, Banu. Application of multi-objective controller to optimal tuning of pid parameters for different process systems using cuckoo search algorithm. Anadolu University of Sciences & Technology-A: Applied Sciences & Engineering, 20(1), 2019.
  • Wang, L. (2009). Model predictive control system design and implementation using MATLAB®. Springer Science & Business Media.
  • Mayne, D. Q., Rawlings, J. B., Rao, C. V., & Scokaert, P. O. (2000). Constrained model predictive control: Stability and optimality. Automatica, 36(6), 789-814.
Journal of Aeronautical Engineering
Volume 22, Issue 2
December 2020
Pages 133-145

  • Receive Date 10 April 2021
  • Revise Date 25 July 2021
  • Accept Date 27 July 2021