Comparative Study and Robustness Analysis of Quadrotor Control in Presence of Wind Disturbances
الموضوعات :
1 - Department of Electrical Engineering,
University of Suez Canal, Egypt
الکلمات المفتاحية: Fractional Order PID (FOPID), Quadrotor, Artificial Neural Network (ANN), Fuzzy-PID, Genetic Algorithm (GA), Proportional Integral Derivative (PID) Controller, Adaptive Neuro Fuzzy Inference System (ANFIS),
ملخص المقالة :
Controlling of the quadrotor has been noted for its trouble as the consequence of exceeds nonlinear system, strong coupled multivariable and external disturbances. Quadrotor position and attitude is controlled by several methodologies using feedback linearization, but when quadrotor works with unstructured inputs (e.g. wind disturbance), some limitations of this technique appear which influence flight work. Design control system with fast response, disturbance rejection, small error, and stability is the main objective of this work. So in this paper we can make use of new methods of control to design a controller of nonlinear robust with a reasonable performance to test the impact of wind disturbance in quadrotor control such as Fuzzy-PID controller and compared its results with the others four controllers which are PID tuned using GA, FOPID tuned using GA, ANN and ANFIS then discus which controller give the best results in the presence and absence of wind disturbance. The main objective of this paper is that performance of the designed control structure is computed by the fast response without overshoot and minim error of the position and attitude. Simulation results, shows that position and attitude control using FOPID has fast response and better steady state error and RMS error than Fuzzy-PID, ANFIS, ANN and PID tuned using GA without impact of wind disturbance but after impact of wind disturbance it was observed using Fuzzy-PID has fast response with minimum overshoot and better steady state error and RMS error than the other four controllers used in the paper and compared with most of literature reviews which didn't give the adequate results contrasted with the required position and attitude. The all controllers are tested by simulation under the same conditions using SIMULINK under MATLAB2015a.
[1] Kendoul, F., Survey of Advances in Guidance, Navigation, and Control of Unmanned Rotorcraft Systems, Journal of Field Robotics, Vol. 29, No. 2, 2012, pp. 315-378.
[2] Hou, H., et al,. A Simple Controller of Minisize Quad-Rotor Vehicle, In: 2010 IEEE International Conference on Mechatronics and Automation. IEEE, 2010. pp. 1701-1706.
[3] Reizenstein, A., Position and Trajectory Control of a Quadcopter Using PID and LQ Controllers, 2017.
[4] Bresciani, T., Modelling, Identification and Control of a Quadrotor Helicopter, MSc Theses, 2008.
[5] De Lellis Costa De Oliveira, M., Modeling, Identification and Control of a Quadrotor Aircraft, 2011.
[6] Al-Younes, Y. M., Al-Jarrah, M. A., and Jhemi, A. A., Linear vs. Nonlinear Control Techniques for a Quadrotor Vehicle. In: 7th International Symposium on Mechatronics and its Applications, IEEE, 2010, pp. 1-10.
[7] Minh, L. D., Ha, C., Modeling and Control of Quadrotor MAV Using Vision-Based Measurement, In: International Forum on Strategic Technology, IEEE, 2010, pp. 70-75.
[8] Altug, E., Ostrowski, J. P., Mahony, R., Control of a Quadrotor Helicopter Using Visual Feedback, In: Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292), IEEE, 2002, pp. 72-77.
[9] Mokhtari, A., et al. Feedback Linearization and Linear Observer for a Quadrotor Unmanned Aerial Vehicle, Advanced Robotics, Vol. 20, No. 1, 2006, pp. 71-91.
[10] Mellinger, D., et al. Design, Modeling, Estimation and Control for Aerial Grasping and Manipulation, In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, 2011, pp. 2668-2673.
[11] Subodh M., Robust Control of Quadrotors. Department of Automatic Control and Robotics, MSc Theses, 2017.
[12] Madani, T., Benallegue, A., Control of a Quadrotor Mini-Helicopter Via Full State Backstepping Technique, In: Proceedings of the 45th IEEE Conference on Decision and Control, IEEE, 2006, pp. 1515-1520.
[13] Hamamci, S. E., An Algorithm for Stabilization of Fractional-Order Time Delay Systems Using Fractional-Order PID Controllers, IEEE Transactions on Automatic Control, Vol. 52, No. 10, 2007, pp. 1964-1969.
[14] Efe, M. Ö., Neural Network Assisted Computationally Simple PIλDμ Control of a Quadrotor UAV, IEEE Transactions on Industrial Informatics, Vol. 7, No. 2, 2011, pp. 354-361.
[15] Han, J., From PID to Active Disturbance Rejection Control, IEEE Transactions on Industrial Electronics, Vol. 56, No. 3, 2009, pp. 900-906.
[16] Podlubny, I., Fractional-Order Systems and PIλDμ. IEEE Transactions on Automatic Control, Vol. 44, No. 1, 1999, pp. 208-214.
[17] Mahony, R., Kumar, V., and Corke, P., Multirotor Aerial Vehicles: Modeling, Estimation, and Control of Auadrotor. IEEE Robotics & Automation Magazine, Vol. 19, No. 3, 2012, pp. 20-32.
[18] Mohammed, R. H., Quadrotor Control Using Advanced Control Techniques. International Journal of Image, Graphics and Signal Processing, Vol. 11, No. 2, 2019, pp. 40.
[19] Youns, M. D., Salih, M. A., and Abdulla, I. Abdulla. Position ControlOf Robot Arm Using Genetic Algorithm Based PID Controller, AL Rafdain Engineering Journal, Vol. 21, No. 6, 2013, pp. 19-30.
[20] Xue, D., Chen, Y., A Comparative Introduction of Four Fractional Order Controllers, In: Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No. 02EX527), IEEE, 2002. pp. 3228-3235.
[21] Faieghi, M. R. Nemati, A., On Fractional-Order PID Design, In: Applications of MATLAB in Science and Engineering, IntechOpen, 2011.
[22] Kim, J., Kasabov, N., HyFIS: Adaptive Neuro-Fuzzy Inference Systems and Their Application to Nonlinear Dynamical Systems, Neural Networks, Vol. 19, No. 2, 1999, pp. 1301-1319.
[23] Areed, F. G., Haikal, A. Y., and Mohammed, R. H., Adaptive Neuro-Fuzzy Control of an Induction Motor, Ain Shams Engineering Journal, Vol. 1, No.1, 2010, pp. 71-78.
[24] Mohammed, R. H., Bendary, F., and Elserafi, K., Trajectory Tracking Control for Robot Manipulator Using Fractional Order-Fuzzy-PID Controller, International Journal of Computer Applications, Vol. 134, No. 15, 2016, pp. 8887.
[25] Jang, J. S. R., Sun, C. T., Neuro-Fuzzy Modeling and Control, Proceedings of the IEEE, Vol. 83, No. 3, 1995, pp. 378-406.
[26] Jang, J. S. R., ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, 1993, pp. 665-685.
[27] Mohammed, R., Quadrotor Control Using Fractional-Order PIλDμ Control, Journal of Advances in Computer Engineering and Technology, Vol. 5, No. 1, 2019, pp. 1-10.
