Design and Practical Implementation of a Feed-Forward Neural Observer to Control Quadrotor

Authors

Department of mechanical engineering, Isfahan university of technology

Abstract

In this paper a neural observer is deigned to control a Quadrotor Drone. Quadrotor is a type of flying robot which can fly vertically and has a simple structure. This robot is one of the best models of flying robot that is considered by many researchers recently. Because of nonlinear dynamics of the system, Stability of the control process has an important role in this robot. In this study first, a PD controller is designed to track the desired state and stabilizing the quadrotor based on particle swarm optimization algorithm. Nonlinear observer is then synthesized in order to estimate the unmeasured states. Then a neural network observer is designed and trained based on data that extract of nonlinear observer. After that, simulation results are also provided in order to illustrate the performances of the proposed controller and observer. Finally In addition to simulation we have practically implemented these control and observer methods on a quadrotor test bench. Practical implementation results demonstrate the effectiveness of the presented method.

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References

[1] Lendek, Z., Berna, A., and Guzm, j., "Application of Takagi-Sugeno Observers for State Estimation in a Quadrotor", 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, Florida, pp. 7530-7535, (2011).
 
[2] Mokhtari, A., M'Sirdi, N. K., Meghriche, K., and Belaidi, A., "Feedback Linearization and Linear Observer for a Quadrotor Unmanned Aerial Vehicle", Advanced Robotics, Vol. 20, pp. 71-91, (2006).
 
[3] Bouadi, H., and Tadjine, M., "Nonlinear Observer Design and Sliding Mode Control of Four Rotors Helicopter", International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, Vol. 25, pp. 225-230, (2007).
 
[4] Dierks, T., and  Jagannathan, S., "Output Feedback Control of a Quadrotor UAV using Neural Networks", in IEEE Transactions on Neural Networks, Vol. 21, pp. 50-66, (2010).
 
[5] Benzemrane, K., Santosuosso, G. L., and Damm, G., "Unmanned Aerial Vehicle Speed Estimation via Nonlinear Adaptive Observers", American Control Conference Philadelphia, U.S.A.,  pp. 985-990, (2007).
 
[6] Benallegue, A., Mokhtari, A., and Fridman, L., "High-order Sliding-mode Observer for a Quadrotor UAV", International Journal of Robust and Nonlinear Control, Vol. 18, pp. 427-440,(2008).
 
[7] Bouhali, O., and Boudjedir, H.," Neural Network Control with Neuro-sliding Mode Observer Applied to Quadrotor Helicopter", International Symposium on Innovations in Intelligent Systems and Applications, Istanbul, Turkey, pp. 24-28, (2011).
 
[8] Khalil, H. K., "High-gain Observers in Nonlinear Feedback Control", IEEE International Conference on Control and Automation, Christchurch, Newzealand, pp. 1527-1528, (2009).
 
[9] Boudjedir, H., Bouhali, O., and Rizoug, N., "Neural Network Control Based on Adaptive Observer for Quadrotor Helicopter", International Journal of Information Technology, Control and Automation (IJITCA), Vol. 2, No. 3, pp. 39-54, (2012).
 
[10] Ahmed, S. F., Kushsairy, K., Bakar, M. I. A., D., and Joyo, M. K., "Attitude Stabilization of Quad-rotor (UAV) System using Fuzzy PID Controller (an Experimental Test)", Second International Conference on Computing Technology and Information Management (ICCTIM), At Johar, Kalaysia, pp. 99-104, (2015).
 
[11] Seidabad, E. A., Vandaki, S., and Kamyad, A. V., "Designing Fuzzy PID Controller for Quadrotor", International Journal of Advanced Research in Computer Science & Technology (IJARCST), Vol. 2, pp. 221-227, (2014).
 
[12] Dikmen, I. C., Arisoy, A., and Temeltas, H., "Attitude Control of a Quadrotor", 4th International Conference on Recent Advances in Space Technologies, Istanbul, Turkey, pp. 722-727, (2009).
 
[13] Farrell, J., Sharma, M., and Polycarpou, M., "Backstepping-based Flight Control with Adaptive Function Approximation", Journal of Guidance, Control and Dynamics, Vol. 28, pp. 1089-1102, (2005).
 
[14] Bolandi, H., Rezaei, M., Mohsenipour, R., Nemati, H., and Smailzadeh, S. M., "Attitude Control of a Quadrotor with Optimized PID Controller", Journal of  Intelligent Control and Automation, Vol. 4, pp. 342-349, (2013).
 
[15] Zareb, M., Ayad, R., and Nouibat, W.,  "Fuzzy-PID Hybrid Control System to Navigate an Autonomous Mini-quadrotor", 3rd International Conference on Systems and Control (ICSC), Algiers, Algeria, pp. 906-913, (2013).
 
[16] Dunfied, J., Tarbouchi, M., and Labonte, G., "Neural Network Based Control of a Four Rotor Helicopter", IEEE International Conference on Industrial Technology, Hammamet, Tunisia, Vol. 3, pp. 1543-1548, (2004).
 
[17] Lee, B. Y., Lee, H. I., and Tahk, M. J., "Analysis of Adaptive Control using on-line Neural Networks for a Quadrotor UAV", 13th International Conference on Automation and Systems (ICCAS), Gwangju, Korea, pp. 1840-1844, (2013).
 
[18] Frye, M. T., and Provence, R. S., "Direct Inverse Control using an Artificial Neural Network for the Autonomous Hover of a Helicopter", IEEE International Conference on Systems, Man and Cybernetics (SMC), Manchester, United Kingdom, pp. 4121-4122, (2014).
 
[19] Rezazadeh, S., Ardestani, M. A., and Sadeghi, P. S., "Optimal Attitude Control of a Quadrotor UAV using Adaptive Neuro-Fuzzy Inference System (ANFIS)", 3rd International Conference on Control, Instrumentation, and Automation (ICCIA), Amirkabir University of Technology (Tehran), Iran, pp. 219-223, (2013).
 
 [20] Lower, M., and Tarnawski, W., "Quadrotor Navigation using the PID and Neural Network Controller", Advances in Intelligent System and Computing, Springer, Cham, Vol. 365, pp. 265-274, (2015).
 
[21] Boudjedir, H., Bouhali, O., and Rizoug, N., "Adaptive Neural Network Control Based on Neural Observer for Quadrotor Unmanned Aerial Vehicle", Advanced Robotics, Vol. 28, pp. 1151-1164, (2014).
Iranian Journal of Mechanical Engineering Transactions of ISME
Volume 21, Issue 1 - Serial Number 54
System Dynamics and Solid Mechanics
June 2019
Pages 97-115

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History

  • Receive Date: 23 September 2017
  • Revise Date: 10 March 2018
  • Accept Date: 27 April 2018

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