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Observer-Based Enhanced ANFIS Control for a Quadrotor UAV


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DOI: https://doi.org/10.15866/iremos.v14i1.18991

Abstract


This paper introduces a new approach for optimizing the Adaptive Network Fuzzy Inference System (ANFIS) with the (metaheuristic) particle swarm optimization (PSO) algorithm to solve a trajectory tracking problem for a UAV quadrotor system. In this work, the input-output dataset is collected from the quadrotor's time response from a Proportional Integral Derivative (PID) controller. Then, before its use in the ANFIS algorithm, the collected dataset is optimized using the PSO algorithm. Moreover, an integral control action is integrated into the proposed ANFIS controller to ensure disturbance rejection. A suitable high gain observer is utilized in estimating unmeasured states for translational and rotational motions of the quadrotor system. Compared to a conventional ANFIS and traditional PID controllers, the results of two simulation tests demonstrate the efficiency and robustness of the proposed new output-feedback PSO-ANFIS controller.
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Keywords


ANFIS; Quadrotor Tracking Control; High Gain State Observer; PSO

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References


A. Ollero et al., The AEROARMS project: Aerial robots with advanced manipulation capabilities for inspection and maintenance, IEEE Robotics & Automation Magazine, vol. 25, no. 4, pp. 12–23, 2018.
https://doi.org/10.1109/mra.2018.2852789

S. Alabachi, G. Sukthankar, and R. Sukthankar, Selfie Drone Stick: A Natural Interface for Quadcopter Photography, arXiv preprint arXiv:1909.06491, 2019.

U. R. Mogili and B. Deepak, Review on application of drone systems in precision agriculture, Procedia Computer Science, vol. 133, pp. 502–509, 2018.
https://doi.org/10.1016/j.procs.2018.07.063

M. A. R. Estrada and A. Ndoma, The uses of unmanned aerial vehicles–UAV' s-(or drones) in social logistic: Natural disasters response and humanitarian relief aid, Procedia Computer Science, vol. 149, pp. 375–383, 2019.
https://doi.org/10.1016/j.procs.2019.01.151

X. Liang, Y. Fang, N. Sun, and H. Lin, Dynamics analysis and time-optimal motion planning for unmanned quadrotor transportation systems, Mechatronics, vol. 50, pp. 16–29, 2018.
https://doi.org/10.1016/j.mechatronics.2018.01.009

E. Kaufman, K. Takami, Z. Ai, and T. Lee, Autonomous Quadrotor 3D Mapping and Exploration Using Exact Occupancy Probabilities, Second IEEE International Conference on Robotic Computing (IRC), 2018, pp. 49–55.
https://doi.org/10.1109/irc.2018.00016

Siti, I., Mjahed, M., Ayad, H., El Kari, A., New Designing Approaches for Quadcopter PID Controllers Using Reference Model and Genetic Algorithm Techniques, (2017) International Review of Automatic Control (IREACO), 10 (3), pp. 240-248.
https://doi.org/10.15866/ireaco.v10i3.12115

E. Chater, H. Housny, and H. El Fadil, Robust Control Design for Quadrotor Trajectory Path Tracking, 8th International Conference on Systems and Control (ICSC), 2019, pp. 21–26.
https://doi.org/10.1109/icsc47195.2019.8950509

Loubar, H., Boushaki, R., Aouati, A., Bouanzoul, M., Sliding Mode Controller for Linear and Nonlinear Trajectory Tracking of a Quadrotor, (2020) International Review of Automatic Control (IREACO), 13 (3), pp. 128-138.
https://doi.org/10.15866/ireaco.v13i3.18522


L. Martins, C. Cardeira, and P. Oliveira, Linear Quadratic Regulator for Trajectory Tracking of a Quadrotor, IFAC-PapersOnLine, vol. 52, no. 12, pp. 176–181, 2019.
https://doi.org/10.1016/j.ifacol.2019.11.195

R. Bonna and J. F. Camino, Trajectory tracking control of a quadrotor using feedback linearization, Proceedings of the XVII International Symposium on Dynamic Problems of Mechanics, Natal-Rio Grande Do Norte, Brazil, 2015.

H. Housny and H. El Fadil, New Deterministic Optimization Algorithm for Fuzzy Control Tuning Design of a Quadrotor, 5th International Conference on Optimization and Applications (ICOA), 2019, pp. 1–6.
https://doi.org/10.1109/icoa.2019.8727622

R. Czabanski, M. Jezewski, and J. Leski, Introduction to fuzzy systems (Theory and Applications of Ordered Fuzzy Numbers, Springer, Cham, 2017. p. 23-43).‏
https://doi.org/10.1007/978-3-319-59614-3_2

Belhadri, K., Kouadri, B., Zegai, M., Adaptive Neural Control Algorithm Design for Attitude Stabilization of Quadrotor UAV, (2016) International Review of Automatic Control (IREACO), 9 (6), pp. 390-396.
https://doi.org/10.15866/ireaco.v9i6.9919

A. B. Arrieta et al., Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI, Information Fusion, vol. 58, pp. 82–115, 2020.
https://doi.org/10.1016/j.inffus.2019.12.012

S. Rezazadeh, M. A. Ardestani, and P. S. Sadeghi, Optimal attitude control of a quadrotor UAV using Adaptive Neuro-Fuzzy Inference System (ANFIS), The 3rd International Conference on Control, Instrumentation, and Automation, Tehran, Iran, Dec. 2013, pp. 219–223.
https://doi.org/10.1109/icciautom.2013.6912838

P. Ponce, A. Molina, I. Cayetano, J. Gallardo, H. Salcedo, and J. Rodriguez, Experimental Fuzzy Logic Controller Type 2 for a Quadrotor Optimized by ANFIS, IFAC-PapersOnLine, vol. 48, no. 3, pp. 2435–2441, 2015.
https://doi.org/10.1016/j.ifacol.2015.06.453

A. Moltajaei Farid, UAV Controller Based on Adaptive Neuro-Fuzzy Inference System and PID, IAES International Journal of Robotics and Automation (IJRA), vol. 2, no. 2, pp. 73–82, Jun. 2013.
https://doi.org/10.11591/ijra.v2i2.1936

O. Ghorbanzadeh, H. Rostamzadeh, T. Blaschke, K. Gholaminia, and J. Aryal, A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping, Nat Hazards, vol. 94, no. 2, pp. 497–517, Nov. 2018.
https://doi.org/10.1007/s11069-018-3449-y

D. Karaboga and E. Kaya, Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey, Artif Intell Rev, vol. 52, no. 4, pp. 2263–2293, Dec. 2019.
https://doi.org/10.1007/s10462-017-9610-2

H. M. I. Pousinho, J. P. S. Catalao, and V. M. F. Mendes, Wind power short-term prediction by a hybrid PSO-ANFIS approach, 15th IEEE Mediterranean Electrotechnical Conference, pp. 955–960, Melecon, 2010.
https://doi.org/10.1109/melcon.2010.5475923

J. P. da S. Catalão, H. M. I. Pousinho, and V. M. F. Mendes, Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting, IEEE Transactions on Power Systems, vol. 26, no. 1, pp. 137–144, 2010.
https://doi.org/10.1109/pes.2011.6038968

H. Housny, E. A. Chater, and H. E. Fadil. Multi Closed-loop Adaptive Neuro-Fuzzy Inference System for Quadrotor Position Control, Adv. Sci. technol. Eng. Syst. j., vol. 5, no. 5, pp. 526–535, 2020.
https://doi.org/10.25046/aj050565

M. Guisser and H. Medromi, A high gain observer and sliding mode controller for an autonomous quadrotor helicopter, International Journal of Intelligent Control and Systems, vol. 14, no. 3, pp. 204–212, 2009.

Bunjaku, D., Nadzinski, G., Stankovski, M., Stefanovski, J., Dynamic Modeling and Flight Control Design for Multicopter, (2018) International Review of Aerospace Engineering (IREASE), 11 (5), pp. 224-235.
https://doi.org/10.15866/irease.v11i5.15512

S. Raza, Autonomous UAV Control for Low-Altitude Flight in an Urban Gust Environment, Doctor of Philosophy, Carleton University, Ottawa, Ontario, 2015.
https://doi.org/10.22215/etd/2015-11115

A. Benallegue, A. Mokhtari, and L. Fridman, High-order sliding-mode observer for a quadrotor UAV, International Journal of Robust and Nonlinear Control: IFAC-Affiliated Journal, vol. 18, no. 4–5, pp. 427–440, 2008.
https://doi.org/10.1002/rnc.1225

H. K. Khalil, High-gain observers in nonlinear feedback control, Society for Industrial and Applied Mathematics, 2017.‏

M. Farza, M. M'Saad, and L. Rossignol, Observer design for a class of MIMO nonlinear systems, Automatica, vol. 40, no. 1, pp. 135–143, 2004.
https://doi.org/10.1016/j.automatica.2003.08.008

J.-S. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE transactions on systems, man, and cybernetics, vol. 23, no. 3, pp. 665–685, 1993.
https://doi.org/10.1109/21.256541

J. Kennedy and R. C. Eberhart, A discrete binary version of the particle swarm algorithm, IEEE International conference on systems, man, and cybernetics. Computational cybernetics and simulation, 1997, vol. 5, pp. 4104–4108.
https://doi.org/10.1109/icsmc.1997.637339

El Gmili, N., Mjahed, M., El Kari, A., Ayad, H., An Improved Particle Swarm Optimization (IPSO) Approach for Identification and Control of Stable and Unstable Systems, (2017) International Review of Automatic Control (IREACO), 10 (3), pp. 229-239.
https://doi.org/10.15866/ireaco.v10i3.11857

H. Housny, E. Chater, and H. El Fadil, Multi-Closed-Loop Design for Quadrotor path-Tracking Control, 8th International Conference on Systems and Control (ICSC), 2019, pp. 27–32.
https://doi.org/10.1109/icsc47195.2019.8950659

H. Housny and H. El Fadil, Fuzzy PID Control Tuning Design Using Particle Swarm Optimization Algorithm for a Quadrotor, 5th International Conference on Optimization and Applications (ICOA), 2019, pp. 1–6.
https://doi.org/10.1109/icoa.2019.8727702

N. Talpur, M. N. M. Salleh, and K. Hussain, An investigation of membership functions on performance of ANFIS for solving classification problems, IOP Conf. Ser.: Mater. Sci. Eng., vol. 226, p. 012103, Aug. 2017.
https://doi.org/10.1088/1757-899x/226/1/012103

A. Sadollah, Introductory Chapter: Which Membership Function is Appropriate in Fuzzy System?, (Fuzzy Logic Based in Optimization Methods and Control Systems and its Applications, A. Sadollah, Ed. InTech, 2018).
https://doi.org/10.5772/intechopen.79552

Almabrok, Abdoalnasir, Mihalis Psarakis, and Anastasios Dounis. Fast Tuning of the PID Controller in An HVAC System Using the Big Bang–Big Crunch Algorithm and FPGA Technology. Algorithms 11.10 (2018): 146.
https://doi.org/10.3390/a11100146

T. K. Priyambodo, A. Dharmawan, O. A. Dhewa, and N. A. S. Putro, Optimizing control based on fine tune PID using ant colony logic for vertical moving control of UAV system, AIP Conference Proceedings, 2016, vol. 1755, p. 170011.
https://doi.org/10.1063/1.4958613

S. Bouabdallah, Design and control of quadrotors with application to autonomous flying, (Epfl, 2007).
https://doi.org/10.5075/epfl-thesis-3727


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