Novel Levenberg-Marquardt based learning algorithm for unmanned aerial vehicles. (English) Zbl 1447.93196
Summary: In this paper, Levenberg-Marquardt inspired sliding mode control theory based adaptation laws are proposed to train an intelligent fuzzy neural network controller for a quadrotor aircraft. The proposed controller is used to control and stabilize a quadrotor unmanned aerial vehicle in the presence of periodic wind gust. A proportional-derivative controller is firstly introduced based on which fuzzy neural network is able to learn the quadrotor’s control model on-line. The proposed design allows handling uncertainties and lack of modelling at a computationally inexpensive cost. The parameter update rules of the learning algorithms are derived based on a Levenberg-Marquardt inspired approach, and the proof of the stability of two proposed control laws are verified by using the Lyapunov stability theory. In order to evaluate the performance of the proposed controllers extensive simulations and real-time experiments are conducted. The 3D trajectory tracking problem for a quadrotor is considered in the presence of time-varying wind conditions.
MSC:
| 93C42 | Fuzzy control/observation systems |
| 93C40 | Adaptive control/observation systems |
| 93B12 | Variable structure systems |
| 93C85 | Automated systems (robots, etc.) in control theory |
| 93B70 | Networked control |
Keywords:
fuzzy neural networks; sliding mode control; Levenberg-Marquardt algorithm; type-1 fuzzy logic control; unmanned aerial vehicleReferences:
| [1] | Alejo, D.; Cobano, J. A.; Heredia, G.; Ollero, A., Collision-free 4d trajectory planning in unmanned aerial vehicles for assembly and structure construction, J. Intell. Robotic Syst., 73, 1-4, 783 (2014) |
| [2] | Castillo, O.; Amador-Angulo, L.; Castro, J. R.; Garcia-Valdez, M., A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems, Inf. Sci., 354, 257-274 (2016) |
| [3] | Castillo, O.; Castro, J. R.; Melin, P.; Rodriguez-Diaz, A., Universal approximation of a class of interval type-2 fuzzy neural networks in nonlinear identification, Adv. Fuzzy Syst., 2013, 7 (2013) · Zbl 1323.68427 |
| [4] | Castillo, O.; Cervantes, L.; Soria, J.; Sanchez, M.; Castro, J. R., A generalized type-2 fuzzy granular approach with applications to aerospace, Inf. Sci., 354, 165-177 (2016) |
| [5] | Celikyilmaz, A.; Turksen, I. B., Modeling Uncertainty with Fuzzy Logic: With Recent Theory and Applications (2009), Springer Publishing Company, Incorporated · Zbl 1168.68051 |
| [6] | Cervantes, L.; Castillo, O., Type-2 fuzzy logic aggregation of multiple fuzzy controllers for airplane flight control, Inf. Sci., 324, 247-256 (2015) |
| [7] | Dong, Y.; Jun, F.; Bin, Y.; Youmin, Z.; Jianliang, A., Position and heading angle control of an unmanned quadrotor helicopter using LQR method, Control Conference (CCC), 2015 34th Chinese, 5566-5571 (2015) |
| [8] | Eresen, A.; Imamoglu, N.; Efe, M. O., Autonomous quadrotor flight with vision-Based obstacle avoidance in virtual environment, Expert Syst. Appl., 39, 1, 894-905 (2012) |
| [9] | Fakurian, F.; Menhaj, M. B.; Mohammadi, A., Design of a fuzzy controller by minimum controlling inputs for a quadrotor, Robotics and Mechatronics (ICRoM), 2014 Second RSI/ISM International Conference on, 619-624 (2014) |
| [10] | Fu, C.; Sarabakha, A.; Kayacan, E.; Wagner, C.; John, R.; Garibaldi, J. M., A comparative study on the control of quadcopter uavs by using singleton and non-singleton fuzzy logic controllers, 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1023-1030 (2016) |
| [11] | Gaxiola, F.; Melin, P.; Valdez, F.; Castillo, O., Interval type-2 fuzzy weight adjustment for backpropagation neural networks with application in time series prediction, Inf. Sci., 260, 1-14 (2014) · Zbl 1328.68164 |
| [12] | Gaxiola, F.; Melin, P.; Valdez, F.; Castillo, O., Generalized type-2 fuzzy weight adjustment for backpropagation neural networks in time series prediction, Inf. Sci., 325, 159-174 (2015) |
| [13] | Hagras, H. A., A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots, IEEE Trans. Fuzzy Syst., 12, 4, 524-539 (2004) |
| [14] | Hernandez-Gonzalez, M.; Alanis, A.; Hernandez-Vargas, E., Decentralized discrete-time neural control for a quanser 2-DOF helicopter, Appl. Soft Comput., 12, 8, 2462-2469 (2012) |
| [15] | Hofer, M.; Muehlebach, M.; D’Andrea, R., Application of an approximate model predictive control scheme on an unmanned aerial vehicle, 2016 IEEE International Conference on Robotics and Automation (ICRA), 2952-2957 (2016) |
| [16] | Hua, M. D.; Hamel, T.; Morin, P.; Samson, C., Introduction to feedback control of underactuated vtool vehicles: a review of basic control design ideas and principles, IEEE Control Syst., 33, 1, 61-75 (2013) · Zbl 1395.93395 |
| [17] | Khanesar, M. A.; Kayacan, E., Levenberg-Marquardt training method for type-2 fuzzy neural networks and its stability analysis, Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on, 1-7 (2015) |
| [18] | Kim, C. J.; Chwa, D., Obstacle avoidance method for wheeled mobile robots using interval type-2 fuzzy neural network, IEEE Trans. Fuzzy Syst., 23, 3, 677-687 (2015) |
| [19] | Kumbasar, T.; Hagras, H., Big bang-big crunch optimization based interval type-2 fuzzy PID cascade controller design strategy, Inf. Sci., 282, 277-295 (2014) |
| [20] | Lee, T.; Leok, M.; McClamroch, N. H., Nonlinear robust tracking control of a quadrotor UAV on SE(3), 2012 American Control Conference (ACC), 4649-4654 (2012) |
| [21] | Lin, F. J.; Hung, Y. C.; Ruan, K. C., An intelligent second-order sliding-mode control for an electric power steering system using a wavelet fuzzy neural network, IEEE Trans. Fuzzy Syst., 22, 6, 1598-1611 (2014) |
| [22] | Ma, X.; Sun, F.; Li, H.; He, B., Neural-network-based sliding-mode control for multiple rigid-body attitude tracking with inertial information completely unknown, Inf. Sci., 400401, 91-104 (2017) · Zbl 1432.93056 |
| [23] | Mahony, R.; Kumar, V.; Corke, P., Multirotor aerial vehicles: modeling, estimation, and control of quadrotor, IEEE Robotics Automat. Mag., 19, 3, 20-32 (2012) |
| [24] | Martinez-Martinez, V.; Gomez-Gil, F. J.; Gomez-Gil, J.; Ruiz-Gonzalez, R., An artificial neural network based expert system fitted with genetic algorithms for detecting the status of several rotary components in agro-Industrial machines using a single vibration signal, Expert Syst. Appl., 42, 1718, 6433-6441 (2015) |
| [25] | Mendel, J.; Hagras, H.; Tan, W.-W.; Melek, W. W.; Ying, H., Introduction to type-2 fuzzy logic control: Theory and applications (2014), John Wiley & Sons · Zbl 1380.93003 |
| [26] | Michael, N.; Fink, J.; Kumar, V., Cooperative manipulation and transportation with aerial robots, Auton. Robots, 30, 1, 73-86 (2011) |
| [27] | Mukherjee, I.; Routroy, S., Comparing the performance of neural networks developed by using Levenberg-Marquardt and quasi-Newton with the gradient descent algorithm for Modelling a Multiple Response Grinding Process, Expert Syst. Appl., 39, 3, 2397-2407 (2012) |
| [28] | Olivares-Mendez, M. A.; Fu, C.; Ludivig, P.; Bissyande, T. F.; Kannan, S.; Zurad, M.; Annaiyan, A.; Voos, H.; Campoy, P., Towards an autonomous vision-based unmanned aerial system against wildlife poachers, Sensors, 15, 12, 29861 (2015) |
| [29] | Perez-Ortiz, M.; Pena, J. M.; Gutierrez, P. A.; Torres-Sanchez, J.; Hervas-Martinez, C.; Lopez-Granados, F., Selecting patterns and features for between- and within- crop-row weed mapping using UAV-Imagery, Expert Syst. Appl., 47, 85-94 (2016) |
| [30] | Salim, J.; Ismail, M.; Suwarno, I.; Nawi, N. M.; Khan, A.; Rehman, M., A New Levenberg-Marquardt based Back Propagation Algorithm Trained with Cuckoo Search, 4th International Conference on Electrical Engineering and Informatics, ICEEI 2013, 11, 18-23 (2013) |
| [31] | Salimifard, M.; Safavi, A. A., Nonlinear system identification based on a novel adaptive fuzzy wavelet neural network, 2013 21st Iranian Conference on Electrical Engineering (ICEE), 1-5 (2013) |
| [32] | Sanchez, M. A.; Castillo, O.; Castro, J. R., Generalized type-2 fuzzy systems for controlling a mobile robot and a performance comparison with interval type-2 and type-1 fuzzy systems, Expert Syst. Appl., 42, 14, 5904-5914 (2015) |
| [33] | Sarabakha, A.; Kayacan, E., Y6 tricopter autonomous evacuation in an indoor environment using Q-learning algorithm, 2016 IEEE 55th Conference on Decision and Control (CDC), 5992-5997 (2016) |
| [34] | Sun, J.; Li, B.; Jiang, Y.; Wen, C.-y., A camera-based target detection and positioning uav system for search and rescue (sar) purposes, Sensors, 16, 11 (2016) |
| [35] | Tai, K.; El-Sayed, A.-R.; Biglarbegian, M.; Gonzalez, C. I.; Castillo, O.; Mahmud, S., Review of recent type-2 fuzzy controller applications, Algorithms, 9, 2 (2016) |
| [36] | Topalov, A. V.; Kayacan, E.; Oniz, Y.; Kaynak, O., Adaptive neuro-fuzzy control with sliding mode learning algorithm: application to antilock braking system, 2009 7th Asian Control Conference, 784-789 (2009) |
| [37] | Torres, M.; Pelta, D. A.; Verdegay, J. L.; Torres, J. C., Coverage path planning with unmanned aerial vehicles for 3D terrain reconstruction, Expert Syst. Appl., 55, 441-451 (2016) |
| [38] | Valente, J.; Sanz, D.; Barrientos, A.; Cerro, J.d.; Ribeiro, Á.; Rossi, C., An air-ground wireless sensor network for crop monitoring, Sensors, 11, 6, 6088-6108 (2011) |
| [39] | Vanegas, F.; Campbell, D.; Eich, M.; Gonzalez, F., Uav based target finding and tracking in gps-denied and cluttered environments, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2307-2313 (2016) |
| [40] | Wai, R. J.; Muthusamy, R., Fuzzy-neural-network inherited sliding-mode control for robot manipulator including actuator dynamics, IEEE Trans. Neural Netw. Learn Syst., 24, 2, 274-287 (2013) |
| [41] | Wallace, L.; Lucieer, A.; Watson, C. S., Evaluating tree detection and segmentation routines on very high resolution uav lidar data, IEEE Trans. Geosci. Remote Sens., 52, 12, 7619-7628 (2014) |
| [42] | Wang, N.; Er, M. J.; Han, M., Dynamic tanker steering control using generalized ellipsoidal-basis-function-based fuzzy neural networks, IEEE Trans. Fuzzy Syst., 23, 5, 1414-1427 (2015) |
| [43] | Zhou, H.; Kong, H.; Wei, L.; Creighton, D.; Nahavandi, S., Efficient road detection and tracking for unmanned aerial vehicle, IEEE Trans. Intell. Transp. Syst., 16, 1, 297-309 (2015) |
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