Incorporating fast and intelligent control technique into ecology: A Chebyshev neural network-based terminal sliding mode approach for fractional chaotic ecological systems |
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| Institution: | 3. Department of Mechanical Engineering, University of Manitoba, Winnipeg, R3T 5V6, Canada;4. Department of Mathematics, Gauhati University, Guwahati, India;5. Facultad de Ingeniería Mecánica y Eléctrica, Universidad Autónoma de Nuevo León, Av. Universidad S/N, Cd.Universitaria, San Nicolás de los Garza,N.L., C.P. 66455, México;6. Ascent Systems Technologies, Heffley Creek, BC V0E 1Z0, Canada;7. Department of Banking and Finance, FEMA, University of Malta, MSD 2080, Msida, Malta;8. European University Institute, Department of Economics, Via delle Fontanelle, 18, I-50014, Florence, Italy;9. Department of Mechanical Engineering, College of Engineering, Taif University, P.O.Box 11099, Taif 21944, Saudi Arabia;1. Department of Aerospace Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, 14395 -1561, Iran;2. School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, 14399?57131, Iran;3. School of Mathematics and Physics, China University of Geosciences, Wuhan, 430074, China;4. Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha (UCLM), 16071, Cuenca, Spain;5. European University Institute, Department of Economics, Via delle Fontanelle, 18, I-50014, Florence, Italy;6. Rimini Centre for Economic Analysis (RCEA), LH3079, Wilfrid Laurier University, 75 University Ave W., ON, N2L3C5, Waterloo, Canada;1. Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;2. Department of Mathematics, Faculty of Science, Sohag University, Sohag 82524, Egypt;3. Department of Mathematics, Jamia Millia Islamia, New Delhi 110025, India;4. Department of Biology, College of Science, University of Jeddah, Jeddah, Saudi Arabia |
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| Abstract: | In the present study, a new neural network-based terminal sliding mode technique is proposed to stabilize and synchronize fractional-order chaotic ecological systems in finite-time. The Chebyshev neural network is implemented to estimate unknown functions of the system. Moreover, through the proposed Chebyshev neural network observer, the effects of external disturbances are fully taken into account. The weights of the Chebyshev neural network observer are adjusted based on adaptive laws. The finite-time convergence of the closed-loop system, which is a new concept for ecological systems, is proven. Then, the dependency of the system on the value of the fractional time derivatives is investigated. Lastly, the proposed control scheme is applied to the fractional-order ecological system. Through numerical simulations, the performance of the developed technique for synchronization and stabilization are assessed and compared with a conventional method. The numerical simulations strongly corroborate the effective performance of the proposed control technique in terms of accuracy, robustness, and convergence time for the unknown nonlinear system in the presence of external disturbances. |
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