Incorporating fast and intelligent control technique into ecology: A Chebyshev neural network-based terminal sliding mode approach for fractional chaotic ecological systems

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.

Automated summary

In this study, a new neural network-based terminal sliding mode technique is proposed for stabilizing and synchronizing fractional-order chaotic ecological systems in finite-time. The Chebyshev neural network is utilized to estimate unknown functions and adjust weights based on adaptive laws, demonstrating effective performance in terms of accuracy, robustness, and convergence time in the presence of external disturbances. Additionally, the finite-time convergence and dependency of the system on the value of fractional time derivatives are investigated, with numerical simulations confirming the validity of the proposed control scheme.