Bingöl, Özhan

This study proposes a practical predefined-time adaptive control framework for the synchronisation and anti-synchronisation of chaotic satellite attitude dynamics subject to unknown nonlinearities, parametric uncertainties, and bounded external disturbances. The proposed controller combines radial-basis-function (RBF) neural-network approximation of lumped unknown dynamics with adaptive estimation laws for both physically meaningful satellite parameters and neural-network weights. A Lyapunov-based stability analysis proves that all closed-loop signals remain bounded and that the synchronisation errors converge to an explicitly characterised residual neighbourhood of the origin within a predefined upper-bound time independent of initial conditions. The residual bound is expressed in terms of the neural approximation error, adaptive gains, and predefined-time design parameters, thereby providing practical tuning insight. Comparative numerical simulations demonstrate that the proposed approach achieves fast convergence, reduced actuator effort, and improved robustness under varying inertia, disturbances, and initial conditions. These results indicate the suitability of the proposed method for satellite formation and attitude-control tasks requiring predictable convergence times and resilience to modelling uncertainties.

Bingöl, Özhan

This study investigates the synchronization and anti-synchronization of both identical and non-identical chaotic satellite systems. A fixed-time sliding mode control framework, based on a radial basis function (RBF) neural network, has been developed to synchronize the chaotic dynamics of master–slave satellite configurations. The proposed control scheme operates under the assumption that the dynamics of the satellites are not entirely known. The proposed control method guarantees that system errors will converge to negligible levels within a fixed time. Furthermore, the controller exhibits robustness in the presence of parametric uncertainties and external disturbances. The stability of the controlled systems is rigorously validated through Lyapunov analysis, and the controller's effectiveness is confirmed through extensive simulation studies. These simulations are conducted on both identical and non-identical satellite models, with performance comparisons made against recent findings reported in the literature.

Bingöl, Özhan; Güzey, Hacı Mehmet

This study develops a fixed-time neuro-sliding mode controller to enhance the transient response and robustness of a quadrotor that is subjected to external disturbances and parameter uncertainties along with the dynamics of a suspended payload. A simple neural network with a fixed-time adaptive law is used to estimate unknown dynamics; therefore, no previous knowledge of dynamics is required to develop controllers. The neural network component will also help to eliminate the chattering effect of the sliding mode controller through its learning structure. Fixed-time convergence to desired trajectories is proven by detailed theoretical analysis based on the Lyapunov stability theorem, and to show the proposed controller's effective performance, thorough numerical simulations are run.

Bingöl, Özhan; Güzey, Hacı Mehmet

Due to the quadrotor’s underactuated nature, suspended payload dynamics, parametric uncertainties, and external disturbances, designing a controller for tracking the desired trajectories for a quadrotor that carries a suspended payload is a challenging task. Furthermore, one of the most significant disadvantages of designing a controller for nonlinear systems is the infinite-time convergence to the desired trajectory. In this paper, a finite-time neuro-sliding mode controller (FTNSMC) for a quadrotor with a suspended payload that is subject to parametric uncertainties and external disturbances is designed. By constructing a finite-time sliding mode controller, the quadrotor can follow the reference trajectories in finite time. Furthermore, despite time-varying nonlinear dynamics, parametric uncertainties, and external disturbances, a neural network structure is added to the controller to effectively reduce chattering phenomena caused by high switching gains, and significantly reduce the size of the control signals. Following the completion of the controller design, the system’s stability is demonstrated using the Lyapunov stability criterion. Extensive numerical simulations with various scenarios are run to demonstrate the effectiveness of the proposed controller.

Bingöl, Özhan; Güzey, Haci Mehmet; Ronzhin, A.; Shishlakov, V.

In this study, a second-order sliding mode control (2-SMC) strategy has been developed to control a small quadrotor UAV with a suspended payload. It is shown that the existence of a load will cause an oscillation on the system and its movement will remarkably change the dynamics of the quadrotor. In order to overcome this, an upgraded 2-SMC has been suggested to ensure the stability of the system. Firstly, the mathematical model of the quadrotor-suspended payload system is derived based on Lagrange approach, then the suggested controller is designed, and the stability of the system is proved by Lyapunov theory. Finally, numerical simulations are carried out to demonstrate the robustness of the proposed method.