Research Output
Articles
Publication Date: 2025
Advances in Space Research (02731177)75(7)pp. 5656-5668
This study focuses on the boundary control of flexible satellites equipped with honeycomb panels using Lyapunov's direct method. The panels are modeled as Euler–Bernoulli beams, and the govern ing dynamic equations are derived through Hamilton's principle. A novel Lyapunov function candidate is introduced, and asymptotic stability is rigorously established through the extended LaSalle's invariance principle. Control input laws are strategically developed to handle actuator failures while ensuring stability with minimal sensor utilization. Numerical simulations, performed using the assumed mode method, validate the theoretical findings. The results underscore key contributions, including guaranteed asymptotic stability, large-angle maneuvering capabilities, robustness to actuator failures, and the prevention of spillover instability phenomena. © 2025 COSPAR
Publication Date: 2025
JVC/Journal of Vibration and Control (10775463)
This study presents a boundary control strategy that addresses actuator constraints to achieve simultaneous attitude tracking and vibration suppression in a flexible satellite equipped with honeycomb-structured solar arrays. To enhance adaptability and autonomy, a reinforcement learning-based mechanism is employed to automatically tune the controller gains in real time. The approach leverages temporal-difference learning for online, model-free control, with a radial basis function (RBF) neural network organized in a critic–actor architecture to approximate the value function and control policy dynamically. The effectiveness of the proposed method is validated through numerical simulations across various scenarios, including nominal attitude tracking, response to external disturbances, and tracking a new desired trajectory under system uncertainties. Furthermore, the reinforcement learning algorithm is applied to a benchmark satellite dynamic model and control architecture adopted from earlier studies. Beyond facilitating autonomous gain tuning, the proposed approach demonstrates significant enhancements in control performance relative to the outcomes of the original framework. A key contribution of this work is the improvement of controller robustness against disturbances and uncertainties through intelligent, automatic gain adaptation. These findings highlight the potential of combining boundary control with reinforcement learning to enhance performance and resilience in flexible space structures. © The Author(s) 2025
Publication Date: 2024
Journal of Vibration Engineering and Technologies (25233920)12(Suppl 1)pp. 985-996
Objective: In this paper, the nonlinear flutter of the wing is investigated under the influence of aerodynamic control surfaces. Methods: The wing aerodynamic loads are determined using Peter’s unsteady aerodynamic model, and the aerodynamic loads of the control surface are added with quasi-steady relations in the interior of the equations. The governing aeroelastic equations are presented in the structure of fully intrinsic and these equations are discretized using the finite difference method. Results: The effects of the presence of an aerodynamic control surface have been investigated based on the analytical-experimental relationships and considering the nonlinear effects of high control surface deflections. Furthermore, investigation of the effects of some important parameters such as deflections, location, chord size, and length of the control surface on the speed and frequency of flutter instability, is another achievement of this article. Conclusions: The results show that based on aeroelastic considerations, the deflection angle of the control surface has an important effect on the aeroelastic stability. Also, by bringing the control surface closer to the wing tip, increasing the thickness ratio and the chord ratio in accordance with other effective parameters, flutter suppression can be caused. © Springer Nature Singapore Pte Ltd. 2024.
