http://yetl.yabesh.ir/yetl1/handle/yetl/19047 2026-08-23T23:42:37Z http://yetl.yabesh.ir/yetl1/handle/yetl/4316947 Robust Prescribed Performance Control for Heavy-Haul Freight Trains Under Actuator Faults and Input Time Delays Nguyen, Tien Dung; Nguyen, Tung Lam; Le, Duc Thinh Abstract. With the rapid development of autonomous vehicles, heavy-haul freight trains (HhFT), with their capability of transporting large volumes of cargo over long distances, are emerging as promising subjects for research in automatic control. One of the main challenges in operating HhFT lies in ensuring accurate trajectory tracking performance under the influence of adverse factors such as external disturbances, model uncertainties, and actuator-related issues, including actuator faults and input time delays. If not effectively addressed, these factors can significantly degrade the control performance of the system. This paper presents a robust trajectory tracking control system with prescribed performance for HhFTs using multiple electric locomotives. In this framework, a prescribed performance function (PPF) is designed to ensure that the position tracking errors of the locomotives remain within a predefined bound. Simultaneously, an extended state observer (ESO) is employed to estimate the states and the lumped disturbances representing the adverse effects. Based on the outputs of the PPF and the ESO, a robust sliding mode controller (RSMC) is designed. The stability of the closed-loop system is proven through Lyapunov stability theory, showing that all system states converge to a neighborhood of the origin in finite time. Computer simulation results clearly demonstrate the superior control performance of the proposed system compared to previously introduced methods. 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4316943 Mooring Actuation for Stabilization of Floating Offshore Wind Turbines Hasan, Tajnuba; Sarker, Doyal; Ngo, Tri; Das, Tuhin Abstract. The oscillatory motion of floating offshore wind turbines (FOWTs) under erratic sea conditions negatively impacts power generation efficiency, increases structural fatigue loading, and reduces system longevity. While individual blade pitch control can be used to regulate rotor speed and stabilize platform dynamics, it imposes significant mechanical loads on the pitch actuators. To alleviate this burden, mooring line actuation (MLA) offers a promising complementary strategy. This paper investigates the potential and challenges of mooring line actuation for dynamic stabilization of FOWTs. Two platform configurations-a spar-buoy and a tension-leg platform (TLP)-are modeled and validated. For TLP, a tuned mass damper (TMD) strategy is examined as a benchmark for comparative stabilization performance. A comprehensive controllability investigation is conducted to evaluate the effectiveness of MLA in influencing platform degrees-of-freedom (DOFs). Based on these insights, a linear quadratic regulator (LQR) controller is designed to modulate mooring line lengths and associated tensions for active stabilization. Numerical simulations reveal that MLA provides significantly greater stabilization benefits for the TLP configuration compared to the spar-buoy. This underscores the importance of integrated control co-design (CCD) to improve MLA performance, especially for platforms with lower inherent controllability. Across a range of operational scenarios, the proposed MLA strategy demonstrates effective simultaneous surge and pitch suppression with minimal mooring line actuation, offering a viable path toward load-reducing, performance-enhancing control architectures in next-generation FOWTs. 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4316938 Alternating Learning for Modular Sensorimotor Control of a Flapping Wing Unmanned Aerial Vehicle Tejaswi, K. C.; Lee, Taeyoung Abstract. This paper introduces a data-driven sensorimotor control framework for a flapping-wing unmanned aerial vehicle (FWUAV). It integrates an imitation learning algorithm for optimal controls with a deep neural pose estimation scheme. Recognizing that a direct concatenation of the neural pose estimator with the learning-based controller fails, we propose an alternating learning algorithm, namely, ALICE, for the coordinated integration of the two learning schemes. In particular, we enhance the learning capability of the estimator and the controller such that they converge to a synergistic pair. The proposed framework demonstrates excellent stabilizing capabilities compared to alternative ablated strategies or even an end-to-end controller. Furthermore, the presented technique overcomes the common restrictions of existing methods for FWUAV control, particularly the requirement for high-frequency flapping to justify linearization over averaged dynamics. 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4316933 Physics-Informed Dynamical Modeling of Extrusion-Based Three-Dimensional Printing Processes Looey, Mandana Mohammadi; Scalise, Marissa Loraine; Basak, Amrita; Dey, Satadru Abstract. The tradeoff between model fidelity and computational cost remains a central challenge in the computational modeling of extrusion-based 3D printing, particularly for real-time optimization and control. Although high-fidelity simulations have advanced considerably for offline analysis, dynamical modeling tailored for online, control-oriented applications is still significantly underdeveloped. In this study, we propose a reduced-order dynamical flow model that captures the transient behavior of extrusion-based 3D printing. The model is grounded in physics-based principles derived from the Navier–Stokes equations and further simplified through spatial averaging and input-dependent parameterization. To assess its performance, the model is identified via a nonlinear least-squares approach using computational fluid dynamics (CFD) simulation data spanning a range of printing conditions and subsequently validated across multiple combinations of training and testing scenarios. The results demonstrate strong agreement with the CFD data within the nozzle, the nozzle–substrate gap, and the deposited-layer regions. Overall, the proposed reduced-order model successfully captures the dominant flow dynamics of the process while maintaining a level of simplicity compatible with real-time control and optimization. 2026-01-01T00:00:00Z