http://yetl.yabesh.ir/yetl1/handle/yetl/4273117 2026-08-27T12:37:28Z http://yetl.yabesh.ir/yetl1/handle/yetl/4315930 Adversarial Models for Understanding Fundamental Limitations of Attack Detection in Vehicle Platoons Vyas, Shashank Dhananjay; Dey, Satadru Abstract. Connected and autonomous vehicles (CAVs) are becoming a promising intelligent transportation technology, which can be beneficial in reducing transportation energy consumption as well as human-induced accident risks. However, with the heavy reliance on communication and information technologies, CAVs also pose significant security risks in the form of adversarial cyber-attacks. Recent research works have been exploring various solutions to the security problems in CAVs. However, a key research gap exist in the fact that there is no standard adversarial threat model for CAV applications. Typical threat models used in analyzing security risks are mostly ad hoc. This brief attempts to address this research gap by proposing an adversarial threat model for CAVs. Specifically, we propose an adversarial threat model for vehicular platoon applications, which utilizes underlying physics model in conjunction with optimal control theory to generate intelligent attack policies. Such threat model will be useful in (i) testing the effectiveness of security solutions in a formal way and (ii) uncovering the fundamental limitation of model and real-time data-based attack detection strategies. 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4315928 Robust Motion Trajectories for an Uncertain Flexible Robotic System Using Ensemble Control Bhattacharjee, Shambo; Karpenko, Mark Abstract. Controlling one or more flexible components connected to a maneuvering robotic structure has been a long-standing problem in the robotics community. Typical applications include the control of flexible beams, robotic manipulators, spacecraft systems, and cranes. A large number of different approaches for solving this problem have been proposed, e.g., phase-plane trajectory analysis, switching property analysis, Pontryagin’s maximization principle, and various input shaping methods. Many of the studies have used a canonical spring–mass–damper system as a proxy for the practical plant. The previous approaches offer limited robustness in the presence of uncertainty in the flexible modes, which are generally challenging to model accurately. This article presents a new approach based on the concept of ensemble control to improve the robustness of motion control for flexible systems. In particular, rest-to-rest time-optimal slewing control of a planar structure is studied. Compared to available standard methods, the proposed method is observed to offer superior performance. Several examples are presented to illustrate the efficacy of the approach. 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4315927 Speed Aware Hybrid Adaptive Optimal Control of Bicycle Dynamics With Sliding-Mode Robustness Riyami, Hawriya Saleh Al; Khan, Gulam Dastagir; Al-Naimi, Ibrahim; Al-Saadi, Taha Abstract. The stability of a bicycle changes qualitatively with forward speed: an unstable low-speed weave mode transitions into a lightly damped self-stable region and eventually gives rise to high-speed capsize instability. Because no single controller can deliver uniform performance across these distinct regimes, this article develops a speed aware hybrid control architecture for the benchmark Carvallo–Whipple bicycle model. The velocity domain is partitioned into three physically meaningful regions—low-speed weave, mid-speed self-stability, and high-speed capsize—each governed by a regime-appropriate controller: (1) a high-gain Guard stabilizer at low-speed, (2) a Lyapunov-based model reference adaptive controller (MRAC) in the self-stable band, and (3) a velocity parameterized linear quadratic regulator (LQR), refreshed via recursive least-squares identification, at high-speed. All regimes share a thin-boundary sliding-mode augmentation that provides matched-disturbance robustness with reduced chattering. A multiple Lyapunov functions (MLF) framework formally establishes practical stability under hysteresis and dwell-time-based switching. Controller performance is evaluated on the linearized Whipple model, while open-loop comparisons between linear and nonlinear dynamics confirm that the linear model captures the dominant modal behavior across representative speeds. Simulation results demonstrate improved convergence, disturbance rejection, and stabilization relative to single-strategy controllers, providing a structured, stability-certified methodology for controlling underactuated vehicles with speed-dependent dynamics. 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4315926 Prediction of Essential Tremor Severity Based on TETRAS Using an LSTM Regression Model Miller, Zachary R.; Harrison, Kenneth D.; Roper, Jaimie A.; Rose, Chad G. Abstract. Essential tremor (ET) is a neurological disorder that causes involuntary rhythmic limb movements, which negatively impact quality of life. ET’s severity is often quantified by The Essential Tremor Rating Assessment Scale (TETRAS). However, this clinical scale suffers from inter- and intrarater error, and still requires expert clinical evaluation. Toward the development of an assessment available outside of the clinic, a long short-term memory (LSTM) network was used to predict ET severity with inertial measurement unit (IMU) data collected from 12 participants with ET rated on TETRAS. LSTM hyperparameters were tuned using a Bayesian optimization algorithm (Optuna library). The LSTM was trained on the normalized data and labels to minimize root mean square error (RMSE) and evaluated using a leave-one-out cross-validation (LOOCV). Across the 12 iterations of the LOOCV, the model achieved an RMSE of 6.72 ± 3.52. The model performs more consistently than naive median guessing but suffers from greater variance; however, the model’s performance improvement over naive median guessing was not statistically significant. Overall, the model’s performance falls close to the 10% interrater error seen in TETRAS. However, additional participants representing a larger range of the TETRAS scale could improve the prediction performance. In summary, this paper presents the first use of TETRAS to train an LSTM to predict ET severity, and motivates the acquisition of large datasets and development of this approach into a flexible app-based severity assessment, which can be administered outside of the clinic. 2026-01-01T00:00:00Z