Systems and Control (EESS) Papers of the Week

This report reviews the protection challenges identified in recent academic literature for power systems with high shares of converter-based resources.

We present Pixel, a co-designed full-stack system for learning general-purpose mobile manipulation.

In this work, we investigate the impact of Doppler frequency shift on sub-terahertz low Earth orbit (LEO) satellite communications using OFDM transmission.

ABSTRACT This article investigates the practically predefined‐time control problem for stochastic nonlinear systems with state constraints and both state and input delays.

This paper considers online intrusion-risk assessment and graded warning for unmanned aerial vehicles under bounded measurement attacks. To address this problem, we develop a monitoring framework that combines trusted state estimation, attack-aware risk assessment, and probabilistic signal temporal logic (PrSTL).

It is structured into two primary parts: Part I covers model-based control design, detailing Feedback Error Learning (FEL), pseudocontrol strategies, and neural backstepping for strict-feedback systems, along with practical refinements such as learning rule modifications, composite learning, and discrete-time formulations, all grounded in rigorous Lyapunov stability analysis.

Here we introduce research world models, which treat a research field as a dynamical system that can be acted upon, and establish one for power engineering from 1.1 million papers.

This paper proposes a real-time, forecasting-independent control framework utilizing a Hybrid Energy Storage System (HESS) that coordinates a Battery Energy Storage System (BESS) and a supercapacitor (SC).

This paper proposes a transformer-guided variable-projection estimator that extracts the fundamental phasor, DDC parameters, and fault inception time from point-on-wave (POW) samples as an interpretable analytical expression.

We present Pixel-UMI, a pipeline for learning environment-aware whole-body policies from human data.

For linear controllers, we derive exact closed-loop maps under plant and delay mismatch and provide small-gain and $\nu$-gap robustness conditions.

Can learned latent dynamics models support the design of feedback controllers that provably stabilize the original system? For linear time-invariant systems, we show that learning linear autoencoders and latent dynamics with multi-step prediction-reconstruction losses $\unicode{x2014}$ which we term "backward losses" as they encourage latent trajectories to reconstruct full state trajectories $\unicode{x2014}$ provably recovers the structure needed for stabilization.

This paper presents a robotic harvesting method that combines RGB-D fruit localization, GA-based harvest ordering, and waypoint-based robot motion in dense and unstructured crop environments.

Using the lower-level Karush-Kuhn-Tucker conditions, we obtain an exact single-level reformulation with complementarity constraints. To address the numerical difficulties introduced by these constraints, we develop a continuation method that couples the complementarity relaxation parameter with the interior-point barrier parameter and warm-starts successive stages.

We present an emergency-braking approach that uses a predicted motion corridor to filter \ac{lidar} measurements and then decide whether the ego vehicle must brake.

Conversely, applying heavy tail-weighting in the distribution to capture these extremes can distort the underlying probability distribution. To address this issue, this paper proposes adaptive residual modeling for scenario learning (ARMSL), a two-stage framework.

We present a CUDA implementation of the canonical WENO5 + TVD-RK3 + Lax-Friedrichs level-set Hamilton-Jacobi-Isaacs solver for 2D, 3D, and 4D dynamics that is fast enough to run inside a robot's control loop.

While post-training approaches can mitigate this through explicit training, they demand expensive real-world robotics data and require computationally intensive post-training for every individual base model. To resolve these limitations, we introduce D&A (Describe & Assess): our approach decouples progress estimation into an objective, task-grounded textual description of scene dynamics followed by an assessment phase using explicit rubrics.