279 papers
This paper proposes a privacy-preserving dynamic average consensus algorithm that utilizes randomly generated sinusoidal masking signals to protect reference data from eavesdroppers while maintaining convergence, and demonstrates its practical effectiveness through state-of-charge balancing in battery energy storage systems.
This survey presents a structured taxonomy and critical analysis of cloud-based 6G deployments, examining current solutions from major cloud providers, key technical challenges like security and scalability, and future directions such as AI-driven orchestration to guide the transition from hardware-bound to cloud-native cellular networks.
This paper proposes a novel field-free 3-bit spintronic flash ADC architecture utilizing perpendicular SOT MTJs with VCMA and STT switching, which achieves a 304.1 MHz conversion rate and 476 µW power consumption by employing a dual-set dummy/conversion scheme to eliminate the reset step.
This paper proposes a learning-augmented primal-dual control framework for secondary frequency regulation that guarantees asymptotic stability and steady-state optimality while using neural networks to optimize transient performance metrics such as frequency nadir and control effort.
This paper investigates the implementation of embedded Model Predictive Control for high-speed EMS-type maglev vehicles, demonstrating its ability to robustly stabilize the highly nonlinear system at speeds exceeding 600 km/h while validating the algorithm's performance on resource-constrained microcontrollers through processor-in-the-loop studies.
This paper proposes an infinite-horizon Model Predictive Control framework that achieves constrained finite-time stabilization for discrete-time linear and feedback-linearizable nonlinear systems by replacing short-horizon terminal costs with an infinite sum of stage costs, thereby significantly enlarging the initial feasibility region while ensuring computational tractability and eliminating the need for terminal equality constraints or switching strategies.
This paper proposes a vector-field guided constraint-following control approach that solves the dynamics-level geometric path-following problem for uncertain mechanical systems, effectively handling both fully-actuated and underactuated configurations with heterogeneous, fast time-varying uncertainties of unknown bounds and self-intersecting desired paths.
This paper proposes a unified, system-wide dynamic performance metric for inverter-based resource (IBR) networks that combines weighted local voltage phasor variations with complex power injections to decompose grid dynamics into device-driven and network-driven components, validated through sensitivity analysis on a modified IEEE 39-bus system.
This paper proposes a Two-Stage Distributionally Robust Model Predictive Control (TSDR-MPC) framework that utilizes a Wasserstein ambiguity set and a cutting-plane algorithm to achieve adaptive constraint tightening and guaranteed closed-loop stability for systems subject to disturbances with unknown, time-varying distributions.
This paper addresses the functional state estimation problem for linear time-delay systems with distinct state and measurement delays by proposing three observer structures, establishing algebraic existence conditions, and introducing a functional augmentation framework to facilitate the systematic design of observers with varying orders.
This paper introduces a method for computing less conservative -gain bounds for nonlinear Lur'e systems over restricted frequency and amplitude ranges by combining Scaled Relative Graphs with Sobolev theory, resulting in a three-dimensional nonlinear generalization of the Bode diagram that recovers both the standard LTI Bode plot and the global -gain as limiting cases.
This paper demonstrates that for approximately memoryless surjective POST channels, feedback does not increase channel capacity, thereby extending Shannon's classical result on discrete memoryless channels to a broader class of finite-state channels.
This study demonstrates that reducing the penetration speed of nails in large-format lithium-ion pouch cells prevents thermal runaway, causing the cells to self-discharge instead, thereby highlighting penetration velocity as a critical factor for improving battery safety protocols.
This paper proposes a sequential convex programming-based Model Predictive Control framework for station-keeping spacecraft formations on libration point orbits that ensures recursive feasibility and prevents inter-sample constraint violations while maintaining propellant efficiency comparable to existing methods.
This paper proposes a novel, biologically plausible spiking control framework based on the free energy principle, where neurons fire only to reduce internal free energy, achieving efficient and robust performance against both external and internal perturbations while offering new insights for neuromorphic hardware implementation.
This paper presents a comprehensive dynamic modeling and stability analysis of a grid-connected Integrated Energy System combining Small Modular Reactors and battery storage to power data centers, demonstrating through IEEE 118-bus simulations that this configuration significantly enhances voltage and frequency stability compared to conventional grid connections.
This paper proposes a neural network-based tuning method for the parameters of a finite state model predictive controller in a five-phase induction motor drive, with its effectiveness validated through experimental step tests.
This paper rigorously proves that incorporating local updates into the distributed DIGing algorithm can provably accelerate convergence for a broad class of objective functions, demonstrating that two local updates are sufficient to achieve maximal improvement without requiring reduced step sizes.
This study demonstrates that while the multichannel linear descriptors-based block field method (MLD-BFM) achieves the highest accuracy in decoding five finger-joint degrees of freedom from HD sEMG, its performance is not statistically superior to conventional time-domain features, though it significantly outperforms dimensionality reduction methods, highlighting the critical importance of preserving spatial resolution in high-density recordings.
This paper introduces AffPCL, a personalized collaborative learning framework that utilizes affinity-based variance reduction to automatically adapt to unknown heterogeneity among agents, achieving sample complexity improvements that seamlessly interpolate between independent learning and linear speedup without requiring prior knowledge of system similarity.