321 papers
This paper proposes and validates a unified hybrid control architecture that integrates model predictive control with feedback regulation and machine learning-based hardware implementation to effectively manage the complex, nonlinear dynamics of multi-DOF robotic manipulators while ensuring stability and high computational efficiency.
This paper proposes a receding-horizon maximum-likelihood estimator that jointly identifies Neural ODE dynamics and unknown contrast thresholds from asynchronous event camera streams by modeling events as a history-dependent marked point process and optimizing a log-likelihood objective via gradient steps on a sliding window.
This paper presents a formal control synthesis framework for continuous-state stochastic systems that minimizes a linear combination of system entropy (measured by KL divergence to uniform) and cumulative cost by deriving novel bounds on the entropy difference between continuous distributions and their finite-state abstractions, thereby enabling entropy-aware controllers with rigorous performance guarantees.
This paper proposes a nonlinear feedback controller utilizing log-type Barrier Lyapunov Functions to achieve trajectory tracking for uncrewed surface vessels while simultaneously enforcing asymmetric position and heading constraints, symmetric velocity constraints, and input saturation limits, with rigorous stability analysis and simulations confirming that all states remain within prescribed bounds.
This paper proposes a cooperative game theory framework to determine the optimal and stable partitioning of distribution grids into local energy market coalitions, demonstrating that while the largest coalition is optimal under deterministic conditions, a specific algorithm is required to address the complexities introduced by stochastic prosumption and grid constraints.
This paper presents methods to compute the Scaled Relative Graph (SRG) of discrete-time linear-time-invariant systems exactly from state-space models and exclusively from input-output data, while also introducing a robust SRG formulation capable of handling noisy trajectories.
This paper introduces Curve-induced Dynamical Systems on Smooth Manifolds (CDSM), a real-time framework that generates stable and adaptable robotic behaviors on Riemannian manifolds and Lie groups by constructing dynamical systems with tangential and normal components relative to a nominal curve, demonstrating superior accuracy and efficiency in both benchmarks and practical robotic applications.
This paper presents a Vehicle-in-the-Loop and digital twin-based framework that integrates a physical test vehicle on a dynamometer with a synchronized virtual environment to enable safe, cost-effective, and realistic validation of autonomous driving algorithms on centralized E/E architectures without requiring individual ECU testing or intermediate software layers.
This paper proposes a robust stochastic guidance framework that explicitly addresses time-varying estimation delays in pursuit-evasion scenarios by integrating a particle-based fixed-lag smoother with semi-Markov maneuver modeling to enable real-time delay estimation and adaptive guidance adjustments.
This paper introduces MPPI-DK, a computationally efficient model predictive path integral control framework that leverages a learned linear deep Koopman operator to replace nonlinear dynamics for faster trajectory sampling, achieving near-optimal performance with significantly reduced computational costs in both simulation and real-world robotic applications.
This white paper proposes a structured reduced-search enumeration algorithm that achieves near-maximum-likelihood hard-decision performance in high-order MIMO systems with linear complexity, demonstrating through simulations that specific list sizes relative to constellation size effectively match full ML results even under challenging channel conditions.
NL2GDS is a novel framework that leverages large language models to automatically translate natural language hardware specifications into synthesizable RTL and complete GDSII layouts via the OpenLane flow, achieving significant improvements in area, delay, and power efficiency while democratizing ASIC design.
This paper establishes a superposition theorem for input-to-output stability in a broad class of nonlinear infinite-dimensional systems by introducing novel stability concepts and criteria, while also addressing the specific challenges of extending such results from finite-dimensional and state-based frameworks through counterexamples.
This paper proposes a forecast-based multi-horizon optimization approach that reduces the carbon footprint of energy-intensive, location-constrained services like generative AI by dynamically adjusting response quality tiers in response to grid carbon intensity, achieving up to 10% emission reductions while adhering to annual carbon budgets.
This paper proposes a novel Linear Parameter-Varying (LPV) framework utilizing Integral Quadratic Constraints (IQCs) to analyze and establish quantitative tracking error bounds for iterative first-order optimization algorithms applied to time-varying convex problems, where the bounds depend on specific measures of temporal variability such as function value and gradient rates of change.
This paper introduces Space-O-RAN, a distributed control architecture that extends Open RAN principles to satellite constellations by deploying lightweight onboard applications and hierarchical coordination via SpaceRIC and terrestrial SMO to enable intelligent, autonomous, and interoperable Non-Terrestrial Networks for 6G.
This paper proposes a self-supervised UAV trajectory planning framework that integrates learning-based depth perception with differentiable optimization and neural time allocation to achieve robust, label-free navigation in 3D environments, significantly outperforming state-of-the-art methods in tracking accuracy and control efficiency.
This paper presents an iterative tangential interpolation algorithm for reducing large-scale MIMO systems that leverages interpolation weight freedom and low-rank data to optimize error proxies, ensuring monotonic error reduction while offering trade-offs between computational complexity and approximation performance comparable to standard methods.
This paper proposes and experimentally validates a robust control strategy for regulating the outlet temperature of a counter-current heat exchanger by modeling it as a structured bilinear system and implementing both an output feedback controller with a state observer and a purely integral control law.
This paper introduces zono-conformal prediction, a data-efficient method that utilizes zonotopes to generate less conservative, statistically valid uncertainty sets for both regression and classification tasks by directly embedding these sets into the base predictor via a single linear program.