287 papers
This paper proposes CN-CBF, a composite neural control barrier function method that combines multiple Hamilton-Jacobi-trained neural CBFs with a residual architecture to enable safe, non-conservative robot navigation in dynamic environments, achieving up to 18% higher success rates than baselines in both simulation and hardware experiments.
The paper introduces PolyFormer, a physics-informed machine learning framework that transforms complex physical constraints into efficient polytopic reformulations, achieving massive computational speedups and memory reductions while maintaining high solution quality for scalable optimization problems.
This paper establishes finite-sample guarantees for cost-driven state representation learning in infinite-horizon time-invariant Linear Quadratic Gaussian (LQG) control by analyzing two approaches—explicit latent modeling and implicit MuZero-like dynamics—while introducing a key technical proof of persistency of excitation for a novel stochastic process arising from quadratic regression.
This paper explores how integrating quantum computing, sensing, and communication technologies into electrical grid edge devices can overcome the limitations of traditional systems by enabling faster optimization, atomic-precision measurements, and information-theoretic security, while also addressing the associated challenges and future directions.
The paper proposes LexiSafe, a theoretically grounded offline safe reinforcement learning framework that employs lexicographic prioritization to strictly enforce safety constraints while optimizing task performance, offering improved guarantees and empirical results over existing methods for safety-critical cyber-physical systems.
This paper presents and quantitatively evaluates an embedded EEG platform based on an ESP32-S3 and ADS1299 that achieves real-time, closed-loop SSVEP decoding with high measurement integrity, demonstrating 99.17% online accuracy and 27.66 bits/min information transfer rate entirely on-device.
This paper proposes a safety-guaranteed and optimal learning framework for autonomous systems that utilizes Weighted Signal Temporal Logic (WSTL) with structural pruning and log-transform techniques to efficiently solve preference-based learning problems as Mixed-Integer Linear Programs, validated through experiments in robotic navigation and Formula 1 racing.
This paper presents a framework for analyzing and synthesizing discrete-time optimization algorithms that guarantee certified exponential convergence and robustness against time-varying delays and unstable channel dynamics by utilizing Zames-Falb multipliers and linear matrix inequalities.
This paper presents a customized, dependency-free C-based second-order cone programming solver for embedded real-time guidance and control applications that utilizes a predictor-corrector primal-dual interior-point method with homogeneous embedding to efficiently handle quadratic objectives without sparsity loss, supported by an automated code generation tool that outperforms existing solvers on typical problem scales.
This paper proposes a deterministic diffusion-based framework for controlling the probability density of nonlinear control-affine systems by leveraging a forward noise-excitation process and a reverse denoising feedback law to steer state distributions toward desired targets, with theoretical guarantees for drift-free and linear time-invariant dynamics.
This paper proposes a novel targeted exploration strategy for uncertain linear time-invariant systems with energy-bounded, non-stochastic disturbances, utilizing a semidefinite program to guarantee parameter estimation accuracy based on spectral content conditions that account for initial parametric uncertainty.
This paper proposes a novel method that leverages Large Language Models to evolve interpretable control policies represented as standard Python programs, offering a transparent and verifiable alternative to black-box neural network controllers for dynamical systems.
This paper presents a novel co-design approach that simultaneously optimizes the physical arrangement and control strategies of multiple rigidly connected quadcopters transporting a payload, utilizing a new H2-inspired robustness metric to maximize disturbance rejection capabilities, which is experimentally validated with diverse multi-UAV fleets and payload shapes.
This paper introduces two novel inverse optimal control frameworks utilizing "contractor and expander functions" to achieve stabilization for general control-affine nonlinear systems constrained to the positive orthant with positive controls, addressing the limitations of conventional methods through asymmetric cost designs and providing explicit universal formulae for applications such as predator-prey models.
This paper proposes a novel multi-product market mechanism for local energy markets that combines combinatorial clock exchange with machine learning to simplify preference elicitation for prosumers, enabling them to express complex, interdependent preferences through an intuitive package-reporting format while achieving rapid price convergence and transparent linear pricing.
This paper establishes the theoretical universality of analog quantum simulators under global control, introduces a direct quantum optimal control framework to bridge theory with experimental reality, and demonstrates the successful engineering of complex multi-body interactions and topological dynamics on a Rydberg-atom array.
This paper establishes a system-theoretic framework for dynamic Generalized Nash Equilibria by demonstrating the equivalence between strict dissipativity and the turnpike phenomenon, deriving conditions for optimal steady-state operation, and designing linear terminal penalties to ensure the convergence and stability of game-theoretic Model Predictive Control.
This paper proposes a predictive, privacy-preserving control scheme for low-voltage distribution systems that aggregates residential flexibility into real-time admissible power charts through offline multiparametric optimization, enabling efficient and cost-effective network management validated on a 43-bus test case.
This paper proposes a distributed formation control strategy for multi-agent systems that utilizes a rotation symmetry-based potential function to drive agents into a desired planar symmetric configuration with minimal connectivity ( edges) and extends the approach to enable coordinated maneuvering including translation, rotation, and scaling.
This paper proposes a rollout-based event-based control framework that utilizes sparsity-promoting regularization to balance control performance and actuation frequency, providing theoretical guarantees on stability and performance relative to periodic control.