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Found 22 papers, 8 papers with code
Fast Assignment in Asset-Guarding Engagements using Function Approximation
This subproblem is computationally intensive and hence the computational cost of the assignment is dominated by the construction of the cost matrix.
Synthesizing Neural Network Controllers with Closed-Loop Dissipativity Guarantees
Finally, this convex condition is used in a projection-based training method to synthesize neural network controllers with dissipativity guarantees.
Capabilities of Large Language Models in Control Engineering: A Benchmark Study on GPT-4, Claude 3 Opus, and Gemini 1.0 Ultra
In this paper, we explore the capabilities of state-of-the-art large language models (LLMs) such as GPT-4, Claude 3 Opus, and Gemini 1. 0 Ultra in solving undergraduate-level control problems.
Model-Free $μ$-Synthesis: A Nonsmooth Optimization Perspective
Built upon such a policy optimization persepctive, our paper extends these subgradient-based search methods to a model-free setting.
Frequency-domain Gaussian Process Models for $H_\infty$ Uncertainties
We then establish how an $H_\infty$ Gaussian process can serve as a prior for Bayesian system identification and as a probabilistic uncertainty model for probabilistic robust control.
Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear Systems
In this paper, we consider the learning of a Reduced-Order Linear Parameter-Varying Model (ROLPVM) of a nonlinear dynamical system based on data.
On the convexity of static output feedback control synthesis for systems with lossless nonlinearities
Computing a stabilizing static output-feedback (SOF) controller is an NP-hard problem, in general.
Trajectory-based Robustness Analysis for Nonlinear Systems
This paper considers the robustness of an uncertain nonlinear system along a finite-horizon trajectory.
Frequency Domain Gaussian Process Models for $H^\infty$ Uncertainties
Complex-valued Gaussian processes are used in Bayesian frequency-domain system identification as prior models for regression.
Structured Singular Value of a Repeated Complex Full-Block Uncertainty
Existing algorithms compute upper and lower bounds on the SSV for structured uncertainties that contain repeated (real or complex) scalars and/or non-repeated complex full blocks.
Synthesis of Stabilizing Recurrent Equilibrium Network Controllers
We propose a parameterization of a nonlinear dynamic controller based on the recurrent equilibrium network, a generalization of the recurrent neural network.
Revisiting PGD Attacks for Stability Analysis of Large-Scale Nonlinear Systems and Perception-Based Control
We show that the ROA analysis can be approximated as a constrained maximization problem whose goal is to find the worst-case initial condition which shifts the terminal state the most.
On the Necessity and Sufficiency of Discrete-Time O'Shea-Zames-Falb Multipliers
Roughly speaking, a successful proof of the conjecture would require: (a) a conic parameterization of a set of multipliers that describes exactly the set of nonlinearities, (b) a lossless S-procedure to show that the non-existence of a multiplier implies that the Lurye system is not uniformly robustly stable over the set of nonlinearities, and (c) the existence of a multiplier in the set of multipliers used in (a) implies the existence of an LTI multiplier.
Model-Free $μ$ Synthesis via Adversarial Reinforcement Learning
We build a connection between robust adversarial RL and $\mu$ synthesis, and develop a model-free version of the well-known $DK$-iteration for solving state-feedback $\mu$ synthesis with static $D$-scaling.
Robustness against Adversarial Attacks in Neural Networks using Incremental Dissipativity
Adversarial examples can easily degrade the classification performance in neural networks.
Recurrent Neural Network Controllers Synthesis with Stability Guarantees for Partially Observed Systems
Neural network controllers have become popular in control tasks thanks to their flexibility and expressivity.
Estimating Regions of Attraction for Transitional Flows using Quadratic Constraints
This approach reduces conservatism and improves estimates for regions of attraction and bounds on permissible perturbation amplitudes over related methods that employ quadratic constraints on spherical sets.
Fluid Dynamics
Imitation Learning with Stability and Safety Guarantees
A method is presented to learn neural network (NN) controllers with stability and safety guarantees through imitation learning (IL).
Finite Horizon Robust Synthesis Using Integral Quadratic Constraints
We present a robust synthesis algorithm for uncertain linear time-varying (LTV) systems on finite horizons.
Tractable Reinforcement Learning of Signal Temporal Logic Objectives
Learning to satisfy STL specifications often needs a sufficient length of state history to compute reward and the next action.
Analysis of Biased Stochastic Gradient Descent Using Sequential Semidefinite Programs
We present a convergence rate analysis for biased stochastic gradient descent (SGD), where individual gradient updates are corrupted by computation errors.
A Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints
Our proposed model recovers SAGA, SDCA, Finito, and SAG as special cases.
