no code implementations • 10 Apr 2024 • Neelay Junnarkar, Murat Arcak, Peter Seiler

Finally, this convex condition is used in a projection-based training method to synthesize neural network controllers with dissipativity guarantees.

no code implementations • 4 Apr 2024 • Darioush Kevian, Usman Syed, Xingang Guo, Aaron Havens, Geir Dullerud, Peter Seiler, Lianhui Qin, Bin Hu

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.

no code implementations • 18 Feb 2024 • Darioush Keivan, Xingang Guo, Peter Seiler, Geir Dullerud, Bin Hu

Built upon such a policy optimization persepctive, our paper extends these subgradient-based search methods to a model-free setting.

no code implementations • 15 Dec 2023 • Alex Devonport, Peter Seiler, Murat Arcak

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.

1 code implementation • 11 Dec 2023 • Patrick J. W. Koelewijn, Rajiv Sing, Peter Seiler, Roland Tóth

In this paper, we consider the learning of a Reduced-Order Linear Parameter-Varying Model (ROLPVM) of a nonlinear dynamical system based on data.

no code implementations • 6 Jul 2023 • Talha Mushtaq, Peter Seiler, Maziar S. Hemati

Computing a stabilizing static output-feedback (SOF) controller is an NP-hard problem, in general.

no code implementations • 11 Feb 2023 • Peter Seiler, Raghu Venkataraman

This paper considers the robustness of an uncertain nonlinear system along a finite-horizon trajectory.

no code implementations • 29 Nov 2022 • Alex Devonport, Peter Seiler, Murat Arcak

Complex-valued Gaussian processes are used in Bayesian frequency-domain system identification as prior models for regression.

no code implementations • 11 Nov 2022 • Talha Mushtaq, Diganta Bhattacharjee, Peter Seiler, Maziar S. Hemati

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.

1 code implementation • 31 Mar 2022 • Neelay Junnarkar, He Yin, Fangda Gu, Murat Arcak, Peter Seiler

We propose a parameterization of a nonlinear dynamic controller based on the recurrent equilibrium network, a generalization of the recurrent neural network.

no code implementations • 3 Jan 2022 • Aaron Havens, Darioush Keivan, Peter Seiler, Geir Dullerud, Bin Hu

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.

no code implementations • 14 Dec 2021 • Lanlan Su, Peter Seiler, Joaquin Carrasco, Sei Zhen Khong

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.

no code implementations • 30 Nov 2021 • Darioush Keivan, Aaron Havens, Peter Seiler, Geir Dullerud, Bin Hu

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.

1 code implementation • 25 Nov 2021 • Bernardo Aquino, Arash Rahnama, Peter Seiler, Lizhen Lin, Vijay Gupta

Adversarial examples can easily degrade the classification performance in neural networks.

1 code implementation • 8 Sep 2021 • Fangda Gu, He Yin, Laurent El Ghaoui, Murat Arcak, Peter Seiler, Ming Jin

Neural network controllers have become popular in control tasks thanks to their flexibility and expressivity.

no code implementations • 9 Mar 2021 • Aniketh Kalur, Talha Mushtaq, Peter Seiler, Maziar S. Hemati

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

1 code implementation • 16 Dec 2020 • He Yin, Peter Seiler, Ming Jin, Murat Arcak

A method is presented to learn neural network (NN) controllers with stability and safety guarantees through imitation learning (IL).

1 code implementation • 2 Jun 2020 • Jyot Buch, Peter Seiler

We present a robust synthesis algorithm for uncertain linear time-varying (LTV) systems on finite horizons.

1 code implementation • L4DC 2020 • Harish Venkataraman, Derya Aksaray, Peter Seiler

Learning to satisfy STL specifications often needs a sufficient length of state history to compute reward and the next action.

no code implementations • 3 Nov 2017 • Bin Hu, Peter Seiler, Laurent Lessard

We present a convergence rate analysis for biased stochastic gradient descent (SGD), where individual gradient updates are corrupted by computation errors.

no code implementations • 25 Jun 2017 • Bin Hu, Peter Seiler, Anders Rantzer

Our proposed model recovers SAGA, SDCA, Finito, and SAG as special cases.

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