Search Results for author: Deepan Muthirayan

Found 9 papers, 1 papers with code

Graph Learning for Cognitive Digital Twins in Manufacturing Systems

no code implementations17 Sep 2021 Trier Mortlock, Deepan Muthirayan, Shih-Yuan Yu, Pramod P. Khargonekar, Mohammad A. Al Faruque

In this paper, we detail the cognitive digital twin as the next stage of advancement of a digital twin that will help realize the vision of Industry 4. 0.

Graph Learning

Online Learning Robust Control of Nonlinear Dynamical Systems

no code implementations8 Jun 2021 Deepan Muthirayan, Pramod P. Khargonekar

We show that when the controller has preview of the cost functions and the disturbances for a short duration of time and the system is known $R^p_T(\gamma) = O(1)$ when $\gamma \geq \gamma_c$, where $\gamma_c = \mathcal{O}(\overline{\gamma})$.

Generative Adversarial Imitation Learning for Empathy-based AI

no code implementations27 May 2021 Pratyush Muthukumar, Karishma Muthukumar, Deepan Muthirayan, Pramod Khargonekar

Generative adversarial imitation learning (GAIL) is a model-free algorithm that has been shown to provide strong results in imitating complex behaviors in high-dimensional environments.

Imitation Learning Language Modelling +3

Dynamic Matching Markets in Power Grid: Concepts and Solution using Deep Reinforcement Learning

no code implementations12 Apr 2021 Majid Majidi, Deepan Muthirayan, Masood Parvania, Pramod P. Khargonekar

Traditional bulk load flexibility options, such as load shifting and load curtailment, for managing uncertainty in power markets limit the diversity of options and ignore the preferences of the individual loads, thus reducing efficiency and welfare.

Neuroscience-Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems

no code implementations23 Feb 2021 Arnav V. Malawade, Nathan D. Costa, Deepan Muthirayan, Pramod P. Khargonekar, Mohammad A. Al Faruque

If machine failures can be detected preemptively, then maintenance and repairs can be performed more efficiently, reducing production costs.

Anomaly Detection

Meta-Learning Guarantees for Online Receding Horizon Learning Control

no code implementations21 Oct 2020 Deepan Muthirayan, Pramod P. Khargonekar

In this paper we provide provable regret guarantees for an online meta-learning receding horizon control algorithm in an iterative control setting, where in each iteration the system to be controlled is a linear deterministic system that is different and unknown, the cost for the controller in an iteration is a general additive cost function and the control input is required to be constrained, which if violated incurs an additional cost.

Meta-Learning

Regret Guarantees for Online Receding Horizon Learning Control

no code implementations14 Oct 2020 Deepan Muthirayan, Jianjun Yuan, Pramod P. Khargonekar

In this paper we provide provable regret guarantees for an online learning receding horizon type control policy in a setting where the system to be controlled is an unknown linear dynamical system, the cost for the controller is a general additive function over a finite period $T$, and there exist control input constraints that when violated incur an additional cost.

Optimization and Control Systems and Control Systems and Control

Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions

3 code implementations31 Aug 2020 Shih-Yuan Yu, Arnav V. Malawade, Deepan Muthirayan, Pramod P. Khargonekar, Mohammad A. Al Faruque

Finally, we demonstrate that the use of spatial and temporal attention layers improves our model's performance by 2. 7% and 0. 7% respectively, and increases its explainability.

Autonomous Driving Scene Classification

A Meta-Learning Control Algorithm with Provable Finite-Time Guarantees

no code implementations30 Aug 2020 Deepan Muthirayan, Pramod Khargonekar

In this work we provide provable regret guarantees for an online meta-learning control algorithm in an iterative control setting, where in each iteration the system to be controlled is a linear deterministic system that is different and unknown, the cost for the controller in an iteration is a general additive cost function and the control input is required to be constrained, which if violated incurs an additional cost.

Meta-Learning

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