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Online Learning Robust Control of Nonlinear Dynamical Systems [article]

Deepan Muthirayan, Pramod P. Khargonekar
2021 arXiv   pre-print
In this work we address the problem of the online robust control of nonlinear dynamical systems perturbed by disturbance. We study the problem of attenuation of the total cost over a duration T in response to the disturbances. We consider the setting where the cost function (at a particular time) is a general continuous function and adversarial, the disturbance is adversarial and bounded at any point of time. Our goal is to design a controller that can learn and adapt to achieve a certain level
more » ... of attenuation. We analyse two cases (i) when the system is known and (ii) when the system is unknown. We measure the performance of the controller by the deviation of the controller's cost for a sequence of cost functions with respect to an attenuation γ, R^p_t. We propose an online controller and present guarantees for the metric R^p_t when the maximum possible attenuation is given by γ, which is a system constant. 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(γ) = O(1) when γ≥γ_c, where γ_c = 𝒪(γ). We then show that when the system is unknown the proposed controller with a preview of the cost functions and the disturbances for a short horizon achieves R^p_T(γ) = 𝒪(N) + 𝒪(1) + 𝒪((T-N)g(N)), when γ≥γ_c, where g(N) is the accuracy of a given nonlinear estimator and N is the duration of the initial estimation period. We also characterize the lower bound on the required prediction horizon for these guarantees to hold in terms of the system constants.
arXiv:2106.04092v1 fatcat:eswglw6apvbvvdf3zswoden7bq

Online Algorithms for Network Robustness under Connectivity Constraints [article]

Deepan Muthirayan, Pramod P. Khargonekar
2021 arXiv   pre-print
Muthirayan and P. P.  ... 
arXiv:2106.04037v1 fatcat:qyfwdtvngzbs3dmg6vpdo3skma

Cognitive Preadaptation for Resilient Adaptive Control [article]

Deepan Muthirayan, Pramod P. Khargonekar
2020 arXiv   pre-print
In this paper, we investigate a novel control architecture and algorithm for incorporating preadaption functions. We propose a preadaptation mechanism that can augment any adaptive control scheme and improve its resilience. Through simulations of a flight control system we illustrate the effectiveness of the preadaptation mechanism in improving the adaptation. We show that the preadaptation mechanism we propose can reduce the peak of the response by as much as 50%. The scenarios we present also
more » ... show that the preadaptation mechanism is effective across a wide range of scenarios suggesting that the mechanism is reliable.
arXiv:2010.13205v2 fatcat:d4elcixkzbhthdsoiwibp6rtsa

Generative Adversarial Imitation Learning for Empathy-based AI [article]

Pratyush Muthukumar, Karishma Muthukumar, Deepan Muthirayan, Pramod Khargonekar
2021 arXiv   pre-print
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. In this paper, we utilize the GAIL model for text generation to develop empathy-based context-aware conversational AI. Our model uses an expert trajectory of empathetic prompt-response dialogues which can accurately exhibit the correct empathetic emotion when generating a response. The Generator of the GAIL
more » ... uses the GPT-2 sequential pre-trained language model trained on 117 million parameters from 40 GB of internet data. We propose a novel application of an approach used in transfer learning to fine tune the GPT-2 model in order to generate concise, user-specific empathetic responses validated against the Discriminator. Our novel GAIL model utilizes a sentiment analysis history-based reinforcement learning approach to empathetically respond to human interactions in a personalized manner. We find that our model's response scores on various human-generated prompts collected from the Facebook Empathetic Dialogues dataset outperform baseline counterparts. Moreover, our model improves upon various history-based conversational AI models developed recently, as our model's performance over a sustained conversation of 3 or more interactions outperform similar conversational AI models.
arXiv:2105.13328v1 fatcat:agbxrbl4rbhrhbz4zsgkll3w2q

Graph Learning for Cognitive Digital Twins in Manufacturing Systems [article]

Trier Mortlock, Deepan Muthirayan, Shih-Yuan Yu, Pramod P. Khargonekar, Mohammad A. Al Faruque
2021 arXiv   pre-print
Future manufacturing requires complex systems that connect simulation platforms and virtualization with physical data from industrial processes. Digital twins incorporate a physical twin, a digital twin, and the connection between the two. Benefits of using digital twins, especially in manufacturing, are abundant as they can increase efficiency across an entire manufacturing life-cycle. The digital twin concept has become increasingly sophisticated and capable over time, enabled by rises in
more » ... technologies. 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. Cognitive digital twins will allow enterprises to creatively, effectively, and efficiently exploit implicit knowledge drawn from the experience of existing manufacturing systems. They also enable more autonomous decisions and control, while improving the performance across the enterprise (at scale). This paper presents graph learning as one potential pathway towards enabling cognitive functionalities in manufacturing digital twins. A novel approach to realize cognitive digital twins in the product design stage of manufacturing that utilizes graph learning is presented.
arXiv:2109.08632v1 fatcat:63kqrdg2hzakpj3jplptuayyjy

Mechanism Design for Demand Response Programs [article]

Deepan Muthirayan, Dileep Kalathil, Kameshwar Poolla, Pravin Varaiya
2019 arXiv   pre-print
Recall that we keep forming pods until M −1 Deepan Muthirayan is with the School of Electrical Engineering and Computer Science, UC Irvine.  ... 
arXiv:1712.07742v3 fatcat:d62n4kwthzaxzcey653e2tynbq

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

Shih-Yuan Yu, Arnav V. Malawade, Deepan Muthirayan, Pramod P. Khargonekar, Mohammad A. Al Faruque
2020 arXiv   pre-print
Despite impressive advancements in Autonomous Driving Systems (ADS), navigation in complex road conditions remains a challenging problem. There is considerable evidence that evaluating the subjective risk level of various decisions can improve ADS' safety in both normal and complex driving scenarios. However, existing deep learning-based methods often fail to model the relationships between traffic participants and can suffer when faced with complex real-world scenarios. Besides, these methods
more » ... ack transferability and explainability. To address these limitations, we propose a novel data-driven approach that uses scene-graphs as intermediate representations. Our approach includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory Network, and attention layers for modeling the subjective risk of driving maneuvers. To train our model, we formulate this task as a supervised scene classification problem. We consider a typical use case to demonstrate our model's capabilities: lane changes. We show that our approach achieves a higher classification accuracy than the state-of-the-art approach on both large (96.4% vs. 91.2%) and small (91.8% vs. 71.2%) synthesized datasets, also illustrating that our approach can learn effectively even from smaller datasets. We also show that our model trained on a synthesized dataset achieves an average accuracy of 87.8% when tested on a real-world dataset compared to the 70.3% accuracy achieved by the state-of-the-art model trained on the same synthesized dataset, showing that our approach can more effectively transfer knowledge. 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.
arXiv:2009.06435v1 fatcat:5tktswbqzzfh7g5wbunq73e5yi

Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction [article]

Arnav V. Malawade, Shih-Yuan Yu, Brandon Hsu, Deepan Muthirayan, Pramod P. Khargonekar, Mohammad A. Al Faruque
2021 arXiv   pre-print
In autonomous vehicles (AVs), early warning systems rely on collision prediction to ensure occupant safety. However, state-of-the-art methods using deep convolutional networks either fail at modeling collisions or are too expensive/slow, making them less suitable for deployment on AV edge hardware. To address these limitations, we propose sg2vec, a spatio-temporal scene-graph embedding methodology that uses Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) layers to predict future
more » ... lisions via visual scene perception. We demonstrate that sg2vec predicts collisions 8.11% more accurately and 39.07% earlier than the state-of-the-art method on synthesized datasets, and 29.47% more accurately on a challenging real-world collision dataset. We also show that sg2vec is better than the state-of-the-art at transferring knowledge from synthetic datasets to real-world driving datasets. Finally, we demonstrate that sg2vec performs inference 9.3x faster with an 88.0% smaller model, 32.4% less power, and 92.8% less energy than the state-of-the-art method on the industry-standard Nvidia DRIVE PX 2 platform, making it more suitable for implementation on the edge.
arXiv:2111.06123v1 fatcat:rn3qazdffjhftouszhfnlhc7w4

Adaptive Gradient Online Control [article]

Deepan Muthirayan, Jianjun Yuan, Pramod P. Khargonekar
2021 arXiv   pre-print
Muthirayan and P. P.  ... 
arXiv:2103.08753v5 fatcat:tpoqr5ymvba43jdect54ntm5gi

Selling Demand Response Using Options [article]

Deepan Muthirayan, Dileep Kalathil, Sen Li, Kameshwar Poolla, Pravin Varaiya
2020 arXiv   pre-print
Wholesale electricity markets in many jurisdictions use a two-settlement structure: a day-ahead market for bulk power transactions and a real-time market for fine-grain supply-demand balancing. This paper explores trading demand response assets within this two-settlement market structure. We consider two approaches for trading demand response assets: (a) an intermediate spot market with contingent pricing, and (b) an over-the-counter options contract. In the first case, we characterize the
more » ... titive equilibrium of the spot market, and show that it is socially optimal. Economic orthodoxy advocates spot markets, but these require expensive infrastructure and regulatory blessing. In the second case, we characterize competitive equilibria and compare its efficiency with the idealized spot market. Options contract are private bilateral over-the-counter transactions and do not require regulatory approval. We show that the optimal social welfare is, in general, not supported. We then design optimal option prices that minimize the social welfare gap. This optimal design serves to approximate the ideal spot market for demand response using options with modest loss of efficiency. Our results are validated through numerical simulations.
arXiv:1906.01069v5 fatcat:r3gy4q5mynf6jkekeuppmiyflm

Online Learning for Predictive Control with Provable Regret Guarantees [article]

Deepan Muthirayan, Jianjun Yuan, Dileep Kalathil, Pramod P. Khargonekar
2022 arXiv   pre-print
We study the problem of online learning in predictive control of an unknown linear dynamical system with time varying cost functions which are unknown apriori. Specifically, we study the online learning problem where the control algorithm does not know the true system model and has only access to a fixed-length (that does not grow with the control horizon) preview of the future cost functions. The goal of the online algorithm is to minimize the dynamic regret, defined as the difference between
more » ... he cumulative cost incurred by the algorithm and that of the best sequence of actions in hindsight. We propose two different online Model Predictive Control (MPC) algorithms to address this problem, namely Certainty Equivalence MPC (CE-MPC) algorithm and Optimistic MPC (O-MPC) algorithm. We show that under the standard stability assumption for the model estimate, the CE-MPC algorithm achieves 𝒪(T^2/3) dynamic regret. We then extend this result to the setting where the stability assumption holds only for the true system model by proposing the O-MPC algorithm. We show that the O-MPC algorithm also achieves 𝒪(T^2/3) dynamic regret, at the cost of some additional computation. We also present numerical studies to demonstrate the performance of our algorithm.
arXiv:2111.15041v2 fatcat:he43ygi3bjegzj2we7si2btdg4

A Minimal Incentive-based Demand Response Program With Self Reported Baseline Mechanism [article]

Deepan Muthirayan, Enrique Baeyens, Pratyush Chakraborty, Kameshwar Poolla, Pramod P. Khargonekar
2019 arXiv   pre-print
In this paper, we propose a novel incentive based Demand Response (DR) program with a self reported baseline mechanism. The System Operator (SO) managing the DR program recruits consumers or aggregators of DR resources. The recruited consumers are required to only report their baseline, which is the minimal information necessary for any DR program. During a DR event, a set of consumers, from this pool of recruited consumers, are randomly selected. The consumers are selected such that the
more » ... d load reduction is delivered. The selected consumers, who reduce their load, are rewarded for their services and other recruited consumers, who deviate from their reported baseline, are penalized. The randomization in selection and penalty ensure that the baseline inflation is controlled. We also justify that the selection probability can be simultaneously used to control SO's cost. This allows the SO to design the mechanism such that its cost is almost optimal when there are no recruitment costs or at least significantly reduced otherwise. Finally, we also show that the proposed method of self-reported baseline outperforms other baseline estimation methods commonly used in practice.
arXiv:1901.02923v3 fatcat:bnmaylwsjnavpgocdvm6iffuee

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

Majid Majidi, Deepan Muthirayan, Masood Parvania, Pramod P. Khargonekar
2021 arXiv   pre-print
Muthirayan and P. P.  ...  Muthirayan, M. Parvania, and P. P.  ... 
arXiv:2104.05654v4 fatcat:rsggsbuqmje6dl5usgyd26hewe

A Meta-Learning Control Algorithm with Provable Finite-Time Guarantees [article]

Deepan Muthirayan, Pramod Khargonekar
2022 arXiv   pre-print
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. We prove (i) that the algorithm achieves a regret for the controller cost
more » ... d constraint violation that are O(T^3/4) for an episode of duration T with respect to the best policy that satisfies the control input control constraints and (ii) that the average of the regret for the controller cost and constraint violation with respect to the same policy vary as O((1+log(N)/N)T^3/4) with the number of iterations N, showing that the worst regret for the learning within an iteration continuously improves with experience of more iterations.
arXiv:2008.13265v6 fatcat:3tfzkfqrcnhtfnoapjtzc2yd34

Improved Attention Models for Memory Augmented Neural Network Adaptive Controllers [article]

Deepan Muthirayan, Scott Nivison, Pramod P. Khargonekar
2020 arXiv   pre-print
Deepan Attention is the state of focused awareness on some aspects of the environment.  ...  Muthirayan and Pramod P. Khargonekar are with the Department of Electrical Engineering and Computer Sciences, University of California, Irvine, CA 92697.  ... 
arXiv:1910.01189v7 fatcat:6xjsmsxbbjfgbmygkebccozu6y
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