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Karkulali Pugalenthi - IEEE Xplore Author Profile

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Author details

Karkulali Pugalenthi

Publications

8

Citations

31

Publications by Year

20182024

Co-Authors:

Matthew C. H. ChuaShaista HussainHaiming LuoShuyi PanHyunseok Park

Show All Co-Authors (8)

Karkulali Pugalenthi

Affiliation

Singapore Institute of Manufacturing Technology

Agency for Science

Technology and Research, Singapore, Republic of Singapore

Publication Topics

Neural Network,
Remaining Useful Life,
Artificial Neural Network,
Mean Square Error,
Model Parameters,
Particle Filter,
Battery Capacity,
Data-driven Methods,
Degradation Trend,
Error Values,
Estimation Of Lithium-ion Batteries,
Feed-forward Network

Biography

Karkulali Pugalenthi received the master’s degree in electrical engineering from the National University of Singapore (NUS), in 2012. She is currently pursuing the Ph.D. degree with the Nano-Macro Reliability Laboratory, Singapore University of Technology and Design (SUTD), Singapore. She was a Research Associate with the Energy Research Institute, Nanyang Technological University (NTU), working on analytics and anomaly detection for commercial gas pipelines. She is also a Senior Research Associate with the Nano-Macro Reliability Laboratory, SUTD. She works on the design and development of particle filter-based framework for prognostics of electronic devices, also exploring hybrid approaches of combining neural networks and particle filters...

Publications

8

Citations

31

Publications by Year

20182024

Co-Authors:

Matthew C. H. Chua
Shaista Hussain
Haiming Luo
Shuyi Pan
Hyunseok Park

Show All Co-Authors (8)

Author's Published Works

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Showing 1-25 of 240 results

Conferences (190)

Journals (48)

Early Access Articles (1)

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M. Sznaier(139)
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Department of Electrical Engineering, Pennsylvania State University, University Park, PA, USA(78)
Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA(25)
ECE Department, Northeastern University, Boston, MA(13)
ECE Department, Northeastern University, Boston, MA, USA(10)
Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USA(10)
IEEE Transactions on Automatic Control(31)
IEEE Control Systems Letters(9)
2015 54th IEEE Conference on Decision and Control (CDC)(5)
Proceedings of 1994 33rd IEEE Conference on Decision and Control(5)
1993 American Control Conference(4)
IEEE(240)
San Diego, CA, USA(10)
Boston, MA, USA(8)
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Optimization Problem(69)
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Linear Matrix Inequalities(48)
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Robust Data-Driven Control of Discrete-Time Linear Systems With Errors in Variables

Jared Miller;Tianyu Dai;Mario Sznaier

IEEE Transactions on Automatic Control

Year: 2025 | Volume: 70, Issue: 2 | Journal Article |

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This article presents a sum of squares (SOS)-based framework to perform data-driven stabilization and robust control tasks on discrete-time linear systems where the full-state observations are corrupted by

$\ell ^\infty$ bounded input, measurement, and process noise (error in variable setting). Certificates of full-state-feedback robust performance, superstabilization or quadratic stabilization of...Show More

Robust Data-Driven Control of Discrete-Time Linear Systems With Errors in Variables

Jared Miller;Tianyu Dai;Mario Sznaier

IEEE Transactions on Automatic Control

Year: 2025 | Volume: 70, Issue: 2 | Journal Article |

Convex Computation of Value-at-Risk Bounds for Stochastic Processes

Jared Miller;Matteo Tacchi;Mario Sznaier;Ashkan Jasour

IEEE Transactions on Automatic Control

Year: 2024 | Early Access Article |

This paper formulates algorithms to upper-bound the maximum Value-at-Risk (VaR) of a state function along trajectories of stochastic processes. The VaR is upper bounded by two methods: minimax tail-bounds (Cantelli/Vysochanskij-Petunin) and Expected Shortfall/Conditional Value-at-Risk (ES). Tail-bounds lead to an infinite-dimensional Second Order Cone Program (SOCP) in occupation measures, while t...Show More

Convex Computation of Value-at-Risk Bounds for Stochastic Processes

Jared Miller;Matteo Tacchi;Mario Sznaier;Ashkan Jasour

IEEE Transactions on Automatic Control

Year: 2024 | Early Access Article |

Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers*

Jinyang Liu;Wondmgezahu Teshome;Sandesh Ghimire;Mario Sznaier;Octavia Camps

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

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Solving image and video jigsaw puzzles poses the chal-lenging task of rearranging image fragments or video frames from unordered sequences to restore meaningful images and video sequences. Existing approaches often hinge on discriminative models tasked with predicting either the absolute positions of puzzle elements or the permutation actions applied to the original data. Unfortunately, these meth...Show More

Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers*

Jinyang Liu;Wondmgezahu Teshome;Sandesh Ghimire;Mario Sznaier;Octavia Camps

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Data-Driven Superstabilization of Linear Systems Under Quantization

Jared Miller;Jian Zheng;Mario Sznaier;Chris Hixenbaugh

2024 American Control Conference (ACC)

Year: 2024 | Conference Paper |

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This paper focuses on the stabilization and regulation of linear systems affected by quantization in state-transition data and in actuated input. The observed data are composed of tuples of current state, input, and the next state's interval ranges based on sensor quantization. Using an established characterization of input-logarithmically-quantized stabilization based on robustness to sector-boun...Show More

Data-Driven Superstabilization of Linear Systems Under Quantization

Jared Miller;Jian Zheng;Mario Sznaier;Chris Hixenbaugh

2024 American Control Conference (ACC)

Year: 2024 | Conference Paper |

Data-Driven Safe Control of Discrete-Time Non-Linear Systems

Jian Zheng;Jared Miller;Mario Sznaier

IEEE Control Systems Letters

Year: 2024 | Volume: 8 | Journal Article |

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This letter proposes a framework to perform verifiably safe control of all discrete-time non-linear systems that are compatible with collected data. Most safety-maintaining control synthesis algorithms (e.g., control barrier functions, density functions) are limited to obtaining theoretical guarantees of safety in continuous-time, even while their implementation on real systems is typically in dis...Show More

Data-Driven Safe Control of Discrete-Time Non-Linear Systems

Jian Zheng;Jared Miller;Mario Sznaier

IEEE Control Systems Letters

Year: 2024 | Volume: 8 | Journal Article |

Data-Driven Superstabilizing Control Under Quadratically-Bounded Errors-in-Variables Noise

Jared Miller;Tianyu Dai;Mario Sznaier

IEEE Control Systems Letters

Year: 2024 | Volume: 8 | Journal Article |

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The Errors-in-Variables model of system identification/control involves nontrivial input and measurement corruption of observed data, resulting in generically nonconvex optimization problems. This letter performs full-state-feedback stabilizing control of all discrete-time linear systems that are consistent with observed data for which the input and measurement noise obey quadratic bounds. Instanc...Show More

Data-Driven Superstabilizing Control Under Quadratically-Bounded Errors-in-Variables Noise

Jared Miller;Tianyu Dai;Mario Sznaier

IEEE Control Systems Letters

Year: 2024 | Volume: 8 | Journal Article |

Tractable Approximations of LMI Robust Feasibility Sets

Teodoro Alamo;Martina Mammarella;Fabrizio Dabbene;Mario Sznaier

IEEE Control Systems Letters

Year: 2024 | Volume: 8 | Journal Article |

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In this letter, we introduce novel tractable approximations for robust Linear Matrix Inequality (LMI) problems. We present various Quadratic Matrix Inequalities (QMIs) that enable us to characterize the effect of ellipsoidal uncertainty in the robust problem. These formulations are expressed in terms of a set of auxiliary decision variables, which facilitate the derivation of a generalized S-proce...Show More

Tractable Approximations of LMI Robust Feasibility Sets

Teodoro Alamo;Martina Mammarella;Fabrizio Dabbene;Mario Sznaier

IEEE Control Systems Letters

Year: 2024 | Volume: 8 | Journal Article |

Efficient Identification of Error-in-Variables Switched Systems Using a Riemannian Embedding

Mario Sznaier;Xikang Zhang;Octavia Camps

IEEE Transactions on Automatic Control

Year: 2024 | Volume: 69, Issue: 2 | Journal Article |

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This article considers the problem of error in variables identification for switched affine models. Since it is well known that this problem is generically NP-hard, several relaxations have been proposed in the literature. However, while these approaches work well for low-dimensional systems with few subsystems, they scale poorly with both the number of subsystems and their memory. To address this...Show More

Efficient Identification of Error-in-Variables Switched Systems Using a Riemannian Embedding

Mario Sznaier;Xikang Zhang;Octavia Camps

IEEE Transactions on Automatic Control

Year: 2024 | Volume: 69, Issue: 2 | Journal Article |

Peak Value-at-Risk Estimation for Stochastic Differential Equations Using Occupation Measures

Jared Miller;Matteo Tacchi;Mario Sznaier;Ashkan Jasour

2023 62nd IEEE Conference on Decision and Control (CDC)

Year: 2023 | Conference Paper |

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This paper proposes an algorithm to upper-bound maximal quantile statistics of a state function over the course of a Stochastic Differential Equation (SDE) system execution. This chance-peak problem is posed as a nonconvex program aiming to maximize the Value-at-Risk (VaR) of a state function along SDE state distributions. The VaR problem is upper-bounded by an infinite-dimensional Second-Order Co...Show More

Peak Value-at-Risk Estimation for Stochastic Differential Equations Using Occupation Measures

Jared Miller;Matteo Tacchi;Mario Sznaier;Ashkan Jasour

2023 62nd IEEE Conference on Decision and Control (CDC)

Year: 2023 | Conference Paper |

Peak Estimation of Time Delay Systems Using Occupation Measures

Jared Miller;Milan Korda;Victor Magron;Mario Sznaier

2023 62nd IEEE Conference on Decision and Control (CDC)

Year: 2023 | Conference Paper |

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This work proposes a method to compute the maximum value obtained by a state function along trajectories of a Delay Differential Equation (DDE). An example of this task is finding the maximum number of infected people in an epidemic model with a nonzero incubation period. The variables of this peak estimation problem include the stopping time and the original history (restricted to a class of admi...Show More

Peak Estimation of Time Delay Systems Using Occupation Measures

Jared Miller;Milan Korda;Victor Magron;Mario Sznaier

2023 62nd IEEE Conference on Decision and Control (CDC)

Year: 2023 | Conference Paper |

Data-Driven Control of Positive Linear Systems using Linear Programming

Jared Miller;Tianyu Dai;Mario Sznaier;Bahram Shafai

2023 62nd IEEE Conference on Decision and Control (CDC)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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This paper presents a linear-programming based algorithm to perform data-driven stabilizing control of linear positive systems. A set of state-input-transition observations is collected up to magnitude-bounded noise. A state feedback controller and dual linear copositive Lyapunov function are created such that the set of all data-consistent plants is contained within the set of all stabilized syst...Show More

Data-Driven Control of Positive Linear Systems using Linear Programming

Jared Miller;Tianyu Dai;Mario Sznaier;Bahram Shafai

2023 62nd IEEE Conference on Decision and Control (CDC)

Year: 2023 | Conference Paper |

On the Hardness of Learning to Stabilize Linear Systems

Xiong Zeng;Zexiang Liu;Zhe Du;Necmiye Ozay;Mario Sznaier

2023 62nd IEEE Conference on Decision and Control (CDC)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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Inspired by the work of Tsiamis et al. [1], in this paper we study the statistical hardness of learning to stabilize linear time-invariant systems. Hardness is measured by the number of samples required to achieve a learning task with a given probability. The work in [1] shows that there exist system classes that are hard to learn to stabilize with the core reason being the hardness of identificat...Show More

On the Hardness of Learning to Stabilize Linear Systems

Xiong Zeng;Zexiang Liu;Zhe Du;Necmiye Ozay;Mario Sznaier

2023 62nd IEEE Conference on Decision and Control (CDC)

Year: 2023 | Conference Paper |

Robust Data-Driven Safe Control Using Density Functions

Jian Zheng;Tianyu Dai;Jared Miller;Mario Sznaier

IEEE Control Systems Letters

Year: 2023 | Volume: 7 | Journal Article |

Cited by: Papers (2)

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This letter presents a tractable framework for data-driven synthesis of robustly safe control laws. Given noisy experimental data and some priors about the structure of the system, the goal is to synthesize a state feedback law such that the trajectories of the closed loop system are guaranteed to avoid an unsafe set even in the presence of unknown but bounded disturbances (process noise). The mai...Show More

Robust Data-Driven Safe Control Using Density Functions

Jian Zheng;Tianyu Dai;Jared Miller;Mario Sznaier

IEEE Control Systems Letters

Year: 2023 | Volume: 7 | Journal Article |

Certified Control Oriented Learning: A Robust Predictor-Based Approach

Rajiv Singh;Mario Sznaier

IEEE Control Systems Letters

Year: 2023 | Volume: 7 | Journal Article |

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We present a novel approach to the problem of learning the behavior of dynamical systems for the purpose of robust control design. The approach is centered around the derivation of stable predictors of potentially unstable systems and using them to identify plant models that can be ranked by their complexity (order) vs. empirical Nu -gap value.Show More

Certified Control Oriented Learning: A Robust Predictor-Based Approach

Rajiv Singh;Mario Sznaier

IEEE Control Systems Letters

Year: 2023 | Volume: 7 | Journal Article |

Bounding the Distance to Unsafe Sets With Convex Optimization

Jared Miller;Mario Sznaier

IEEE Transactions on Automatic Control

Year: 2023 | Volume: 68, Issue: 12 | Journal Article |

Cited by: Papers (7)

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This work proposes an algorithm to bind the minimum distance between points on trajectories of a dynamical system and points on an unsafe set. Prior work on certifying safety of trajectories includes barrier and density methods, which do not provide a margin of proximity to the unsafe set in terms of distance. The distance estimation problem is relaxed to a Monge–Kantorovich-type optimal transport...Show More

Bounding the Distance to Unsafe Sets With Convex Optimization

Jared Miller;Mario Sznaier

IEEE Transactions on Automatic Control

Year: 2023 | Volume: 68, Issue: 12 | Journal Article |

Data-Driven Gain Scheduling Control of Linear Parameter-Varying Systems Using Quadratic Matrix Inequalities

Jared Miller;Mario Sznaier

IEEE Control Systems Letters

Year: 2023 | Volume: 7 | Journal Article |

Cited by: Papers (18)

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This letter synthesizes a gain-scheduled controller to stabilize all possible Linear Parameter-Varying (LPV) plants that are consistent with measured input/state data records. Inspired by prior work in data informativity and LTI stabilization, a set of Quadratic Matrix Inequalities is developed to represent the noise set, the class of consistent LPV plants, and the class of stabilizable plants. Th...Show More

Data-Driven Gain Scheduling Control of Linear Parameter-Varying Systems Using Quadratic Matrix Inequalities

Jared Miller;Mario Sznaier

IEEE Control Systems Letters

Year: 2023 | Volume: 7 | Journal Article |

Bounding the Distance of Closest Approach to Unsafe Sets with Occupation Measures

Jared Miller;Mario Sznaier

2022 IEEE 61st Conference on Decision and Control (CDC)

Year: 2022 | Conference Paper |

Cited by: Papers (2)

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This paper presents a method to lower-bound the distance of closest approach between points on an unsafe set and points along system trajectories. Such a minimal distance is a quantifiable and interpretable certificate of safety of trajectories, as compared to prior art in barrier and density methods which offers a binary indication of safety/unsafety. The distance estimation problem is converted ...Show More

Bounding the Distance of Closest Approach to Unsafe Sets with Occupation Measures

Jared Miller;Mario Sznaier

2022 IEEE 61st Conference on Decision and Control (CDC)

Year: 2022 | Conference Paper |

Certified Control-Oriented Learning: A Coprime Factorization Approach

Rajiv Singh;Mario Sznaier

2022 IEEE 61st Conference on Decision and Control (CDC)

Year: 2022 | Conference Paper |

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This paper considers the problem of learning models to be used for controller design. Using a simple example, it argues that in this scenario the objective should reflect the closed-loop, rather than open-loop distance between the learned model and the actual plant, a task that can be accomplished by using a gap metric motivated approach. This is particularly important when identifying open-loop u...Show More

Certified Control-Oriented Learning: A Coprime Factorization Approach

Rajiv Singh;Mario Sznaier

2022 IEEE 61st Conference on Decision and Control (CDC)

Year: 2022 | Conference Paper |

Data-Driven Superstabilizing Control of Error-in-Variables Discrete-Time Linear Systems

Jared Miller;Tianyu Dai;Mario Sznaier

2022 IEEE 61st Conference on Decision and Control (CDC)

Year: 2022 | Conference Paper |

Cited by: Papers (6)

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This paper proposes a method to find super-stabilizing controllers for discrete-time linear systems that are consistent with a set of corrupted observations. The L-infinity bounded measurement noise introduces a bilinearity between the unknown plant parameters and noise terms. A super-stabilizing controller may be found by solving a feasibility problem involving a set of polynomial nonnegativity c...Show More

Data-Driven Superstabilizing Control of Error-in-Variables Discrete-Time Linear Systems

Jared Miller;Tianyu Dai;Mario Sznaier

2022 IEEE 61st Conference on Decision and Control (CDC)

Year: 2022 | Conference Paper |

Peak Estimation for Uncertain and Switched Systems

Jared Miller;Didier Henrion;Mario Sznaier;Milan Korda

2021 60th IEEE Conference on Decision and Control (CDC)

Year: 2021 | Conference Paper |

Cited by: Papers (6)

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Peak estimation bounds extreme values of a function of state along trajectories of a dynamical system. This paper focuses on extending peak estimation to continuous and discrete settings with time-independent and time-dependent uncertainty. Techniques from optimal control are used to incorporate uncertainty into an existing occupation measure-based peak estimation framework, which includes special...Show More

Peak Estimation for Uncertain and Switched Systems

Jared Miller;Didier Henrion;Mario Sznaier;Milan Korda

2021 60th IEEE Conference on Decision and Control (CDC)

Year: 2021 | Conference Paper |

Nonlinear Data-Driven Control via State-Dependent Representations

T. Dai;M. Sznaier

2021 60th IEEE Conference on Decision and Control (CDC)

Year: 2021 | Conference Paper |

Cited by: Papers (12)

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Recently, there has been renewed interest in data-driven control, that is, the design of controllers directly from observed data. In the case of linear time-invariant (LTI) systems, several approaches have been proposed that lead to tractable optimization problems. On the other hand, the case of nonlinear dynamics is considerably less developed, with existing approaches limited to at most rational...Show More

Nonlinear Data-Driven Control via State-Dependent Representations

T. Dai;M. Sznaier

2021 60th IEEE Conference on Decision and Control (CDC)

Year: 2021 | Conference Paper |

Globally Convergent Low Complexity Algorithms for Semidefinite Programming

Biel Roig-Solvas;M. Sznaier

2021 60th IEEE Conference on Decision and Control (CDC)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

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Semidefinite programs (SDP) are a staple of today’s systems theory, with applications ranging from robust control to systems identification. However, current state-of-the art solution methods have poor scaling properties, and thus are limited to relatively moderate size problems. Recently, several approximations have been proposed where the original SDP is relaxed to a sequence of lower complexity...Show More

Globally Convergent Low Complexity Algorithms for Semidefinite Programming

Biel Roig-Solvas;M. Sznaier

2021 60th IEEE Conference on Decision and Control (CDC)

Year: 2021 | Conference Paper |

Peak Estimation Recovery and Safety Analysis

Jared Miller;Didier Henrion;Mario Sznaier

2021 American Control Conference (ACC)

Year: 2021 | Conference Paper |

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Peak Estimation aims to find the maximum value of a state function achieved by a dynamical system. This problem has been previously cast as a convex infinite-dimensional linear program on occupation measures, which can be approximately solved by a converging hierarchy of moment relaxations. In this paper, we present an algorithm to approximate optimal trajectories if the solutions to these relaxat...Show More

Peak Estimation Recovery and Safety Analysis

Jared Miller;Didier Henrion;Mario Sznaier

2021 American Control Conference (ACC)

Year: 2021 | Conference Paper |

Peak Estimation Recovery and Safety Analysis

Jared Miller;Didier Henrion;Mario Sznaier

IEEE Control Systems Letters

Year: 2021 | Volume: 5, Issue: 6 | Journal Article |

Cited by: Papers (13)

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Peak Estimation aims to find the maximum value of a state function achieved by a dynamical system. This problem has been previously cast as a convex infinite-dimensional linear program on occupation measures, which can be approximately solved by a converging hierarchy of moment relaxations. In this letter, we present an algorithm to approximate optimal trajectories if the solutions to these relaxa...Show More

Peak Estimation Recovery and Safety Analysis

Jared Miller;Didier Henrion;Mario Sznaier

IEEE Control Systems Letters

Year: 2021 | Volume: 5, Issue: 6 | Journal Article |

Control Oriented Learning in the Era of Big Data

IEEE Control Systems Letters

Year: 2021 | Volume: 5, Issue: 6 | Journal Article |

Cited by: Papers (14)

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Recent advances in control, coupled with an exponential growth in data gathering capabilities, have made feasible a wide range of applications that can profoundly impact society. Yet, achieving this vision requires addressing the challenge of extracting control relevant information from large amounts of data, a problem that has proven to be surprisingly difficult. While modern machine learning tec...Show More

Control Oriented Learning in the Era of Big Data

IEEE Control Systems Letters

Year: 2021 | Volume: 5, Issue: 6 | Journal Article |

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