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Ankush Chakrabarty
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2020 – today
- 2024
- [j25]Sleiman Safaoui
, Abraham P. Vinod
Safe Multiagent Motion Planning Under Uncertainty for Drones Using Filtered Reinforcement Learning. IEEE Trans. Robotics 40: 2529-2542 (2024) - [c43]Abraham P. Vinod, Sachiyo Yamazaki, Ankush Chakrabarty, Nobuyuki Yoshikawa, Stefano Di Cairano:
Aircraft Approach Management Using Reachability and Dynamic Programming. ACC 2024: 318-324 - [c42]Jiaqi Yan, Ankush Chakrabarty, Alisa Rupenyan, John Lygeros:
MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models. CASE 2024: 1910-1915 - [c41]Farshud Sorourifar, Joel A. Paulson, Ye Wang, Rien Quirynen, Christopher R. Laughman, Ankush Chakrabarty:
Bayesian Forecasting with Deep Generative Disturbance Models in Stochastic MPC for Building Energy Systems. CCTA 2024: 414-419 - [c40]Ankush Chakrabarty, Luigi Vanfretti, Wei-Ting Tang, Joel A. Paulson, Sicheng Zhan, Scott A. Bortoff, Vedang M. Deshpande, Ye Wang, Christopher R. Laughman:
Assessing Building Control Performance Using Physics-Based Simulation Models and Deep Generative Networks. CCTA 2024: 547-554 - [i17]Jiaqi Yan, Ankush Chakrabarty, Alisa Rupenyan
MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models. CoRR abs/2404.12097 (2024) - [i16]Wei-Ting Tang, Ankush Chakrabarty, Joel A. Paulson:
BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems. CoRR abs/2406.03616 (2024) - 2023
- [j24]Vedang M. Deshpande
Physics-Constrained Deep Autoencoded Kalman Filters for Estimating Vapor Compression System States. IEEE Control. Syst. Lett. 7: 3483-3488 (2023) - [j23]Claus Danielson, Scott A. Bortoff
Extremum Seeking Control With an Adaptive Gain Based on Gradient Estimation Error. IEEE Trans. Syst. Man Cybern. Syst. 53(1): 152-164 (2023) - [j22]Ankush Chakrabarty
Simulation Failure-Robust Bayesian Optimization for Data-Driven Parameter Estimation. IEEE Trans. Syst. Man Cybern. Syst. 53(5): 2629-2640 (2023) - [c39]Joel A. Paulson, Farshud Sorourifar, Christopher R. Laughman, Ankush Chakrabarty:
LSR-BO: Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems. ACC 2023: 576-582 - [c38]Truong X. Nghiem
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems. ACC 2023: 3735-3750 - [c37]Raphael Chinchilla, Vedang M. Deshpande, Ankush Chakrabarty, Christopher R. Laughman:
Learning Residual Dynamics via Physics-Augmented Neural Networks: Application to Vapor Compression Cycles. ACC 2023: 4069-4076 - [c36]Alessandro Salatiello
Synthesizing Building Operation Data with Generative Models: VAEs, GANs, or Something In Between? e-Energy (Companion) 2023: 125-133 - [i15]Wenjie Xu, Colin N. Jones, Bratislav Svetozarevic, Christopher R. Laughman, Ankush Chakrabarty:
Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints. CoRR abs/2301.12099 (2023) - [i14]Truong X. Nghiem, Ján Drgona, Colin N. Jones, Zoltán Nagy, Roland Schwan, Biswadip Dey, Ankush Chakrabarty, Stefano Di Cairano, Joel A. Paulson, Andrea Carron, Melanie N. Zeilinger, Wenceslao Shaw-Cortez, Draguna L. Vrabie:
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems. CoRR abs/2306.13867 (2023) - [i13]Sleiman Safaoui, Abraham P. Vinod, Ankush Chakrabarty, Rien Quirynen, Nobuyuki Yoshikawa, Stefano Di Cairano:
Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning. CoRR abs/2311.00063 (2023) - 2022
- [j21]Shamma Nasrin
ENOS: Energy-Aware Network Operator Search in Deep Neural Networks. IEEE Access 10: 81447-81457 (2022) - [j20]Sanjana Vijayshankar
Co-design of reduced-order models and observers from thermo-fluid data. IFAC J. Syst. Control. 19: 100181 (2022) - [j19]Yehan Ma
Optimal Dynamic Transmission Scheduling for Wireless Networked Control Systems. IEEE Trans. Control. Syst. Technol. 30(6): 2360-2376 (2022) - [j18]Ankush Chakrabarty
Active Learning for Estimating Reachable Sets for Systems With Unknown Dynamics. IEEE Trans. Cybern. 52(4): 2531-2542 (2022) - [c35]Wenjie Xu, Colin N. Jones, Bratislav Svetozarevic, Christopher R. Laughman, Ankush Chakrabarty:
VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints. ACC 2022: 5288-5293 - [c34]Marcel Menner, Ankush Chakrabarty, Karl Berntorp, Stefano Di Cairano:
Learning Optimization-based Control Policies Directly from Digital Twin Simulations. CCTA 2022: 895-900 - [c33]Joel A. Paulson, Farshud Sorourifar, Ankush Chakrabarty:
Efficient Multi-Step Lookahead Bayesian Optimization with Local Search Constraints. CDC 2022: 123-129 - [c32]Ankush Chakrabarty:
Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach. CDC 2022: 130-136 - [c31]Abraham P. Vinod, Sleiman Safaoui, Ankush Chakrabarty, Rien Quirynen, Nobuyuki Yoshikawa, Stefano Di Cairano:
Safe multi-agent motion planning via filtered reinforcement learning. ICRA 2022: 7270-7276 - [c30]Abhishek Cauligi, Ankush Chakrabarty, Stefano Di Cairano, Rien Quirynen:
PRISM: Recurrent Neural Networks and Presolve Methods for Fast Mixed-integer Optimal Control. L4DC 2022: 34-46 - [i12]Shen Wang, Ankush Chakrabarty, Ahmad F. Taha:
Data-Driven Identification of Dynamic Quality Models in Drinking Water Networks. CoRR abs/2207.05983 (2022) - [i11]Ankush Chakrabarty:
Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach. CoRR abs/2211.00077 (2022) - [i10]Ankush Chakrabarty, Gordon Wichern, Christopher R. Laughman:
Meta-Learning of Neural State-Space Models Using Data From Similar Systems. CoRR abs/2211.07768 (2022) - 2021
- [j17]Ankush Chakrabarty, Mouhacine Benosman:
Safe learning-based observers for unknown nonlinear systems using Bayesian optimization. Autom. 133: 109860 (2021) - [j16]Ankush Chakrabarty
Safe Approximate Dynamic Programming via Kernelized Lipschitz Estimation. IEEE Trans. Neural Networks Learn. Syst. 32(1): 405-419 (2021) - [c29]Karl Berntorp, Ankush Chakrabarty, Stefano Di Cairano:
Vehicle Center-of-Gravity Height and Dynamics Estimation with Uncertainty Quantification by Marginalized Particle Filter. ACC 2021: 160-165 - [c28]Karl Berntorp, Ankush Chakrabarty, Stefano Di Cairano:
Vehicle Rollover Avoidance by Parameter-Adaptive Reference Governor. CDC 2021: 635-640 - [c27]Ankush Chakrabarty, Scott A. Bortoff, Christopher R. Laughman:
Simulation Failure Robust Bayesian Optimization for Estimating Black-Box Model Parameters. SMC 2021: 1533-1538 - [c26]Ankush Chakrabarty, Rien Quirynen, Diego Romeres, Stefano Di Cairano:
Learning Disagreement Regions with Deep Neural Networks to Reduce Practical Complexity of Mixed-Integer MPC. SMC 2021: 3238-3244 - [c25]Gordon Wichern, Ankush Chakrabarty, Zhong-Qiu Wang, Jonathan Le Roux:
Anomalous Sound Detection Using Attentive Neural Processes. WASPAA 2021: 186-190 - [i9]Shen Wang, Ahmad F. Taha, Ankush Chakrabarty, Lina Sela, Ahmed A. Abokifa:
Model Order Reduction for Water Quality Dynamics. CoRR abs/2102.10737 (2021) - [i8]Ankush Chakrabarty, Gordon Wichern, Christopher R. Laughman:
Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins. CoRR abs/2106.15502 (2021) - [i7]Claus Danielson, Scott A. Bortoff, Ankush Chakrabarty:
Extremum Seeking Control with an Adaptive Gain Based On Gradient Estimation Error. CoRR abs/2107.01176 (2021) - [i6]Wenjie Xu, Colin N. Jones, Bratislav Svetozarevic, Christopher R. Laughman, Ankush Chakrabarty:
VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints. CoRR abs/2110.07479 (2021) - 2020
- [j15]Arnab Raha
Embedding Approximate Nonlinear Model Predictive Control at Ultrahigh Speed and Extremely Low Power. IEEE Trans. Control. Syst. Technol. 28(3): 1092-1099 (2020) - [j14]Ankush Chakrabarty
Embedded Model Predictive Control for a Wearable Artificial Pancreas. IEEE Trans. Control. Syst. Technol. 28(6): 2600-2607 (2020) - [c24]Ankush Chakrabarty, Karl Berntorp, Stefano Di Cairano:
Learning-based Parameter-Adaptive Reference Governors. ACC 2020: 956-961 - [c23]Sanjana Vijayshankar, Saleh Nabi, Ankush Chakrabarty, Piyush Grover, Mouhacine Benosman:
Dynamic Mode Decomposition and Robust Estimation: Case Study of a 2D Turbulent Boussinesq Flow. ACC 2020: 2351-2356 - [c22]Ankush Chakrabarty, Claus Danielson, Yebin Wang:
Data-Driven Optimal Tracking with Constrained Approximate Dynamic Programming for Servomotor Systems. CCTA 2020: 352-357 - [i5]Ankush Chakrabarty, Mouhacine Benosman:
Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization. CoRR abs/2005.05888 (2020)
2010 – 2019
- 2019
- [j13]John H. Abel, Ankush Chakrabarty, Elizabeth B. Klerman, Francis J. Doyle III:
Pharmaceutical-based entrainment of circadian phase via nonlinear model predictive control. Autom. 100: 336-348 (2019) - [j12]Olivia Choudhury
Highly Accurate and Efficient Data-Driven Methods for Genotype Imputation. IEEE ACM Trans. Comput. Biol. Bioinform. 16(4): 1107-1116 (2019) - [c21]Ankush Chakrabarty, Devesh K. Jha, Yebin Wang:
Data-Driven Control Policies for Partially Known Systems via Kernelized Lipschitz Learning. ACC 2019: 4192-4197 - [c20]Ankush Chakrabarty, Ali Zemouche, Rajesh Rajamani, Mouhacine Benosman:
Robust Data-Driven Neuro-Adaptive Observers With Lipschitz Activation Functions. CDC 2019: 2862-2867 - [c19]Ankush Chakrabarty, Rien Quirynen, Claus Danielson, Weinan Gao
Approximate Dynamic Programming For Linear Systems with State and Input Constraints. ECC 2019: 524-529 - [c18]Martin Corless, Ankush Chakrabarty:
L2Observers for a Class of Nonlinear Systems with Unknown Inputs. ECC 2019: 1142-1147 - [c17]Yehan Ma, Jianlin Guo, Yebin Wang, Ankush Chakrabarty, Heejin Ahn, Philip V. Orlik, Chenyang Lu:
Optimal dynamic scheduling of wireless networked control systems. ICCPS 2019: 77-86 - [c16]Uros Kalabic, Ankush Chakrabarty, Rien Quirynen, Stefano Di Cairano:
Learning autonomous vehicle passengers' preferred driving styles using g-g plots and haptic feedback. ITSC 2019: 4012-4017 - [c15]Yebin Wang, Ankush Chakrabarty, MengChu Zhou, Jinyun Zhang:
Near-Optimal Control of Motor Drives via Approximate Dynamic Programming. SMC 2019: 3679-3686 - [i4]Martin J. Corless, Ankush Chakrabarty:
L2 Observers for a Class of Nonlinear Systems with Unknown Inputs. CoRR abs/1902.08288 (2019) - [i3]Ankush Chakrabarty, Rien Quirynen, Claus Danielson, Weinan Gao:
Approximate Dynamic Programming For Linear Systems with State and Input Constraints. CoRR abs/1906.11369 (2019) - [i2]Ankush Chakrabarty, Devesh K. Jha, Gregery T. Buzzard, Yebin Wang, Kyriakos G. Vamvoudakis:
Safe Approximate Dynamic Programming Via Kernelized Lipschitz Estimation. CoRR abs/1907.02151 (2019) - 2018
- [j11]Ankush Chakrabarty, Emilia Fridman, Stanislaw H. Zak, Gregery T. Buzzard
State and unknown input observers for nonlinear systems with delayed measurements. Autom. 95: 246-253 (2018) - [j10]Ankush Chakrabarty
Unknown Input Estimation for Nonlinear Systems Using Sliding Mode Observers and Smooth Window Functions. SIAM J. Control. Optim. 56(5): 3619-3641 (2018) - [j9]Ankush Chakrabarty
Event-Triggered Model Predictive Control for Embedded Artificial Pancreas Systems. IEEE Trans. Biomed. Eng. 65(3): 575-586 (2018) - [c14]Ankush Chakrabarty, Francis J. Doyle, Eyal Dassau:
Deep Learning Assisted Macronutrient Estimation For Feedforward-Feedback Control In Artificial Pancreas Systems. ACC 2018: 3564-3570 - [c13]Ankush Chakrabarty, Arvind U. Raghunathan, Stefano Di Cairano, Claus Danielson:
Data-Driven Estimation of Backward Reachable and Invariant Sets for Unmodeled Systems via Active Learning. CDC 2018: 372-377 - 2017
- [j8]Ankush Chakrabarty
Delayed unknown input observers for discrete-time linear systems with guaranteed performance. Syst. Control. Lett. 103: 9-15 (2017) - [j7]Ankush Chakrabarty, Vu C. Dinh, Martin J. Corless, Ann E. Rundell, Stanislaw H. Zak, Gregery T. Buzzard
Support Vector Machine Informed Explicit Nonlinear Model Predictive Control Using Low-Discrepancy Sequences. IEEE Trans. Autom. Control. 62(1): 135-148 (2017) - [j6]Ankush Chakrabarty
State and Unknown Input Observers for Nonlinear Systems With Bounded Exogenous Inputs. IEEE Trans. Autom. Control. 62(11): 5497-5510 (2017) - [j5]Ankush Chakrabarty, Gregery T. Buzzard
Output-Tracking Quantized Explicit Nonlinear Model Predictive Control Using Multiclass Support Vector Machines. IEEE Trans. Ind. Electron. 64(5): 4130-4138 (2017) - [c12]Ankush Chakrabarty, Stamatina Zavitsanou, Francis J. Doyle, Eyal Dassau:
Reducing controller updates via event-triggered model predictive control in an embedded artificial pancreas. ACC 2017: 134-139 - [c11]Stanislaw H. Zak, Ankush Chakrabarty, Gregery T. Buzzard
Robust state and unknown input estimation for nonlinear systems characterized by incremental multiplier matrices. ACC 2017: 3270-3275 - [c10]Arnab Raha
Ultrafast embedded explicit model predictive control for nonlinear systems. ACC 2017: 4398-4403 - [c9]Ankush Chakrabarty, Stamatina Zavitsanou, Francis J. Doyle, Eyal Dassau:
Model predictive control with event-triggered communication for an embedded artificial pancreas. CCTA 2017: 536-541 - 2016
- [b1]Ankush Chakrabarty:
Supervised learning-based explicit nonlinear model predictive control and unknown input estimation in biomedical systems. Purdue University, USA, 2016 - [j4]Xiaohang Li, Fanglai Zhu, Ankush Chakrabarty, Stanislaw H. Zak:
Nonfragile Fault-Tolerant Fuzzy Observer-Based Controller Design for Nonlinear Systems. IEEE Trans. Fuzzy Syst. 24(6): 1679-1689 (2016) - [c8]Ankush Chakrabarty, Shreyas Sundaram, Martin J. Corless, Gregery T. Buzzard
Distributed unknown input observers for interconnected nonlinear systems. ACC 2016: 101-106 - [c7]Olivia Choudhury, Ankush Chakrabarty, Scott J. Emrich
HAPI-Gen: Highly Accurate Phasing and Imputation of Genotype Data. BCB 2016: 78-87 - [c6]Ankush Chakrabarty, Gregery T. Buzzard
Unknown input estimation via observers for nonlinear systems with measurement delays. CDC 2016: 2308-2313 - [c5]Haotian Zhang, Ankush Chakrabarty, Raid Ayoub, Gregery T. Buzzard
Sampling-based explicit nonlinear model predictive control for output tracking. CDC 2016: 4722-4727 - [c4]Ankush Chakrabarty, Stanislaw H. Zak, Shreyas Sundaram:
State and unknown input observers for discrete-time nonlinear systems. CDC 2016: 7111-7116 - 2015
- [i1]Ankush Chakrabarty, Gregery T. Buzzard, Stanislaw H. Zak, Fanglai Zhu, Ann E. Rundell:
Simultaneous Unknown Input And Sensor Noise Reconstruction For Nonlinear Systems With Boundary Layer Sliding Mode Observers. CoRR abs/1507.03924 (2015) - 2014
- [c3]Ankush Chakrabarty, Vu C. Dinh, Gregery T. Buzzard
Robust explicit nonlinear model predictive control with integral sliding mode. ACC 2014: 2851-2856 - [c2]Ankush Chakrabarty, Gregery T. Buzzard
Correcting hypothalamic-pituitary-adrenal axis dysfunction using observer-based explicit nonlinear model predictive control. EMBC 2014: 3426-3429 - 2013
- [j3]Ankush Chakrabarty, Harsh Jain, Amitava Chatterjee
Volterra kernel based face recognition using artificial bee colonyoptimization. Eng. Appl. Artif. Intell. 26(3): 1107-1114 (2013) - [c1]Ankush Chakrabarty, Serena M. Pearce, Robert P. Nelson, Ann E. Rundell:
Treating acute myeloid leukemia via HSC transplantation: A preliminary study of multi-objective personalization strategies. ACC 2013: 3790-3795 - 2012
- [j2]Ankush Chakrabarty, Olivia Choudhury, Pallab Sarkar, Avishek Paul, Debarghya Sarkar:
Hyperspectral image classification incorporating bacterial foraging-optimized spectral weighting. Artif. Intell. Res. 1(1): 63-83 (2012) - 2011
- [j1]Suvadeep Banerjee
Feedback linearizing indirect adaptive fuzzy control with foraging based on-line plant model estimation. Appl. Soft Comput. 11(4): 3441-3450 (2011)
Coauthor Index
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last updated on 2024-11-15 20:37 CET by the dblp team
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