Feedback linearizing indirect adaptive fuzzy control with foraging based on-line plant model estimation. - Suvadeep Banerjee; Ankush Chakrabarty; Sayan Maity; Amitava Chatterjee (Appl. Soft Comput., 2011)

Hyperspectral image classification incorporating bacterial foraging-optimized spectral weighting. - Ankush Chakrabarty; Olivia Choudhury; Pallab Sarkar; Avishek Paul; Debarghya Sarkar (Artif. Intell. Res., 2012)

Treating acute myeloid leukemia via HSC transplantation: A preliminary study of multi-objective personalization strategies. - Ankush Chakrabarty; Serena M. Pearce; Robert P. Nelson; Ann E. Rundell (ACC, 2013)

Volterra kernel based face recognition using artificial bee colonyoptimization. - Ankush Chakrabarty; Harsh Jain; Amitava Chatterjee (Eng. Appl. Artif. Intell., 2013)

Robust explicit nonlinear model predictive control with integral sliding mode. - Ankush Chakrabarty; Vu C. Dinh; Gregery T. Buzzard; Stanislaw H. Zak; Ann E. Rundell (ACC, 2014)

Correcting hypothalamic-pituitary-adrenal axis dysfunction using observer-based explicit nonlinear model predictive control. - Ankush Chakrabarty; Gregery T. Buzzard; Martin J. Corless; Stanislaw H. Zak; Ann E. Rundell (EMBC, 2014)

Simultaneous Unknown Input And Sensor Noise Reconstruction For Nonlinear Systems With Boundary Layer Sliding Mode Observers. - Ankush Chakrabarty; Gregery T. Buzzard; Stanislaw H. Zak; Fanglai Zhu; Ann E. Rundell (CoRR, 2015)

Distributed unknown input observers for interconnected nonlinear systems. - Ankush Chakrabarty; Shreyas Sundaram; Martin J. Corless; Gregery T. Buzzard; Stanislaw H. Zak; Ann E. Rundell (ACC, 2016)

HAPI-Gen: Highly Accurate Phasing and Imputation of Genotype Data. - Olivia Choudhury; Ankush Chakrabarty; Scott J. Emrich (BCB, 2016)

Unknown input estimation via observers for nonlinear systems with measurement delays. - Ankush Chakrabarty; Gregery T. Buzzard; Emilia Fridman; Stanislaw H. Zak (CDC, 2016)

State and unknown input observers for discrete-time nonlinear systems. - Ankush Chakrabarty; Stanislaw H. Zak; Shreyas Sundaram (CDC, 2016)

Sampling-based explicit nonlinear model predictive control for output tracking. - Haotian Zhang 001; Ankush Chakrabarty; Raid Ayoub; Gregery T. Buzzard; Shreyas Sundaram (CDC, 2016)

Nonfragile Fault-Tolerant Fuzzy Observer-Based Controller Design for Nonlinear Systems. - Xiaohang Li; Fanglai Zhu; Ankush Chakrabarty; Stanislaw H. Zak (IEEE Trans. Fuzzy Syst., 2016)

Supervised learning-based explicit nonlinear model predictive control and unknown input estimation in biomedical systems - Ankush Chakrabarty (2016)

Reducing controller updates via event-triggered model predictive control in an embedded artificial pancreas. - Ankush Chakrabarty; Stamatina Zavitsanou; Francis J. Doyle; Eyal Dassau (ACC, 2017)

Ultrafast embedded explicit model predictive control for nonlinear systems. - Arnab Raha; Ankush Chakrabarty; Vijay Raghunathan; Gregery T. Buzzard (ACC, 2017)

Robust state and unknown input estimation for nonlinear systems characterized by incremental multiplier matrices. - Stanislaw H. Zak; Ankush Chakrabarty; Gregery T. Buzzard (ACC, 2017)

Model predictive control with event-triggered communication for an embedded artificial pancreas. - Ankush Chakrabarty; Stamatina Zavitsanou; Francis J. Doyle; Eyal Dassau (CCTA, 2017)

Delayed unknown input observers for discrete-time linear systems with guaranteed performance. - Ankush Chakrabarty; Raid Ayoub; Stanislaw H. Zak; Shreyas Sundaram (Syst. Control. Lett., 2017)

State and Unknown Input Observers for Nonlinear Systems With Bounded Exogenous Inputs. - Ankush Chakrabarty; Martin J. Corless; Gregery T. Buzzard; Stanislaw H. Zak; Ann E. Rundell (IEEE Trans. Autom. Control., 2017)

Support Vector Machine Informed Explicit Nonlinear Model Predictive Control Using Low-Discrepancy Sequences. - Ankush Chakrabarty; Vu C. Dinh; Martin J. Corless; Ann E. Rundell; Stanislaw H. Zak; Gregery T. Buzzard (IEEE Trans. Autom. Control., 2017)

Output-Tracking Quantized Explicit Nonlinear Model Predictive Control Using Multiclass Support Vector Machines. - Ankush Chakrabarty; Gregery T. Buzzard; Stanislaw H. Zak (IEEE Trans. Ind. Electron., 2017)

Deep Learning Assisted Macronutrient Estimation For Feedforward-Feedback Control In Artificial Pancreas Systems. - Ankush Chakrabarty; Francis J. Doyle; Eyal Dassau (ACC, 2018)

Data-Driven Estimation of Backward Reachable and Invariant Sets for Unmodeled Systems via Active Learning. - Ankush Chakrabarty; Arvind U. Raghunathan; Stefano Di Cairano; Claus Danielson (CDC, 2018)

State and unknown input observers for nonlinear systems with delayed measurements. - Ankush Chakrabarty; Emilia Fridman; Stanislaw H. Zak; Gregery T. Buzzard (Autom., 2018)

Unknown Input Estimation for Nonlinear Systems Using Sliding Mode Observers and Smooth Window Functions. - Ankush Chakrabarty; Ann E. Rundell; Stanislaw H. Zak; Fanglai Zhu; Gregery T. Buzzard (SIAM J. Control. Optim., 2018)

Event-Triggered Model Predictive Control for Embedded Artificial Pancreas Systems. - Ankush Chakrabarty; Stamatina Zavitsanou; Francis J. Doyle; Eyal Dassau (IEEE Trans. Biomed. Eng., 2018)

Data-Driven Control Policies for Partially Known Systems via Kernelized Lipschitz Learning. - Ankush Chakrabarty; Devesh K. Jha; Yebin Wang (ACC, 2019)

Robust Data-Driven Neuro-Adaptive Observers With Lipschitz Activation Functions. - Ankush Chakrabarty; Ali Zemouche; Rajesh Rajamani; Mouhacine Benosman (CDC, 2019)

Approximate Dynamic Programming For Linear Systems with State and Input Constraints. - Ankush Chakrabarty; Rien Quirynen; Claus Danielson; Weinan Gao (ECC, 2019)

L2Observers for a Class of Nonlinear Systems with Unknown Inputs. - Martin Corless; Ankush Chakrabarty (ECC, 2019)

Optimal dynamic scheduling of wireless networked control systems. - Yehan Ma; Jianlin Guo; Yebin Wang; Ankush Chakrabarty; Heejin Ahn; Philip V. Orlik; Chenyang Lu 001 (ICCPS, 2019)

Learning autonomous vehicle passengers' preferred driving styles using g-g plots and haptic feedback. - Uros Kalabic; Ankush Chakrabarty; Rien Quirynen; Stefano Di Cairano (ITSC, 2019)

Near-Optimal Control of Motor Drives via Approximate Dynamic Programming. - Yebin Wang; Ankush Chakrabarty; MengChu Zhou; Jinyun Zhang (SMC, 2019)

Pharmaceutical-based entrainment of circadian phase via nonlinear model predictive control. - John H. Abel; Ankush Chakrabarty; Elizabeth B. Klerman; Francis J. Doyle III (Autom., 2019)

L2 Observers for a Class of Nonlinear Systems with Unknown Inputs. - Martin J. Corless; Ankush Chakrabarty (CoRR, 2019)

Highly Accurate and Efficient Data-Driven Methods for Genotype Imputation. - Olivia Choudhury; Ankush Chakrabarty; Scott J. Emrich (IEEE ACM Trans. Comput. Biol. Bioinform., 2019)

Learning-based Parameter-Adaptive Reference Governors. - Ankush Chakrabarty; Karl Berntorp; Stefano Di Cairano (ACC, 2020)

Dynamic Mode Decomposition and Robust Estimation: Case Study of a 2D Turbulent Boussinesq Flow. - Sanjana Vijayshankar; Saleh Nabi; Ankush Chakrabarty; Piyush Grover; Mouhacine Benosman (ACC, 2020)

Data-Driven Optimal Tracking with Constrained Approximate Dynamic Programming for Servomotor Systems. - Ankush Chakrabarty; Claus Danielson; Yebin Wang (CCTA, 2020)

Embedded Model Predictive Control for a Wearable Artificial Pancreas. - Ankush Chakrabarty; Elizabeth Healey; Dawei Shi; Stamatina Zavitsanou; Francis J. Doyle; Eyal Dassau (IEEE Trans. Control. Syst. Technol., 2020)

Embedding Approximate Nonlinear Model Predictive Control at Ultrahigh Speed and Extremely Low Power. - Arnab Raha; Ankush Chakrabarty; Vijay Raghunathan; Gregery T. Buzzard (IEEE Trans. Control. Syst. Technol., 2020)

Vehicle Center-of-Gravity Height and Dynamics Estimation with Uncertainty Quantification by Marginalized Particle Filter. - Karl Berntorp; Ankush Chakrabarty; Stefano Di Cairano (ACC, 2021)

Vehicle Rollover Avoidance by Parameter-Adaptive Reference Governor. - Karl Berntorp; Ankush Chakrabarty; Stefano Di Cairano (CDC, 2021)

Simulation Failure Robust Bayesian Optimization for Estimating Black-Box Model Parameters. - Ankush Chakrabarty; Scott A. Bortoff; Christopher R. Laughman (SMC, 2021)

Learning Disagreement Regions with Deep Neural Networks to Reduce Practical Complexity of Mixed-Integer MPC. - Ankush Chakrabarty; Rien Quirynen; Diego Romeres; Stefano Di Cairano (SMC, 2021)

Anomalous Sound Detection Using Attentive Neural Processes. - Gordon Wichern; Ankush Chakrabarty; Zhong-Qiu Wang 001; Jonathan Le Roux (WASPAA, 2021)

Safe learning-based observers for unknown nonlinear systems using Bayesian optimization. - Ankush Chakrabarty; Mouhacine Benosman (Autom., 2021)

Model Order Reduction for Water Quality Dynamics. - Shen Wang 001; Ahmad F. Taha; Ankush Chakrabarty; Lina Sela; Ahmed A. Abokifa (CoRR, 2021)

Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins. - Ankush Chakrabarty; Gordon Wichern; Christopher R. Laughman (CoRR, 2021)

Safe Approximate Dynamic Programming via Kernelized Lipschitz Estimation. - Ankush Chakrabarty; Devesh K. Jha; Gregery T. Buzzard; Yebin Wang; Kyriakos G. Vamvoudakis (IEEE Trans. Neural Networks Learn. Syst., 2021)

VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints. - Wenjie Xu; Colin N. Jones; Bratislav Svetozarevic; Christopher R. Laughman; Ankush Chakrabarty (ACC, 2022)

Learning Optimization-based Control Policies Directly from Digital Twin Simulations. - Marcel Menner; Ankush Chakrabarty; Karl Berntorp; Stefano Di Cairano (CCTA, 2022)

Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach. - Ankush Chakrabarty (CDC, 2022)

Efficient Multi-Step Lookahead Bayesian Optimization with Local Search Constraints. - Joel A. Paulson; Farshud Sorourifar; Ankush Chakrabarty (CDC, 2022)

Safe multi-agent motion planning via filtered reinforcement learning. - Abraham P. Vinod; Sleiman Safaoui; Ankush Chakrabarty; Rien Quirynen; Nobuyuki Yoshikawa; Stefano Di Cairano (ICRA, 2022)

PRISM: Recurrent Neural Networks and Presolve Methods for Fast Mixed-integer Optimal Control. - Abhishek Cauligi; Ankush Chakrabarty; Stefano Di Cairano; Rien Quirynen (L4DC, 2022)

ENOS: Energy-Aware Network Operator Search in Deep Neural Networks. - Shamma Nasrin; Ahish Shylendra; Nastaran Darabi; Theja Tulabandhula; Wilfred Gomes; Ankush Chakrabarty; Amit Ranjan Trivedi (IEEE Access, 2022)

Data-Driven Identification of Dynamic Quality Models in Drinking Water Networks. - Shen Wang 001; Ankush Chakrabarty; Ahmad F. Taha (CoRR, 2022)

Meta-Learning of Neural State-Space Models Using Data From Similar Systems. - Ankush Chakrabarty; Gordon Wichern; Christopher R. Laughman (CoRR, 2022)

Co-design of reduced-order models and observers from thermo-fluid data. - Sanjana Vijayshankar; Ankush Chakrabarty; Piyush Grover; Saleh Nabi (IFAC J. Syst. Control., 2022)

Optimal Dynamic Transmission Scheduling for Wireless Networked Control Systems. - Yehan Ma; Jianlin Guo; Yebin Wang; Ankush Chakrabarty; Heejin Ahn; Philip V. Orlik; Xinping Guan; Chenyang Lu 001 (IEEE Trans. Control. Syst. Technol., 2022)

Active Learning for Estimating Reachable Sets for Systems With Unknown Dynamics. - Ankush Chakrabarty; Claus Danielson; Stefano Di Cairano; Arvind U. Raghunathan (IEEE Trans. Cybern., 2022)

Learning Residual Dynamics via Physics-Augmented Neural Networks: Application to Vapor Compression Cycles. - Raphael Chinchilla; Vedang M. Deshpande; Ankush Chakrabarty; Christopher R. Laughman (ACC, 2023)

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems. - Truong X. Nghiem; Ján Drgona; Colin N. Jones; Zoltán Nagy 002; Roland Schwan; Biswadip Dey; Ankush Chakrabarty; Stefano Di Cairano; Joel A. Paulson; Andrea Carron; Melanie N. Zeilinger; Wenceslao Shaw-Cortez; et al. (ACC, 2023)

LSR-BO: Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems. - Joel A. Paulson; Farshud Sorourifar; Christopher R. Laughman; Ankush Chakrabarty (ACC, 2023)

Synthesizing Building Operation Data with Generative Models: VAEs, GANs, or Something In Between? - Alessandro Salatiello; Ye Wang 001; Gordon Wichern; Toshiaki Koike-Akino; Yoshihiro Ohta; Yosuke Kaneko; Christopher R. Laughman; Ankush Chakrabarty (e-Energy (Companion), 2023)

Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints. - Wenjie Xu; Colin N. Jones; Bratislav Svetozarevic; Christopher R. Laughman; Ankush Chakrabarty (CoRR, 2023)

Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning. - Sleiman Safaoui; Abraham P. Vinod; Ankush Chakrabarty; Rien Quirynen; Nobuyuki Yoshikawa; Stefano Di Cairano (CoRR, 2023)

Physics-Constrained Deep Autoencoded Kalman Filters for Estimating Vapor Compression System States. - Vedang M. Deshpande; Ankush Chakrabarty; Abraham P. Vinod; Christopher R. Laughman (IEEE Control. Syst. Lett., 2023)

Extremum Seeking Control With an Adaptive Gain Based on Gradient Estimation Error. - Claus Danielson; Scott A. Bortoff; Ankush Chakrabarty (IEEE Trans. Syst. Man Cybern. Syst., 2023)

Simulation Failure-Robust Bayesian Optimization for Data-Driven Parameter Estimation. - Ankush Chakrabarty; Scott A. Bortoff; Christopher R. Laughman (IEEE Trans. Syst. Man Cybern. Syst., 2023)

Aircraft Approach Management Using Reachability and Dynamic Programming. - Abraham P. Vinod; Sachiyo Yamazaki; Ankush Chakrabarty; Nobuyuki Yoshikawa; Stefano Di Cairano (ACC, 2024)

MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models. - Jiaqi Yan; Ankush Chakrabarty; Alisa Rupenyan; John Lygeros (CASE, 2024)

Assessing Building Control Performance Using Physics-Based Simulation Models and Deep Generative Networks. - Ankush Chakrabarty; Luigi Vanfretti; Wei-Ting Tang; Joel A. Paulson; Sicheng Zhan; Scott A. Bortoff; Vedang M. Deshpande; Ye Wang 001; Christopher R. Laughman (CCTA, 2024)

Bayesian Forecasting with Deep Generative Disturbance Models in Stochastic MPC for Building Energy Systems. - Farshud Sorourifar; Joel A. Paulson; Ye Wang 001; Rien Quirynen; Christopher R. Laughman; Ankush Chakrabarty (CCTA, 2024)

Physics-Constrained Meta-Learning for Online Adaptation and Estimation in Positioning Applications. - Ankush Chakrabarty; Vedang M. Deshpande; Gordon Wichern; Karl Berntorp (CDC, 2024)

BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems. - Wei-Ting Tang; Ankush Chakrabarty; Joel A. Paulson (CoRR, 2024)

Coactive Preference-Guided Multi-Objective Bayesian Optimization: An Application to Policy Learning in Personalized Plasma Medicine. - Ketong Shao; Ankush Chakrabarty; Ali Mesbah 002; Diego Romeres (IEEE Control. Syst. Lett., 2024)

Safe Multiagent Motion Planning Under Uncertainty for Drones Using Filtered Reinforcement Learning. - Sleiman Safaoui; Abraham P. Vinod; Ankush Chakrabarty; Rien Quirynen; Nobuyuki Yoshikawa; Stefano Di Cairano (IEEE Trans. Robotics, 2024)

User Preference Meets Pareto-Optimality in Multi-Objective Bayesian Optimization. - Joshua Hang Sai Ip; Ankush Chakrabarty; Ali Mesbah 002; Diego Romeres (AAAI, 2025)

Navigating the Trade-offs and Synergies of Economic and Environmental Sustainability Using Process Systems Engineering. - Qi Zhang 016; Styliani Avraamidou; Joel A. Paulson; Vyom Thakker; Zhenyu Wang; Leo Chiang; Birgit Braun; Tushar Rathi; Ankush Chakrabarty; Farshud Sorourifar; Wei-Ting Tang; Paola Munoz; et al. (ACC, 2025)

Meta-Learning for Physically-Constrained Neural System Identification. - Ankush Chakrabarty; Gordon Wichern; Vedang M. Deshpande; Abraham P. Vinod; Karl Berntorp; Christopher R. Laughman (CoRR, 2025)

User-Preference Meets Pareto-Optimality: Multi-Objective Bayesian Optimization with Local Gradient Search. - Joshua Hang Sai Ip; Ankush Chakrabarty; Ali Mesbah 002; Diego Romeres (CoRR, 2025)

Manifold meta-learning for reduced-complexity neural system identification. - Marco Forgione; Ankush Chakrabarty; Dario Piga; Matteo Rufolo; Alberto Bemporad (CoRR, 2025)

Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models. - Young Jin Park; François G. Germain; Jing Liu 009; Ye Wang 001; Toshiaki Koike-Akino; Gordon Wichern; Navid Azizan; Christopher R. Laughman; Ankush Chakrabarty (CoRR, 2025)