publications

Journal Publications

  • Dirza, R., Varadarajan, H.P., Aas, V., Skogestad, S., Krishnamoorthy, D., 2024. A Comparative Study of Distributed Feedback Optimizing Control Architectures, IEEE Transactions on Control Systems Technology (In-Press) [pdf]
  • Krishnamoorthy, D., 2023. An Improved Data Augmentation Scheme for Model Predictive Control Policy Approximation, IEEE Control System Letters, Vol. 7, p. 1867 - 1872. [pdf]
  • Orrico, C. A., van Berkel, M., Bosman, T., Heemels, W.P.M.H., Krishnamoorthy, D., 2023. Mixed-Integer MPC Strategies for Fueling and Density Control in Fusion Tokamaks, IEEE Control System Letters, Vol. 7, p. 1897 - 1902. (IEEE CSS TC-ES Outstanding Student Paper Prize) [pdf]
  • Bosman, T., Koechl, F., Ho, A., de Baar, M., Krishnamoorthy, D., van Berkel, M., 2023. Integrated model control simulations of the electron density profile and the implications of using multiple discrete pellet injectors for control, Nuclear Fusion, Vol. 63, p. 126047. [pdf]
  • Mdoe, Z., Krishnamoorthy, D., Jäschke, J. 2023. Stability Properties of the Adaptive Horizon Multi-Stage MPC. Journal of Process Control, Vol. 128, p. 103002. [pdf]
  • Krishnamoorthy, D. and Doyle III, F. J., 2022. Safe and Personalized Meal Bolus Calculator for Type-1 Diabetes using Bayesian Optimization. IEEE Transactions on Biomedical Engineering, Vol. 70(5), p. 1481 - 1492. [pdf][video]
  • Krishnamoorthy, D., 2022. A Sensitivity-based Data Augmentation for Model Predictive Controller Policy Approximation. IEEE Transactions on Automatic Control, Vol. 67(11), p. 6090 - 6097. [pdf][code]
  • Dirza, R., Matias, J., Skogestad, S., and Krishnamoorthy, D. 2022. Experimental validation of distributed feedback-based RTO, Control Engineering Practice Vol 126, p. 105253.[pdf]
  • Krishnamoorthy, D. and Doyle III, F. J., 2022. Model-free Real-time Optimization of Process Systems using Safe Bayesian Optimization. AIChE Journal, Vol. 69(4), p. e17993. [pdf]
  • Krishnamoorthy, D. and Doyle III, F. J., 2022. Safe Bayesian Optimization using Interior-Point Methods - Applied to Personalized Insulin Dose Guidance. IEEE Control System Letters Vol. 6, p. 2834 - 2839. [pdf][extended version]
  • Krishnamoorthy, D. and Skogestad, S., 2022. Real-Time Optimization as a Feedback Control Problem - A Review. Comput. & Chem. Eng. Vol. 161, pp. 107723. (Invited paper in connection to the Excellence in CAPE PhD thesis award) [pdf]
  • Krishnamoorthy, D., 2021. A Distributed Feedback-based Online Process Optimization Framework for Optimal Resource Sharing. J. Proc. Control, Vol 97, p.72-83. [pdf][Code]
  • Krishnamoorthy, D., Dimitri Boiroux, Tinna Björk Aradottir, Sarah Ellinor Engell and John Bagterp Jørgensen, 2021. A Model-free Approach to Automatic Dose Guidance in Long Acting Insulin Treatment of Type 2 Diabetes. IEEE Control System Letters, Vol. 5(6), p.2030 - 2035. [pdf][Code][slides][video]
  • Krishnamoorthy, D., Skogestad, S., 2020 Systematic design of active constraint switching using selectors. Comput. & Chem. Eng. Vol. 143, p. 107106. [pdf][Code][slides][video]
  • Krishnamoorthy, D., Biegler, L. and Jäschke, J., 2020 Adaptive Horizon Economic Nonlinear Model Predictive Control. J. Proc. Control, Vol 92, p.108-118. [pdf][slides]
  • Jahanshahi, E., Krishnamoorthy, D., Codas, A., Foss, B. and Skogestad, S., 2020. Plantwide control of an oil production network, Comput. & Chem. Eng. [pdf]
  • Krishnamoorthy, D., Fjalestad, K. and Skogestad, S., 2019. Optimal Control of offshore oil and gas production using simple feedback controllers, Control Engineering Practice Vol 91, 104107. [pdf] [Code]
  • Krishnamoorthy, D., and Skogestad, S., 2019. Online process optimization with changes in active constraint sets using simple feedback control structures, Ind. Eng. Chem. Res. Vol. 58, p.13555 - 13567. DOI: 10.1021/acs.iecr.9b00308 [pdf] [Code Example 1], [Code Example 2]
  • Straus, J., Krishnamoorthy, D. and Skogestad, S., 2019. Combining self-optimizing control and extremum seeking control - Applied to ammonia reactor case study, J. Proc. Control. Vol 78, pp.78-87.[pdf][slides]
  • Krishnamoorthy, D., Foss, B. and Skogestad, S., 2018. A Primal decomposition algorithm for distributed multistage scenario model predictive control. J. Proc. Control, Vol 81, pp 162-17* [pdf] [Code][slides]
  • Krishnamoorthy, D., Jahanshahi, E. and Skogestad, S., 2019. A feedback RTO strategy using Transient Measurements, Ind. Eng. Chem. Res. Vol 58 (1), p.207-216. [pdf]
  • Krishnamoorthy, D., Foss, B. and Skogestad, S., 2018. Steady-State Real-time Optimization using Transient Measurements. Comput. & Chem. Eng., Vol 115, p.34-45 [pdf]
  • Krishnamoorthy, D., Foss, B. Suwartadi, E., Jäschke, J. and Skogestad, S., 2018. Improving Scenario Decomposition for Multistage MPC using a Sensitivity-based Path-following Algorithm, IEEE Control System Letters, Vol 2(4), p.581-586 [pdf][slides]
  • Krishnamoorthy, D., Foss, B. and Skogestad, S., 2016. Real-Time Optimization under Uncertainty Applied to a Gas Lifted Well Network. Processes, 4(4), p.52 [pdf]

Peer-reviewed Conference Publications

  • Krishnamoorthy, D., 2024. ECCBO: An Inherently Safe Bayesian Optimization with Embedded Constraint Control for Real-Time Optimization. IFAC ADCHEM, Toronto, Canada (Keynote paper) [pdf]
  • Van der Horst, A., Meere, B., Krishnamoorthy, D., Bakker, S., van de Vrande, B., Stoutjesdijk, H., Alonso, M., Torta, E., 2024. A Bayesian optimization framework for the automatic tuning of MPC-based shared controllers. Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan. [pdf]
  • Krishnamoorthy, D., 2023. An Improved Data Augmentation Scheme for Model Predictive Control Policy Approximation, Proceedings of the 2023 IEEE Conference on Decision and Control, Singapore. [pdf]
  • Orrico, C. A., van Berkel, M., Bosman, T., Heemels, W.P.M.H., Krishnamoorthy, D., 2023. Mixed-Integer MPC Strategies for Fueling and Density Control in Fusion Tokamaks, Proceedings of the 2023 IEEE Conference on Decision and Control, Singapore. [pdf]
  • Chanfreut, P., Maestre, J.M., Krishnamoorthy, D. and Camacho, E.F., 2023. ALADIN-based Distributed Model Predictive Control with dynamic partitioning: An application to Solar Parabolic Trough Plants. Proceedings of the 2023 IEEE Conference on Decision and Control, Singapore. [pdf]
  • Krishnamoorthy, D. and Paulson, J., 2023. Multi-agent Black-box Optimization using a Bayesian Approach to Alternating Direction Method of Multipliers, IFAC World Congress, Yokohama, Japan (In-Press). [pdf][video]
  • Krishnamoorthy, D., 2023. On Tuning Parameterized Control Policies Online for Safety-Critical Systems – Applied to Biomedical Systems, IFAC World Congress, Yokohama, Japan (In-Press). [pdf][video]
  • Krishnamoorthy, D., 2023. Optimizing Surplus Heat Recovery using Fast Fourier Transform-based Extremum Seeking Control, IFAC World Congress, Yokohama, Japan (In-Press). [pdf][video]
  • Aas, V., Dirza, R., Krishnamoorthy, D., Skogestad, S., 2023. A comparative study of distributed feedback-optimizing control strategies, Computer-aided Chemical Engineering, (in-Press).
  • Krishnamoorthy, D. and Kungurtsev, V., 2022. A Sensitivity-assisted Alternating Directions Method of Multipliers for Distributed Optimization, Proceedings of the 2022 IEEE Conference on Decision and Control, Cancun, Mexico. [pdf][video]
  • Krishnamoorthy, D. and Doyle III, F. J., 2022. Safe Bayesian Optimization using Interior-Point Methods - Applied to Personalized Insulin Dose Guidance. Proceedings of the 2022 IEEE Conference on Decision and Control, Cancun, Mexico. [pdf]
  • Krishnamoorthy, D. and Doyle III, F. J., 2022. Personalized Dose Guidance using Safe Bayesian Optimization. NeurIPS 2022 Workshop on Learning from Time Series for Health, New Orleans, USA. (Spotlight paper)
  • Krishnamoorthy, D. and Doyle III, F. J., 2022. Personalized Dose Guidance using Safe Bayesian Optimization. 2022 Machine Learning for Health (ML4H), New Orleans, USA. [pdf][video]
  • Dirza, R., Rizwan, M., Skogestad, S. and Krishnamoorthy, D., 2022. Real-Time Optimal Resource Allocation Using Online Primal Decomposition, IFAC-PapersOnLine Vol. 55 (21), p.31-36. [pdf]
  • Bernardino, L.F., Krishnamoorthy, D. and Skogestad, S., 2022. Optimal Operation of Heat Exchanger Networks with Changing Active Constraint Regions, Computer Aided Chemical Engineering, Vol. 49, p.421-426.
  • Dirza, R., Krishnamoorthy, D. and Skogestad, S., 2022. Primal-dual Feedback-optimizing Control with Direct Constraint Control, Computer Aided Chemical Engineering, Vol. 49, 1153-1158. [pdf]
  • Bernardino, L.F.,Krishnamoorthy, D. and Skogestad, S., 2022. Comparison of Simple Feedback Control Structures for Constrained Optimal Operation. IFAC-PapersOnLine, Vol. 55 (7), p.883-888.[pdf]
  • Krishnamoorthy, D., Mesbah, A., Paulson, J., 2021. An Adaptive Correction Scheme for Offset-Free Asymptotic Performance in Deep Learning-based Economic MPC. IFAC ADCHEM 2021, In-Press. [pdf][slides][video]
  • Dirza, R., Skogestad,S., Krishnamoorthy, D., 2021. Optimal Resource Allocation using Distributed Feedback Real-time Optimization. IFAC ADCHEM 2021. (Keynote paper) [pdf][slides][keynote talk]
  • Krishnamoorthy, D., Dimitri Boiroux, Tinna Björk Aradottir, Sarah Ellinor Engell and John Bagterp Jørgensen, 2021 A Model-free Approach to Automatic Dose Guidance in Long Acting Insulin Treatment of Type 2 Diabetes. Proceedings of the 2021 American Control Conference, In-Press. [pdf][slides]
  • Mdoe, Z., Krishnamoorthy, D., and Jäschke,J., 2020. Adaptive Horizon Multistage Nonlinear Model Predictive Control. Proceedings of the 2021 American Control Conference, In-Press. [pdf]
  • Prakash, S., Krishnamoorthy, D., and Jäschke,J., Multi-scenario Design Optimization using ADMM of a Thermal Energy Storage system. Computer aided chemical engineering (ESCAPE 31), In-press. [pdf]
  • Krishnamoorthy, D.,Valli, C. and Skogestad, S., 2020. Real-time Optimal Resource Allocation in an Industrial Symbiotic Network using Transient Measurements. Proceedings of the 2020 American Control Conference ,p. 3541-3546, Denver. [pdf][slides][video]
  • Krishnamoorthy, D. and Skogestad, S., 2020. Linear Combination of Gradients as Optimal Controlled Variables, Computer aided chemical engineering, (In-press). [pdf][slides]
  • Krishnamoorthy, D., Jäschke, J. and Skogestad, S., 2019. Multistage Model Predictive Control with Online Scenario Tree Update using Recursive Bayesian Weighting, Proceedings of the 2019 European Control Conference, Naples, Italy [pdf][Slides]
  • Krishnamoorthy, D., Ryu, J. and Skogestad, S., 2019. Dynamic extremum seeking control applied to a gas lifted well network, IFAC DYCOPS-CAB, Florianopolis, Brazil [pdf]
  • Delou, P., Azevedo, J., Krishnamoorthy, D., de Souza Jr, M. and Secchi, A., 2019. Model Predictive Control with Recon_guration Strategy applied to an Electric Submersible Pump in a subsea environment, IFAC DYCOPS-CAB, Florianopolis, Brazil [pdf]
  • Thombre, M., Krishnamoorthy, D., and Jäschke, J., 2019. Data-driven Multistage Model Predictive Control of a Thermal Storage System with Time-Varying Uncertainty, IFAC DYCOPS-CAB, Florianopolis, Brazil [pdf]
  • Krishnamoorthy, D. Jahanshahi, E. and Skogestad, S., 2019. A feedback Real time optimization strategy applied to an evaporator process, PSE Asia, Bangkok, Thailand (In-Press)
  • Krishnamoorthy, D., Foss, B. Suwartadi, E., Jäschke, J. and Skogestad, S., 2018. Improving Scenario Decomposition for Multistage MPC using a Sensitivity-based Path-following Algorithm, 57th IEEE Conference on Decision and Control, Miami beach, Forida. [pdf]
  • Krishnamoorthy, D., Foss, B. and Skogestad, S., 2018. A distributed algorithm for scenariobased model predictive control using primal decomposition (in-press), IFAC ADCHEM, Shenyang, China - Keynote paper and IFAC Young Author Award finalist [pdf]
  • Krishnamoorthy, D., Thombre, M., Jäschke, J. and Skogestad, S., 2018. Data-driven scenario selection for multistage robust model predictive control (in-press), IFAC NMPC, Madison, Wisconsin. [pdf]
  • Krishnamoorthy, D., Jahanshahi, E. and Skogestad, S., 2018. Gas-lift Optimization by Controlling Marginal Gas-Oil Ratio using Transient Measurements, IFAC-PapersOnLine, 51(8), pp.19-24 (IFAC OOGP, Esbjerg, Denmark) - IFAC-ABB Best Student Paper Award. [pdf]
  • Suwartadi, E.,Krishnamoorthy, D. and Jäschke, J., 2018. Fast Economic Model Predictive Control for a Gas Lifted Well Network, IFAC-PapersOnLine, 51(8), pp.25-30 (IFAC OOGP, Esbjerg, Denmark).[pdf]
  • Backi, C. J., Krishnamoorthy, D. and Skogestad, S., 2018. Slug handling with a virtual harp-based on nonlinear predictive control for a gravity separator, IFAC-PapersOnLine, 51(8), pp.120-125 (IFAC OOGP, Esbjerg, Denmark).[pdf]
  • Krishnamoorthy, D., Aguiar, M. A. M., Foss, B. and Skogestad, S., 2018. A Distributed Optimization Strategy for Large scale Oil and Gas Production Systems (in-Press), IEEE CCTA, Copenhagen, Denmark.[pdf]
  • Backi, C. J., Krishnamoorthy, D., Verheyleweghen, A. and Skogestad, S., 2018.Combined nonlinear moving horizon estimation and model predictive control applied to a compressor for active surge control (in-Press), IEEE CCTA, Copenhagen, Denmark.[pdf]
  • Bonnowitz, H., Straus, J., Krishnamoorthy, D., and Skogestad, S., 2018. Control of the Steady-State Gradient of an Ammonia Reactor using Transient Measurements, Computer aided chemical engineering, Vol.43, p.1111-1116 (ESCAPE 28, Graz)[pdf]
  • Reyes-Lúa, A., Zotica, C., Das, T., Krishnamoorthy, D., and Skogestad, S., 2018. Changing between Active Constraint Regions for Optimal Operation: Classical Advanced Control versus Model Predictive Control, Computer aided chemical engineering, Vol.43, p.1015-1020 (ESCAPE 28, Graz) - Keynote paper presented by S. Skogestad.[pdf]
  • Krishnamoorthy, D., Foss, B. and Skogestad, S., 2017. Gaslift optimization under uncertainty. Computer Aided Chemical Engineering, vol.40, pg 1753-1758 (ESCAPE 27, Barcelona).[pdf]
  • Krishnamoorthy, D., Pavlov, A. and Li, Q., 2016. Robust Extremum Seeking Control with application to Gas Lifted Oil Wells. IFAC-PapersOnLine, 49(13), pp.205-210.[pdf]
  • Krishnamoorthy, D., Bergheim, E.M., Pavlov, A., Fredriksen, M. and Fjalestad, K., 2016. Modelling and Robustness Analysis of Model Predictive Control for Electrical Submersible Pump Lifted Heavy Oil Wells. IFAC-PapersOnLine, 49(7), pp.544-549 (IFAC DYCOPS, Trondheim, Norway). [pdf]
  • Pavlov, A., Krishnamoorthy, D., Fjalestad, K., Aske, E. and Fredriksen, M., 2014, October. Modelling and model predictive control of oil wells with electric submersible pumps. IEEE Conference on Control Applications (pp. 586-592). (Antibes, France)

Patents

  • Krishnamoorthy, D., and Doyle III, F.J. 2022, System and Methods for Individualized Bolus Calculations. US serial no. 63/338,363.
  • Aske, E., Krishnamoorthy, D., Fjalestad, K., Pavlov, A. and Fredriksen, M. 2014, Well Control system (WO2015070913A1, CA2930653A1, US20160290077A1, GB2535090B - granted) [link]
  • Krishnamoorthy, D. and Fjalestad, K. 2017, Estimating flow rate at a pump (WO2017061873A1, CA3001234A1, GB2543048A,RU2737055C2 - granted) [link]

Theses

  • Krishnamoorthy, D., 2019. Novel Approaches to Online Process Optimization under Uncertainty, PhD Thesis, NTNU. [pdf] [slides]
  • Krishnamoorthy, D., 2012. Efficient Algorithm implementation of Model Predictive Control, MSc Thesis, Imperial College London. [pdf][slides]

Press, Media, and others

  • Article on Universitets Avisa (in Norwegian) interviewing me in connection to my PhD Excellence Award. [link]
  • Contribution to the Biography on Prof. Jens G. Balchen titled, Altid Rabiat by Gard Paulsen (In Norwegian) [link][link to book]
  • Report on Age-dependent Epidemiological model of COVID-19 to assist policy makers in Norway - Communicated to the Director-General of the Norwegian Institute of Public Health (NIPH) on 23 March 2020 (National Lockdown announed on 12 March 2020). [link]