| Model predictive control in industry: Challenges and opportunities MG Forbes, RS Patwardhan, H Hamadah, RB Gopaluni IFAC-PapersOnLine 48 (8), 531-538, 2015 | 313 | 2015 |
| Lionsimba: a matlab framework based on a finite volume model suitable for li-ion battery design, simulation, and control M Torchio, L Magni, RB Gopaluni, RD Braatz, DM Raimondo Journal of The Electrochemical Society 163 (7), A1192, 2016 | 292 | 2016 |
| Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation J Zhu, Y Wang, Y Huang, R Bhushan Gopaluni, Y Cao, M Heere, ... Nature communications 13 (1), 2261, 2022 | 274 | 2022 |
| Nonlinear Bayesian state estimation: A review of recent developments SC Patwardhan, S Narasimhan, P Jagadeesan, B Gopaluni, S L Shah Control Engineering Practice 20 (10), 933-953, 2012 | 205 | 2012 |
| Deep reinforcement learning approaches for process control SPK Spielberg, RB Gopaluni, PD Loewen 2017 6th international symposium on advanced control of industrial processes …, 2017 | 171 | 2017 |
| A particle filter approach to identification of nonlinear processes under missing observations RB Gopaluni The Canadian Journal of Chemical Engineering 86 (6), 1081-1092, 2008 | 133 | 2008 |
| Toward self‐driving processes: A deep reinforcement learning approach to control S Spielberg, A Tulsyan, NP Lawrence, PD Loewen, R Bhushan Gopaluni AIChE journal 65 (10), e16689, 2019 | 132 | 2019 |
| State-of-charge estimation in lithium-ion batteries: A particle filter approach A Tulsyan, Y Tsai, RB Gopaluni, RD Braatz Journal of Power Sources 331, 208-223, 2016 | 127 | 2016 |
| Identification of chemical processes with irregular output sampling H Raghavan, AK Tangirala, R Bhushan Gopaluni, SL Shah Control engineering practice 14 (5), 467-480, 2006 | 111 | 2006 |
| Real-time model predictive control for the optimal charging of a lithium-ion battery M Torchio, NA Wolff, DM Raimondo, L Magni, U Krewer, RB Gopaluni, ... 2015 American Control Conference (ACC), 4536-4541, 2015 | 93 | 2015 |
| Deep learning of complex batch process data and its application on quality prediction K Wang, RB Gopaluni, J Chen, Z Song IEEE Transactions on Industrial Informatics 16 (12), 7233-7242, 2018 | 87 | 2018 |
| Application of neural networks for optimal-setpoint design and MPC control in biological wastewater treatment M Sadeghassadi, CJB Macnab, B Gopaluni, D Westwick Computers & Chemical Engineering 115, 150-160, 2018 | 80 | 2018 |
| Energy optimization in a pulp and paper mill cogeneration facility DJ Marshman, T Chmelyk, MS Sidhu, RB Gopaluni, GA Dumont Applied Energy 87 (11), 3514-3525, 2010 | 80 | 2010 |
| A deep learning architecture for predictive control SSP Kumar, A Tulsyan, B Gopaluni, P Loewen IFAC-PapersOnLine 51 (18), 512-517, 2018 | 79 | 2018 |
| A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems TM Alabi, EI Aghimien, FD Agbajor, Z Yang, L Lu, AR Adeoye, B Gopaluni Renewable Energy 194, 822-849, 2022 | 77 | 2022 |
| Fault detection and isolation in stochastic non-linear state-space models using particle filters F Alrowaie, RB Gopaluni, KE Kwok Control Engineering Practice 20 (10), 1016-1032, 2012 | 77 | 2012 |
| Optimal control and state estimation of lithium-ion batteries using reformulated models B Suthar, V Ramadesigan, PWC Northrop, B Gopaluni, ... 2013 American Control Conference, 5350-5355, 2013 | 72 | 2013 |
| Deep reinforcement learning with shallow controllers: An experimental application to PID tuning NP Lawrence, MG Forbes, PD Loewen, DG McClement, JU Backström, ... Control Engineering Practice 121, 105046, 2022 | 68 | 2022 |
| Mpc relevant identification––tuning the noise model RB Gopaluni, RS Patwardhan, SL Shah Journal of Process Control 14 (6), 699-714, 2004 | 67 | 2004 |
| Design and application of a database-driven PID controller with data-driven updating algorithm S Wakitani, T Yamamoto, B Gopaluni Industrial & Engineering Chemistry Research 58 (26), 11419-11429, 2019 | 62 | 2019 |
