Bhushan Gopaluni
Bhushan Gopaluni
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Cited by
Cited by
Model predictive control in industry: Challenges and opportunities
MG Forbes, RS Patwardhan, H Hamadah, RB Gopaluni
IFAC-PapersOnLine 48 (8), 531-538, 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
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
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
Deep reinforcement learning approaches for process control
SPK Spielberg, RB Gopaluni, PD Loewen
2017 6th international symposium on advanced control of industrial processes …, 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
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
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
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
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
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
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
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
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
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
A deep learning architecture for predictive control
SSP Kumar, A Tulsyan, B Gopaluni, P Loewen
IFAC-PapersOnLine 51 (18), 512-517, 2018
MPC relevant identification––tuning the noise model
RB Gopaluni, RS Patwardhan, SL Shah
Journal of Process Control 14 (6), 699-714, 2004
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
Nonlinear system identification under missing observations: The case of unknown model structure
RB Gopaluni
Journal of Process Control 20 (3), 314-324, 2010
On simultaneous on-line state and parameter estimation in non-linear state-space models
A Tulsyan, B Huang, RB Gopaluni, JF Forbes
Journal of Process Control 23 (4), 516-526, 2013
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