Bhushan Gopaluni
Bhushan Gopaluni
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Title
Cited by
Cited by
Year
Model predictive control in industry: Challenges and opportunities
MG Forbes, RS Patwardhan, H Hamadah, RB Gopaluni
IFAC-PapersOnLine 48 (8), 531-538, 2015
1642015
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
1382012
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
1152008
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
982016
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
982006
Deep reinforcement learning approaches for process control
SPK Spielberg, RB Gopaluni, PD Loewen
2017 6th international symposium on advanced control of industrial processes …, 2017
842017
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
742016
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
672010
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
642015
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
612012
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
592013
MPC relevant identification––tuning the noise model
RB Gopaluni, RS Patwardhan, SL Shah
Journal of Process Control 14 (6), 699-714, 2004
572004
Nonlinear system identification under missing observations: The case of unknown model structure
RB Gopaluni
Journal of Process Control 20 (3), 314-324, 2010
552010
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
522013
A comparison of simultaneous state and parameter estimation schemes for a continuous fermentor reactor
SB Chitralekha, J Prakash, H Raghavan, RB Gopaluni, SL Shah
Journal of Process Control 20 (8), 934-943, 2010
452010
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
392018
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
342019
The nature of data pre-filters in MPC relevant identification—open-and closed-loop issues
RB Gopaluni, RS Patwardhan, SL Shah
Automatica 39 (9), 1617-1626, 2003
312003
Developing a physiological model for type II diabetes mellitus
O Vahidi, KE Kwok, RB Gopaluni, L Sun
Biochemical Engineering Journal 55 (1), 7-16, 2011
302011
A moving horizon approach to input design for closed loop identification
RS Patwardhan, RB Goapluni
Journal of Process Control 24 (3), 188-202, 2014
292014
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Articles 1–20