146 citations · 271 across the 7 of their papers we have counts for
7 papers
Machine learning for industrial sensing and control: A survey and practical perspective
Nathan P. Lawrence, Seshu Kumar Damarla, Jong Woo Kim +7
With the rise of deep learning, there has been renewed interest within the process industries to utilize data on large-scale nonlinear sensing and control problems. We identify key…
Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
R. Bhushan Gopaluni, Aditya Tulsyan, Benoit Chachuat +6
Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning.…
Deep Reinforcement Learning for Process Control: A Primer for Beginners
Steven Spielberg, Aditya Tulsyan, Nathan P. Lawrence +2
Advanced model-based controllers are well established in process industries. However, such controllers require regular maintenance to maintain acceptable performance. It is a commo…
Input design for Bayesian identification of non-linear state-space models
Aditya Tulsyan, Swanand R. Khare, Biao Huang +2
We propose an algorithm for designing optimal inputs for on-line Bayesian identification of stochastic non-linear state-space models. The proposed method relies on minimization of…
Error analysis in Bayesian identification of non-linear state-space models
Aditya Tulsyan, Biao Huang, R. Bhushan Gopaluni +1
In the last two decades, several methods based on sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) have been proposed for Bayesian identification of stochastic non-…
On-line Bayesian parameter estimation in general non-linear state-space models: A tutorial and new results
Aditya Tulsyan, Biao Huang, R. Bhushan Gopaluni +1
On-line estimation plays an important role in process control and monitoring. Obtaining a theoretical solution to the simultaneous state-parameter estimation problem for non-linear…
A particle filter approach to approximate posterior Cramér-Rao lower bound
Aditya Tulsyan, Biao Huang, R. Bhushan Gopaluni +1
The posterior Cramér-Rao lower bound (PCRLB) derived in Tichavský et al., 1998, provides a bound on the mean square error (MSE) obtained with any non-linear state filter. Computing…