44 citations · 57 across the 9 of their papers we have counts for
9 papers
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.…
A comparative study of model approximation methods applied to economic MPC
Zhiyinan Huang, Qinyao Liu, Jinfeng Liu +1
Economic model predictive control (EMPC) has attracted significant attention in recent years and is recognized as a promising advanced process control method for the next generatio…
Behavioural Approach to Distributed Control of Interconnected Systems
Yitao Yan, Jie Bao, Biao Huang
This paper formulates a framework for the analysis and distributed control of interconnected systems from the behavioural perspective. The discussions are carried out from the view…
A consensus-based approach for parameter and state estimation of agro-hydrological system
Xunyuan Yin, Song Bo, Jinfeng Liu +1
The development of advanced closed-loop irrigation systems requires accurate soil moisture information. In this work, we address the problem of soil moisture estimation for the agr…
Distributed dynamic modeling and monitoring for large-scale industrial processes under closed-loop control
Wenqing Li, Chunhui Zhao, Biao Huang
For large-scale industrial processes under closed-loop control, process dynamics directly resulting from control action are typical characteristics and may show different behaviors…
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…