Iterative Learning Estimation with Lean Measurements

Gupta, Anish; Pravin, P S; Bhartiya, Sharad; Gudi, Ravindra D.

doi:10.1016/j.ifacol.2016.03.031

Cited by 2 publications

(2 citation statements)

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“… 29 Further, it has been shown that a combination of ILC with appropriate process knowledge and system identification techniques helps in multi-variable nonlinear tracking problem. 30 , 31 Along similar lines, a constrained batch-to-batch ILC that utilizes the previous knowledge of the process to obtain the updated control policy was proposed. 32 , 33 It is also shown that latent variable point-to-point iterative learning MPC (LV-PTP-ILMPC) shows faster convergence and better efficiency as compared to the PTP-ILC.…”

Section: Introductionmentioning

confidence: 99%

“…In related work, to capture inherently time-varying parameters and non-linearities, the linear parameter varying model has been used in a model learning MPC framework for the batch process . Further, it has been shown that a combination of ILC with appropriate process knowledge and system identification techniques helps in multi-variable nonlinear tracking problem. , Along similar lines, a constrained batch-to-batch ILC that utilizes the previous knowledge of the process to obtain the updated control policy was proposed. , It is also shown that latent variable point-to-point iterative learning MPC (LV-PTP-ILMPC) shows faster convergence and better efficiency as compared to the PTP-ILC . Tube-based ILMPC proved to show superior performance for nonlinear batch processes as compared to the ILMPC .…”

Section: Introductionmentioning

confidence: 99%

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Batch-to-Batch Adaptive Iterative Learning Control─Explicit Model Predictive Control Two-Tier Framework for the Control of Batch Transesterification Process

Gupta

Kodamana

et al. 2022

ACS Omega

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To harness energy security and reduce carbon emissions, humankind is trying to switch toward renewable energy resources. To this extent, fatty acid methyl esters, also known as biodiesel, are popularly used as a green fuel. Fatty acid methyl esters can be produced by a batch transesterification reaction between vegetable oil and alcohol. Being a batch process, fatty acid methyl esters production is beset with issues such as uncertainties and unsteady state behavior, and therefore, adequate process control measures are necessitated. In this study, we have proposed a novel two-tier framework for the control of the fatty acid methyl esters production process. The proposed approach combines the constrained batch-to-batch iterative learning control technique and explicit model predictive control to obtain the desired concentration of the fatty acid methyl esters. In particular, the batch-to-batch iterative learning control technique is used to generate reactor temperature set-points, which is further utilized to obtain an optimal coolant flow rate by solving a quadratic objective cost function, with the help of explicit model predictive control. Our simulation results indicate that the fatty acid methyl esters concentration trajectory converges to the desired batch trajectory within four batches for uncertainty in activation energy and six batches for uncertainty in both inlet concentration of triglyceride and in activation energy even in the presence of process disturbances. The proposed approach was compared to the heuristic-based approach and constraint iterative learning control approach to showcase its efficacy.

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