Application of Iterative Nonlinear Model Predictive Control to a Batch Pilot Reactor

Cueli, J.R.; Bordons, Carlos

doi:10.3182/20050703-6-cz-1902.01586

Cited by 6 publications

(7 citation statements)

References 6 publications

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“…The matrix P M is selected as symmetric positive definite satisfying a LMI to be specified in the next section. In (7), the variables χ M j|k and xM j|k are the state and the output of the system Σ K,M −1 , respectively. Recall that the state output of Σ K,M −1 , xM j|k is intended to track the state of the true system.…”

Section: Robust Model Predictive Controlmentioning

confidence: 99%

“…A model with sufficiently strong predictive capabilities is, in general, complex, e.g., nonlinear or time varying, which in turn, leads to issues in the computation of MPC and of ILC signals. For industrial processes, such as the batch reactors, several studies have been conducted where a type of IL-MPC is implemented with a nonlinear model, such as [7] where an iterative nonlinear model predictive control strategy is proposed, or [8] which uses a fuzzy model that decides over local linear models.…”

Section: Introductionmentioning

confidence: 99%

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Iterative Learning and Model Predictive Control for Repetitive Nonlinear Systems via Koopman Operator Approximation

Saltik

Jayawardhana

Cherukuri

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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This paper presents an iterative way of computing a control algorithm with the aim of enabling reference tracking for an unknown nonlinear system. The method consists of three blocks: iterative learning control (ILC), robust model predictive control (MPC), and a linear approximation of the Koopman operator. The method proceeds in iterations, where at the end of an iteration, two steps are performed. First, the trajectories of the system obtained from previous iterations are used to build the linear approximation of the Koopman operator. Second, the linear model is used to compute the ILC signal. While these steps are executed in an offline manner, during an iteration, the control actions are computed online using the robust tubebased MPC. The tubes are defined by constraint tightening sets that compensate for the discrepancy between the true dynamics and its linear approximation. We demonstrate our method on the reference tracking for a 4 tank system.

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Section: Robust Model Predictive Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative Learning and Model Predictive Control for Repetitive Nonlinear Systems via Koopman Operator Approximation

Saltik

Jayawardhana

Cherukuri

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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“…Despite of all these advantages, the model used for prediction is still a limitation, also, the most of the processes in industry frequently show nonlinear behavior. − For nonlinear processes, such as the batch reactors, several studies have been conducted where a type of IL-MPC is implemented with nonlinear model. For example, refs and propose an iterative nonlinear model predictive control (INMPC) strategy. INMPC has been applied to two different batch reactions abd convergency, stability, and robustness of the closed-loop system have been analyzed and proven .…”

Section: Introductionmentioning

confidence: 99%

Model Learning Predictive Control for Batch Processes: A Reactive Batch Distillation Column Case Study

Ruíz

Loonen

Saltik

et al. 2019

Ind. Eng. Chem. Res.

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In this paper, we present the control of batch processes using model predictive control (MPC) and iterative learning control (ILC). Existing combinations of MPC and ILC are based on learning of the inputs of the process from previous batches for a fixed linear time-invariant model (LTI). However, batch processes are inherently time varying; therefore, LTI models are limited in capturing the relevant dynamic behavior. An attractive alternative is to use linear parameter varying (LPV) models because of their ability to capture nonlinearities in the control of batch processes. Therefore, in this work we propose a novel method combining MPC and ILC based on LPV models, and we call this method model learning predictive control (ML-MPC). Basically, the idea behind the method is to update the LPV model of the MPC iteratively, by using the repetitive behavior of the batch process. To this end, three different applicationdependent options to estimate the parameters and disturbances of the model are proposed and are compared in simulation on a nonlinear batch reactor. Finally, the ML-MPC with one of the estimation methods is applied to an industrial reactive batch distillation column (RBD) in simulation.

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“…Although, the development of ILC stemmed originally from the robotics area where repetitive motions show up naturally in many applications (Gasalino and Bartolini, 2002), (Craig, 1984), (Bondi et al, 1988), (Guglielmo and Sadegh, 1996), (Horowitz et al, 1991) and (Lange and Hirzinger, 1999); later on, this technique has shown significant ability to deal with other different systems such food processing plants, wafer steppers, hard disk servo controllers and chemical batch reactors (Cueli and Bordons, 2005). This is because its anticipatory character and ability to ensure the compensation for repetitive external disturbances by learning based on previous iterations without further modelling burdens.…”

Section: Introductionmentioning

confidence: 99%

Designing PI Controller Based on Iterative Learning Control using Adaptive Technique

Gomma

Thomas

2008

IFAC Proceedings Volumes

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Application of Iterative Nonlinear Model Predictive Control to a Batch Pilot Reactor

Cited by 6 publications

References 6 publications

Iterative Learning and Model Predictive Control for Repetitive Nonlinear Systems via Koopman Operator Approximation

Iterative Learning and Model Predictive Control for Repetitive Nonlinear Systems via Koopman Operator Approximation

Model Learning Predictive Control for Batch Processes: A Reactive Batch Distillation Column Case Study

Designing PI Controller Based on Iterative Learning Control using Adaptive Technique

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