Iterative Learning Control with Input Shift

Welz, C.; Srinivasan, Bala; Bonvin, Dominique

doi:10.1016/s1474-6670(17)31851-7

Cited by 4 publications

(3 citation statements)

References 20 publications

(32 reference statements)

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“…Hence, the idea is to allow non-zero tracking errors early in the run and have the errors decrease progressively with run time t, which can be achieved with an input shift. This is documented next as part of an ILC implementation that includes (i) input shift, (ii) shift of the previous-cycle errors, and (iii) current-cycle feedback 49 .…”

Section: Ilc With Improved Performancementioning

confidence: 99%

Control and Optimization of Batch Chemical Processes

Bonvin

François

2017

Coulson and Richardson's Chemical Engineering

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A batch process is characterized by the repetition of time-varying operations of finite duration. Due to the repetition, there are two independent "time" variables, namely, the run time during a batch and the batch index. Accordingly, the control and optimization objectives can be defined for a given batch or over several batches. This chapter describes the various control and optimization strategies available for the operation of batch processes. These include online and run-to-run control on the one hand, and repeated numerical optimization and optimizing control on the other. Several case studies are presented to illustrate the various approaches Keywords Batch control, predictive control, iterative learning control, run-to-run control, batch process optimization, dynamic optimization, optimizing control, run-to-run optimization.

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Section: Ilc With Improved Performancementioning

confidence: 99%

Control and Optimization of Batch Chemical Processes

Bonvin

François

2017

Coulson and Richardson's Chemical Engineering

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“…The second equation allows adapting the feedforward term for the jacket temperature setpoint on a run-to-run basis based on ILC with input shift. In Theorem 3, the integral squared output error T 0 e 2 k (τ ) dτ is used as the Lyapunov function in run index k. The value of the input shift is tuned for convergence (Welz et al 2004). Due to the presence of the shift, the error does not converge asymptotically to zero.…”

Section: Illustrative Examplementioning

confidence: 99%

Stability and Controllability of Batch Processes

Srinivasan

Bonvin

2006

IFAC Proceedings Volumes

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Improving the performance of batch processes requires tools that are tailored to the specificities of batch operations. These include a mathematical representation that explicitly shows the two independent time variables (the run time t and the run index k) as well as the two types of outputs (the run-time and run-end outputs). Furthermore, corrective action can be taken via both on-line and run-to-run control. This paper investigates the important notions of stability and controllability for batch processes, where it is shown that a value rather than a yes-no answer needs to be considered. The tools required for evaluating these properties are readily adapted from the literature. Finally, the various control strategies are illustrated via the simulation of a semi-batch reactor, and references are made to the appropriate tools for evaluating stability and controllability.

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“…ILC was used to overcome model error, while MPC was employed to control the process in real time. The contributions in this area include those of Lee et al for the minimization of reaction time, Bonne and Jørgensen for the trajectory tracking of a fed-batch fermentation reactor, and Welz et al for the trajectory control of a batch distillation system. However, in most of these approaches (including previous terminal ILC algorithms and nonsquare systems), a trajectory segmentation or vector parametrization approach was needed to characterize the MVTs.…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning Control for Final Batch Product Quality Using Partial Least Squares Models

Flores‐Cerrillo

MacGregor

2005

Ind. Eng. Chem. Res.

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A terminal iterative learning control (ILC) strategy for batch-to-batch and within-batch control of final product properties, based on empirical partial least squares (PLS) models, is presented. The strategy rejects persistent process disturbances and achieves new final product quality targets using an iterative procedure that works in the reduced space of a latent variable model rather than in the high dimensional space of the manipulated variable trajectories. Complete manipulated variable trajectory reconstruction is then achieved by exploiting the PLS model of the process. The approach is illustrated with a condensation polymerization example for the production of nylon.

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Iterative Learning Control with Input Shift

Cited by 4 publications

References 20 publications

Control and Optimization of Batch Chemical Processes

Control and Optimization of Batch Chemical Processes

Stability and Controllability of Batch Processes

Iterative Learning Control for Final Batch Product Quality Using Partial Least Squares Models

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