Performance assessment for iterative learning control of batch units

Chen, Junghui; Kong, Cho-Kai

doi:10.1016/j.jprocont.2009.01.006

Cited by 27 publications

(14 citation statements)

References 20 publications

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“…To derive a robust ILC updating law for the above closed-loop system, the following theorem is given below: Theorem 1: The closed-loop 2D system in (14) is guaranteed robustly stable with a H infinity control T T T T T t t T T T T t T T T k k T T …”

Section: Robust Ilc Designmentioning

confidence: 99%

“…To overcome the problem of model-plant mismatches from cycle to cycle, model prediction errors in the previous batch runs were suggested under screening to determine a model-based ILC algorithm in the current batch * This work is supported in part by the Alexander von Humboldt Research Fellowship of Germany and the National Nature Science Foundation of China under Grant 61074020. run, in order to guarantee tracking performance [11,12]. Using the partial-least-squares (PLS) models constructed over cycles, on-line adaptive ILC algorithms were simultaneously developed for practical implementation [13,14]. By comparison, a two-stage ILC implementation for improving disturbance rejection against unpredictable process dynamics was proposed in the reference [15].…”

Section: Introductionmentioning

confidence: 99%

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Extended robust iterative learning control design for industrial batch processes with uncertain perturbations

Liu

Shao

2012

Proceedings of the 10th World Congress on Intelligent Control and Automation

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For industrial batch processes subject to uncertain perturbations from cycle to cycle, a robust iterative learning control (ILC) scheme is proposed in this paper to realize robust tracking of the set-point profile for system operation. An important merit is that only measured output errors of current and previous cycles are used to design a synthetic ILC controller consisting of dynamic output feedback plus feedforward control, for the convenience of implementation. By introducing a slack variable matrix to construct a less comprehensive two-dimensional (2D) difference Lyapunov function that guarantees monotonical state energy decrease in both the time and batchwise directions, sufficient conditions are established in terms of linear matrix inequality (LMI) constraints for holding robust stability of the closed-loop ILC system. By solving these LMI constraints, the ILC controller is explicitly formulated, together with an adjustable robust H infinity performance level. An illustrative example of injection molding is given to demonstrate the effectiveness and merits of the proposed ILC design.

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Section: Robust Ilc Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extended robust iterative learning control design for industrial batch processes with uncertain perturbations

Liu

Shao

2012

Proceedings of the 10th World Congress on Intelligent Control and Automation

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“…Liu [16] proposed a closed-loop ILC method for batch processes with state delay and time-varying uncertainties to realize robust tracking and on-line optimization. Chen [17] presented an on-line adaptive ILC strategy to optimize tracking performance, based on estimating the minimum variance bounds of…”

Section: B Iterative Learning Controlmentioning

confidence: 99%

Optimization and Control of Batch Chemical Processes

Hongyan

Decheng

Wang

2011

2011 Fourth International Conference on Intelligent Computation Technology and Automation

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Batch chemical processes have become significant in chemical manufacturing. Recently, economy globalization has resulted in growing worldwide competitions in traditional chemical process industry. In order to increase competition, reduce production costs and meet safety requirements, it is necessary to implement varies optimization methods and advanced control strategies. The goal of this paper is to review the recent developments in this challenging area. Solution methods and classification for dynamic optimization are reviewed, measurement-based optimization methods and two kinds of advanced control strategies are analyzed and summarized. Finally, challenges and development trends in future research are presented.

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“…A quadratic criterion was presented to analyze the ILC convergence in terms of a MPC structure for time-varying linear systems [27]. The achievable tracking performance of an indirect-type ILC scheme was assessed by estimating the minimum output variance bound [28]. Combining with the feedback control design, a two-step ILC design [29] was proposed to adjust the process input for improving the output tracking performance against load disturbance and process uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Robust PID based indirect-type iterative learning control for batch processes with time-varying uncertainties

Liu

Wang

Chen

2014

Journal of Process Control

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Based on the proportional-integral-derivative (PID) control structure widely used in engineering applications, a robust indirect-type iterative learning control (ILC) method is proposed for industrial batch processes subject to time-varying uncertainties. An important merit is that the proposed ILC design is independent of the PID tuning that aims primarily to hold robust stability of the closed-loop system, owing to the fact that the ILC updating law is implemented through adjusting the setpoint of the closed-loop PID control structure plus a feedforward control to the plant input from batch to batch. According to the robust H infinity control objective, a robust discrete-time PID tuning algorithm is given in terms of the plant state-space model description to accommodate for time-varying process uncertainties. For the batchwise direction, a robust ILC updating law is developed based on the two-dimensional (2D) control system theory. Only measured output errors of current and previous cycles are used to implement the proposed ILC scheme for the convenience of practical application. An illustrative example from the literature is adopted to demonstrate the effectiveness and merits of the proposed ILC method.

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Performance assessment for iterative learning control of batch units

Cited by 27 publications

References 20 publications

Extended robust iterative learning control design for industrial batch processes with uncertain perturbations

Extended robust iterative learning control design for industrial batch processes with uncertain perturbations

Optimization and Control of Batch Chemical Processes

Robust PID based indirect-type iterative learning control for batch processes with time-varying uncertainties

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