Iterative learning control for distributed parameter systems using sensor-actuator network

Patan, Maciej; Klimkowicz, Kamil; Patan, Krzysztof

doi:10.1109/icarcv50220.2020.9305367

Cited by 4 publications

(6 citation statements)

References 23 publications

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“…The ILC for hyperbolic PDEs in multidimensional case based on different type of spatial discretizations were developed in [8,22] but there are strictly dependent on spatial mesh resolution. Recently, the optimal reference tracking approach has been proposed by the authors based on the concept of distributed sensing and actuation [19,20] with a decentralized control update strategy.…”

Section: Introductionmentioning

confidence: 99%

“…The topic was discussed to some extent in [27], but without connection to control of DPS. This gap constitutes the main motivation for the study undertaken here in order to extend approach set forth in [20]. For that purpose, the suitable output criterion related to the variance of the controlled variable as measure of the prediction accuracy is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…The main contribution of this work is as follows: (1) the proposal of the appropriate criterion to quantify the prediction accuracy, (2) adaptation of the ingenious exchange algorithm for efficient approximation of the solution for sensor selection problem, (3) the extension of the ILC scheme developed in [20] to the prediction-based measurement strategy, (4) verification on the highly non-trivial three-dimensional example of friction welding of two aluminium plates.…”

Section: Introductionmentioning

confidence: 99%

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Optimal sensor selection for prediction-based iterative learning control of distributed parameter systems

Patan

Klimkowicz

Patan

2022

2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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The purpose of this study is to develop an effective computational scheme to solve the optimal tracking control problem for repeated trials in distributed-parameter system in the situation where quantity under control cannot be observed directly. In such situations, the reliability of model predictions becomes more important than the accuracy of model parameters, because the ultimate objective in model-based control is the prediction or forecast of the system states. Particularly, given a finite number of possible spatial locations at which sensors may reside, we select gaged sites so as maximize the prediction accuracy. For that purpose, an suitable output criterion is proposed as a measure of the prediction accuracy and the sensor selection problem formulated in terms of optimization task. To solve it, a specialized technique is adopted based on relaxation of the original discrete optimization problem which amounts to operating on the density of sensors in lieu of their individual positions. As a result, a simple and effective exchange algorithm is outlined to select the gaged sites. Then, the measurement schedule providing the most informative system observations is further incorporated into the adaptive control scheme based on iterative learning control technique for effective solution of underlying tracking control problem. The proposed approach is verified by numerical experiments on the model of the friction welding process of two aluminum plates.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal sensor selection for prediction-based iterative learning control of distributed parameter systems

Patan

Klimkowicz

Patan

2022

2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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“…However, these scarce approaches are related to one spatial dimension and are dedicated to a specific class of linear PDEs [12,15,16] or steady state [18]. Recently, an approach has been presented that allows for optimal tracking of the reference signal, using the so-called distributed sensing and actuation [20,27,28] with applications in elastic materials and heat transfer, provided that the process is inherently repeatable. Here, we extend this approach assuming a strict taskspecific constraints: identical initial conditions and finite execution time, yielding e.g.…”

Section: Introductionmentioning

confidence: 99%

“…The main contribution of the research is a development of an effective ILC scheme dedicated for realistic thermal process being the generalization of the approach reported in [28] toward simultaneous feedback and feedforward control. Also, the proper characterization of the design of iterative learning supporting control is discussed for various types of feedback controller.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning control for vacuum heat treatment process

Balik

Klimkowicz

Patan

2022

2022 26th International Conference on Methods and Models in Automation and Robotics (MMAR)

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Distributed parameter systems constitute an important class of modern industrial processes. However, in many practical applications the engineers still tend to adapt some classical control techniques developed for lumped systems totally neglecting the spatial dynamics of investigated process. In a view of increasing demands imposed on system accuracy and performance such conventional control algorithms simply become insufficient and there is a great necessity for novel identification and control methods taking into account both the temporal and spatial dynamics. This work reports a dedicated approach to control design for repetitive thermal process consisting of the extension of the existing feedback control scheme with intelligent data-driven component using the iterative learning control technique. Although this is a method which emerged in the context of time-invariant systems, it become adapted to more complex systems due to its flexibility and inherent robustness. The characterization of the resulting control scheme is discussed together with control design and implementation details. In order to compare the quality of the regulation, the approach is illustrated with simulation on the realistic model of wafer heating in industrial vacuum furnace.

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