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Repetitive process based indirect-type iterative learning control for batch processes with model uncertainty and input delay
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DOI：10.1016/j.jprocont.2023.103112.png)
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Abstract
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This paper develops an indirect iterative learning control scheme for batch processes with time-varying uncertainties, input delay, and disturbances. In this paper, a predictor based on a state observer is designed to estimate the future state and to compensate for the input delay. Then a feedback controller based on the estimated state and the set-point error is used to track the specified reference trajectory, where, of the options available, a robust H-infinity controller is designed in the presence of time-varying uncertainties and load disturbances. Then a proportional plus derivative type iterative learning control law is designed. An injection molding process model demonstrates the new method's effectiveness, and a comparison with a direct-type design is given.
Keywords:
Batch process
Iterative learning control
Input-delay
Repetitive process
Robust H-infinity control
Linear matrix inequality
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