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Iterative learning control algorithms combining feedback and difference for batch processes
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DOI：10.1016/j.ces.2024.121063.png)
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Abstract
En 中文
Two types of iterative learning controllers for batch processes are presented in this paper. The controllers not only contain a feedforward term, but also incorporate a feedback term and a difference term. First, vector lifting technology is employed to describe all the inputs or outputs of a single batch. Second, we utilize matrix inversion and exponentiation operations to obtain the sufficient condition for the monotonic convergence of the error norm, and give the upper bound of the error at any time of each iteration, which is not only limited by the control gains, but also related to the number of iterations and the time. Third, when choosing the control gains, the guidance is given according to the upper bound of the tracking error, and the suboptimal learning gains are yielded by applying the distribution of the matrix eigenvalues. Finally, four examples are applied to verify the effectiveness of the algorithm.
Keywords:
Iterative learning control
Feedback
Difference
Convergence
Discrete-time systems
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4.3
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