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Mixed PD-Type Iterative Learning Control Algorithm for a Class of Parabolic Singular Distributed Parameter Systems
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DOI：10.1109/ACCESS.2021.3050486.png)
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摘要
En 中文
In this paper, the iterative learning control (ILC) problem is investigated for a class of time-invariant parabolic singular distributed parameter systems. Initially, the singular distributed parameter systems is decomposed into infinite number of singular systems based on the separation principle. Meanwhile, the slow-fast subsystems are introduced via singular value decomposition method. Then, a novel mixed PD-type ILC algorithm with finite dimension is designed for the low dimensional slow part and the corresponding convergence conditions are manifested. With the proposed controller, the output error of high dimensional fast complement can satisfy the given value instead of neglecting the effect of high dimensional modes. Furthermore, under the aforesaid ILC law and the appropriate number of the low dimensional slow part, the resulting tracking error of parabolic singular distributed parameter systems can converge to any small tracking accuracy. Finally, simulation results on the distributed building automatic temperature system verify the convergence and effectiveness of the mixed PD-type ILC algorithm.
Keyword:
Distributed parameter systems
Convergence
Mathematical model
Iterative learning control
Eigenvalues and eigenfunctions
Differential equations
Matrix decomposition
Iterative learning control
singular distributed parameter systems
separation principle
low dimensional slow part
convergence analysis
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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