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Adaptive iterative learning control based on unfalsified strategy for Chylla-Haase reactor

Content Provider	IEEE Xplore Digital Library
Author	Wang, J. Wang, Y. Cao, L. Jin, Q.
Copyright Year	2014
Abstract	An adaptive iterative learning control based on unfalsified strategy is proposed to solve high precision temperature tracking of the Chylla-Haase reactor, in which iterative learning is the main control method and the unfalsified strategy is adapted to adjust the learning rate adaptively. It is encouraged that the unfalsified control strategy is extended from time domain to iterative domain, and the basic definition and mathematics description of unfalsified control in iterative domain are given. The proposed algorithm is a kind of data-driven method, which does not need an accurate system model. Process data are used to construct fictitious reference signal and switch function in order to handle different process conditions. In addition, the plant data are also used to build the iterative learning control law. Here the learning rate in a different error level is adjusted to ensure the convergent speed and stability, rather than keeping constant in traditional iterative learning control. Furthermore, two important problems in iterative learning control, i.e., the initial control law and convergence analysis, are discussed in detail. The initial input of first iteration is arranged according to a mechanism model, which can assure a good produce quality in the first iteration and a fast convergence speed of tracking error. The convergence condition is given which is obviously relaxed compared with the tradition iterative learning control. Simulation results show that the proposed control algorithm is effective for the Chylla-Haase problem with good performance in both convergent speed and stability.
Starting Page	347
Ending Page	360
Page Count	14
File Size	1285170
File Format	PDF
ISSN	23299266
Volume Number	1
Issue Number	4
Language	English
Publisher Date	2014-01-01
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Chinese Association of Automation
Subject Keyword	Inductors Polymers Heat transfer Cooling Process control Mathematical model Learning (artificial intelligence) Iterative methods Temperature measurement high precision temperature tracking Adaptive iterative learning control unfalsified strategy Chylla-Haase reactor
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Artificial Intelligence Control and Systems Engineering Information Systems

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