Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/83945

Iterative Learning Fault-Tolerant Control for Networked Batch Processes with Multirate Sampling and Quantization Effects

Authors Gao, Ming HKUST affiliated (currently or previously).
Sheng, Li
Zhou, Donghua
Gao, Furong View this author's profile
Issue Date 2017
Source Industrial & Engineering Chemistry Research , v. 56, (9), March 2017, p. 2515-2525
Summary The fault-tolerant control problem is investigated for a class of networked batch processes with actuator faults and external disturbances. A two-dimensional Fornasini-Marchesini (2D-FM) system with multirate sampling and quantization effects is introduced to model the networked batch processes, which may reflect the reality more closely. The aim of this paper is to design a dynamic output feedback controller such that the closed-loop system can achieve fault tolerance with the effect of actuator faults and satisfy the H8 performance constraint for unknown external disturbances. By employing a combination of the Lyapunov stability analysis theory, lifting technique, and logarithmic quantization method, a networked iterative learning fault-tolerant control (NILFTC) scheme is first proposed, and some sufficient conditions are established for the existence of the desired dynamic output feedback controller. Finally, an example is exploited to illustrate the effectiveness of the developed method.
ISSN 0888-5885
Language English
Format Article
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