A New Robust Fuzzy Controller for Nonlinear and Large Dead Time Systems

The importance of improving product quality at continuous hot-dip galvanizing lines with air knives steadily grows. So the developed solutions have to be intelligent, adaptive and modular. This paper describes the revision of a conventional non-adaptive control strategy towards a modern solution using methods of computational intelligence. The already existing feedforward control is complemented by a neural process model and a neuro-fuzzy controller replaces the previously used conventional process controller. Both components are embedded carefully into the control environment so that consumption of time and material for the installation period can be held low. The neural process model is optional and is used for model-based control so that the process inherent measurement dead-time is avoided. The new control arrangement is adaptive, saves zinc, guarantees a more constant coating and relieves the operators.

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H∞ control of dead-time systems based on a transformation

Automatica ◽

10.1016/s0005-1098(02)00225-x ◽

2003 ◽

Vol 39 (2) ◽

pp. 361-366 ◽

Cited By ~ 23

Author(s):

Qing-Chang Zhong

Keyword(s):

Dead Time ◽

Time Systems

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Feedback Control of Dead-Time Systems

Control of Biological and Drug-Delivery Systems for Chemical, Biomedical, and Pharmaceutical Engineering ◽

10.1002/9781118396490.ch13 ◽

2013 ◽

pp. 293-309

Keyword(s):

Feedback Control ◽

Dead Time ◽

Time Systems

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Analytical Tuning Method of MPC Controllers for MIMO First-Order Plus Fractional Dead Time Systems

Processes ◽

10.3390/pr8020212 ◽

2020 ◽

Vol 8 (2) ◽

pp. 212

Author(s):

Ning He ◽

Yichun Jiang ◽

Lile He

Keyword(s):

Predictive Control ◽

Closed Loop ◽

Dead Time ◽

Pole Placement ◽

Closed Loop System ◽

Placement Problem ◽

First Order ◽

Tuning Method ◽

Domain Performance ◽

Time Systems

An analytical model predictive control (MPC) tuning method for multivariable first-order plus fractional dead time systems is presented in this paper. First, the decoupling condition of the closed-loop system is derived, based on which the considered multivariable MPC tuning problem is simplified to a pole placement problem. Given such a simplification, an analytical tuning method guaranteeing the closed-loop stability as well as pre-specified time-domain performance is developed. Finally, simulation examples are provided to show the effectiveness of the proposed method.

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