A Dual Phase Modular Fuzzy Control Structure for an Automode Wheelchair in Ascending and Descending Stairs

A control structure, integrated with the virtue of high approximate capability of neural network, strong adaptability of gray prediction and strong robustness of fuzzy control, is proposed in this paper, in which the time-delay identify, varying step response predict and fuzzy control are achieved at one time. The results of computer simulation indicate that the control scheme can dramatically satisfy the desire of fast dynamic response and stability．

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The Study of Fuzzy Control Algorithm in a Duct Active Noise Control System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.397-400.1337 ◽

2013 ◽

Vol 397-400 ◽

pp. 1337-1340

Author(s):

Xian Jie Meng ◽

Juan Wang ◽

Yu Cheng Wang

Keyword(s):

Fuzzy Control ◽

Control Algorithm ◽

Noise Control ◽

Control Structure ◽

Feedforward Control ◽

Active Noise Control ◽

Good Convergence ◽

System A ◽

Active Noise ◽

Fuzzy Control Algorithm

In this paper, the application of fuzzy control algorithm in duct active noise control technology was studied, according to the problem of secondary acoustical feedback existing in feedforward control structure (FCS) adaptive active noise control (AANC) system, a reverse control structure of fuzzy active noise control (FANC) system was established. Through Matlab software the fuzzy control algorithm was simulated, and the simulation results showed that it has a good convergence and stability in the FANC system.

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A ROBUST INTELLIGENT PID-TYPE FUZZY CONTROL STRUCTURE FOR PRESSURE CONTROL

Chemical Engineering Communications ◽

10.1080/00986440802359329 ◽

2008 ◽

Vol 196 (3) ◽

pp. 291-304 ◽

Cited By ~ 1

Author(s):

N. Kanagaraj ◽

R. Kumar ◽

P. Sivashanmugam

Keyword(s):

Fuzzy Control ◽

Control Structure ◽

Pressure Control ◽

Intelligent Pid

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Self-Evolving Fuzzy Controller Composed of Univariate Fuzzy Control Rules

Applied Sciences ◽

10.3390/app10175836 ◽

2020 ◽

Vol 10 (17) ◽

pp. 5836

Author(s):

Jérôme Mendes ◽

Ricardo Maia ◽

Rui Araújo ◽

Francisco A. A. Souza

Keyword(s):

Fuzzy Control ◽

Control Structure ◽

Fuzzy Controller ◽

Inverse Function ◽

Time Varying ◽

Fuzzy Logic Controllers ◽

Control Rules ◽

Predefined Criterion ◽

Continuously Stirred Tank Reactor ◽

Human Operators

The paper proposes a methodology to online self-evolve direct fuzzy logic controllers (FLCs), to deal with unknown and time-varying dynamics. The proposed methodology self-designs the controller, where fuzzy control rules can be added or removed considering a predefined criterion. The proposed methodology aims to reach a control structure easily interpretable by human operators. The FLC is defined by univariate fuzzy control rules, where each input variable is represented by a set of fuzzy control rules, improving the interpretability ability of the learned controller. The proposed self-evolving methodology, when the process is under control (online stage), adds fuzzy control rules on the current FLC using a criterion based on the incremental estimated control error obtained using the system’s inverse function and deletes fuzzy control rules using a criterion that defines “less active” and “less informative” control rules. From the results on a nonlinear continuously stirred tank reactor (CSTR) plant, the proposed methodology shows the capability to online self-design the FLC by adding and removing fuzzy control rules in order to successfully control the CSTR plant.

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