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A hybrid intelligent control model is designed in which the linear feedback and active control signals are also used for achieving the control and synchronization, respectively.
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This paper describes the design and development of an intelligent hierarchical control model based on a proposed tool management method.
To develop an estimate of the stability of the modelled system in the absence of intelligent control, we simulated the model within each network using three unintelligent control methods: Null No control signals are used.
Crowd model of the moving target is a linear fusion intelligent time domain mapping control model.
The identified model of the AAC system can be used for further implementation in a model based intelligent control of an AAC system equipped with a variable speed compressor.
Its boundary conditions and homogenous case are also analyzed in detail and compared with other two models (i.e., Longitudinal Control Model and Intelligent Driver Model).
This paper presents an intelligent control scheme that uses a cerebellar model articulation controller (CMAC) and genetic algorithms (GA) in aircraft automatic landing control and to make automatic landing systems (ALS) more intelligent.
On the theoretical side, faculty and graduate students pursue research on adaptive and optimal control, digital control, robust control, modeling and identification, learning, intelligent control and nonlinear control, to name a few.
The area of hybrid dynamical systems is of particular importance to intelligent control and several approaches to modeling and control of such systems are briefly discussed, with emphasis on supervisory control approaches that are based on discrete abstractions.
There are many control methods, such as adaptive, feedback and intelligent control and they require a precise model.
The mechanism control matrix is established based on the calculation data of the mechanism models, and the intelligent control matrix is established based on the measured data on the rolling process.
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