Neural Networks and Identification of Systems With Unobserved States

1993 ◽  
Vol 115 (1) ◽  
pp. 196-203 ◽  
Author(s):  
C. J. Goh ◽  
Lyle Noakes
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control System ◽  
Nonlinear Control ◽  
Control Systems ◽  
Output Feedback ◽  
Original System ◽  
Nonlinear Control Systems ◽  
Output Feedback Controller ◽  
Network Emulator

Consider a nonlinear control system, whose structure is not known (apart from the order of the system) and whose states are not observed. We observe the output of the system for a period of time using persistently exciting input, and use the observation to train a neural network emulator whose output approximates that of the original system. We point out that such an explicit dynamical relationship between the input and the output is useful for the purpose of construction of output feedback controller for nonlinear control systems. Specialization of the method to linear systems allows swift convergence and parameter identification in some cases.

scholarly journals Application of quasilinear and CGA models for designing significantly nonlinear control systems

2020 ◽  
Vol 224 ◽  
pp. 01015
Author(s):  
R Neydorf ◽  
A Gaiduk ◽  
N Kudinov ◽  
V Dolgov
Keyword(s):  
Control System ◽  
Nonlinear Control ◽  
Mathematical Models ◽  
Control Systems ◽  
Approximation Method ◽  
Numerical Data ◽  
Nonlinear Control Systems ◽  
Analytical Properties ◽  
Altitude Control

To create control systems, mathematical models of objects are required, which often have to be obtained experimentally. In this case, the numerical data of experiments on the identification of essentially nonlinear objects can be satisfactorily approximated only in certain areas, which leads to fragmentary models that are not analytical. In these cases, when only fragmentary models are adequate, it is proposed to apply the new Cut-Glue approximation method, which allows obtaining a model with analytical properties. The proposed approach to the unification of the Cut-Glue approximation method is demonstrated by solving the problem of synthesizing a nonlinear airship altitude control system and studying it.

A Systematic Approach for Developing Nonlinear Control Systems With Fuzzy Logic

2003 ◽  
Author(s):  
Dean B. Edwards ◽  
Joseph J. Feeley ◽  
Akira Okamoto
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Nonlinear Control ◽  
Control Systems ◽  
Fuzzy Logic Control ◽  
Linear Control ◽  
Superior Performance ◽  
Nonlinear Control Systems ◽  
System A ◽  
Logic Control

In this paper we present a method for systematically developing nonlinear control systems that provide superior performance to conventional linear control systems. The approach uses the results of linear analysis as a starting point for designing and optimizing a nonlinear control system. A linear equivalent fuzzy logic control system is constructed to give the same performance as the “best” linear control system. The fuzzy logic control system is subsequently modified to improve performance by making an optimal nonlinear system. The method is illustrated by designing a nonlinear fuzzy logic control system for a headbox used for papermaking. A discrete linear quadratic regulator (DLQR) is first designed for this system. A nonlinear fuzzy logic control system is subsequently developed from the DLQR controller. The performance of these two control systems is then compared.

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