Adaptive Identification Method of Vehicle Modeling According to the Fluctuation of Road and Running Situation in Autonomous Driving

2019 ◽  
Author(s):  
Yohei Yamauchi ◽  
Mitsuyuki Saito ◽  
Takahiko Ono
Keyword(s):  
Autonomous Driving ◽  
Adaptive Identification ◽  
Vehicle Modeling ◽  
Identification Method

Evaluation of aeration capacity of fermentors by adaptive identification method

1980 ◽  
Vol 45 (8) ◽  
pp. 2266-2271 ◽  
Author(s):  
Miroslav Sobotka ◽  
Jaroslav Votruba
Keyword(s):  
Time Dependence ◽  
Oxygen Electrode ◽  
New Method ◽  
Dynamic Experiment ◽  
Adaptive Identification ◽  
Identification Method ◽  
Column Type ◽  
Method Of Evaluation ◽  
Aeration Capacity

A new method of evaluation of the aeration capacity, kLa from data measured with the use of an oxygen electrode was proposed, based on the algorithm of adaptive identification. In this way, even the time dependence of kLa in the course of a dynamic experiment can be obtained. The results from mechanically stirred fermentors and apparatuses of the bubbled column type show that the commonly used assumption about the time independence of kLa does not hold for intensively aerated apparatuses.

scholarly journals An Adaptive Identification Method Based on the Modulating Functions Technique and Exact State Observers for Modeling and Simulation of a Nonlinear Miso Glass Melting Process

2019 ◽  
Vol 29 (4) ◽  
pp. 739-757 ◽  
Author(s):  
Witold Byrski ◽  
Michał Drapała ◽  
Jȩdrzej Byrski
Keyword(s):  
Glass Melting ◽  
Industrial Process ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Data ◽  
Adaptive Identification ◽  
Identification Method ◽  
State Observers ◽  
On Line ◽  
Modulating Functions

Abstract The paper presents new concepts of the identification method based on modulating functions and exact state observers with its application for identification of a real continuous-time industrial process. The method enables transformation of a system of differential equations into an algebraic one with the same parameters. Then, these parameters can be estimated using the least-squares approach. The main problem is the nonlinearity of the MISO process and its noticeable transport delays. It requires specific modifications to be introduced into the basic identification algorithm. The main goal of the method is to obtain on-line a temporary linear model of the process around the selected operating point, because fast methods for tuning PID controller parameters for such a model are well known. Hence, a special adaptive identification approach with a moving window is proposed, which involves using on-line registered input and output process data. An optimal identification method for a MISO model assuming decomposition to many inner SISO systems is presented. Additionally, a special version of the modulating functions method, in which both model parameters and unknown delays are identified, is tested on real data sets collected from a glass melting installation.

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