Parametric plant modeling using one-shot closed-loop transient response data

2014 ◽  
Author(s):  
Yoshihiro Matsui ◽  
Hideki Ayano ◽  
Kazushi Nakano
Keyword(s):  
Transient Response ◽  
Closed Loop ◽  
Plant Modeling ◽  
Response Data

PID Tuning for Unstable Systems Using Closed-Loop Transient Response Data

2013 ◽  
Vol 133 (10) ◽  
pp. 1936-1942
Author(s):  
Yoshihiro Matsui ◽  
Shunichi Akamatsu ◽  
Tomohiko Kimura ◽  
Kazushi Nakano
Keyword(s):  
Transient Response ◽  
Closed Loop ◽  
Unstable Systems ◽  
Pid Tuning ◽  
Response Data

Data-driven I-PD Gain Tuning using Closed-loop Step Response Data

2019 ◽  
Vol 139 (8) ◽  
pp. 882-888
Author(s):  
Shiro Masuda ◽  
Jongho Park ◽  
Yoshihiro Matsui
Keyword(s):  
Closed Loop ◽  
Data Driven ◽  
Step Response ◽  
Response Data

Mode-Based Controller Design for Hammerstein Models Using Closed-Loop Tranjent Response Data

2016 ◽  
Vol 136 (5) ◽  
pp. 625-632
Author(s):  
Yoshihiro Matsui ◽  
Hideki Ayano ◽  
Shiro Masuda ◽  
Kazushi Nakano
Keyword(s):  
Closed Loop ◽  
Controller Design ◽  
Response Data ◽  
Hammerstein Models

An Expert Controller Based on Transient Response Patterns

2011 ◽  
Vol 219-220 ◽  
pp. 3-7
Author(s):  
Ning Zhang ◽  
Rong Hua Liu
Keyword(s):  
Control System ◽  
Transient Response ◽  
Closed Loop ◽  
Performance Criterion ◽  
Response Patterns ◽  
Loop Control ◽  
Expert Control ◽  
Closed Loop Control System ◽  
Loop Control System ◽  
Control Decision

An expert control system based on transient response patterns and expert system techniques is proposed in this paper. Depending on the features of the closed-loop control system determines the control decision and adjusts the parameters of the controller. The proposed method requires minimal proper information about the controlled plant and, with the linear re-excitation learning method, the system is kept satisfying the performance criterion.

Estimation of response curves in closed-loop physiological control

1986 ◽  
Vol 61 (4) ◽  
pp. 1481-1491 ◽  
Author(s):  
C. S. Poon
Keyword(s):  
Least Squares ◽  
Closed Loop ◽  
Ordinary Least Squares ◽  
Repeated Measurements ◽  
System Structure ◽  
Paired Data ◽  
Response Curves ◽  
Response Data ◽  
Error Components ◽  
Response Slope

Several recent reports have addressed the problem of estimating the response slope from repeated measurements of paired data when both stimulus and response variables are subject to biological variability. These earlier approaches suffer from several drawbacks: useful information about the relationships between the error components in a closed-loop system is not fully utilized; the response intercept cannot be directly estimated; and the normalization procedure required in some methods may fail under certain circumstances. This paper proposes a new, general method of simultaneously estimating the response slope and intercept from corrupted stimulus-response data when the errors in both variables are specifically related by the system structure. A direct extension of the least-squares approach, this method [directed least squares (DLS)] reduces to ordinary least-squares methods when either of the measured variables is error free and to the reduced-major-axis (RMA) method of Kermack and Haldane (Biometrics 37: 30-41, 1950) when the magnitudes of the normalized errors are equal. The DLS estimators are scale invariant, statistically unbiased and always assume the minimum variance. With simple modifications, the method is also applicable to paired data. If, however, the relation between error components is uncertain, then the RMA method is optimal, i.e., having the least possible asymptotic bias and variance. These results are illustrated by using various types of closed-loop respiratory response data.

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