scholarly journals Simulation of open-loop plasma vertical movement response in the Damavand tokamak using closed-loop subspace system identification

2016 ◽  
Vol 11 (02) ◽  
pp. P02006-P02006 ◽  
Author(s):  
N. Darestani Farahani ◽  
F. Abbasi Davani
Keyword(s):  
System Identification ◽  
Closed Loop ◽  
Vertical Movement ◽  
Open Loop ◽  
Movement Response ◽  
Damavand Tokamak ◽  
Subspace System Identification

scholarly journals Behavioural system identification of visual flight speed control in Drosophila melanogaster

2010 ◽  
Vol 8 (55) ◽  
pp. 171-185 ◽  
Author(s):  
Nicola Rohrseitz ◽  
Steven N. Fry
Keyword(s):  
System Identification ◽  
Flight Control ◽  
Closed Loop ◽  
Speed Control ◽  
Control Model ◽  
Open Loop ◽  
Flight Speed ◽  
Level Control ◽  
Wide Range ◽  
Sensory Motor

Behavioural control in many animals involves complex mechanisms with intricate sensory-motor feedback loops. Modelling allows functional aspects to be captured without relying on a description of the underlying complex, and often unknown, mechanisms. A wide range of engineering techniques are available for modelling, but their ability to describe time-continuous processes is rarely exploited to describe sensory-motor control mechanisms in biological systems. We performed a system identification of visual flight speed control in the fruitfly Drosophila , based on an extensive dataset of open-loop responses previously measured under free flight conditions. We identified a second-order under-damped control model with just six free parameters that well describes both the transient and steady-state characteristics of the open-loop data. We then used the identified control model to predict flight speed responses after a visual perturbation under closed-loop conditions and validated the model with behavioural measurements performed in free-flying flies under the same closed-loop conditions. Our system identification of the fruitfly's flight speed response uncovers the high-level control strategy of a fundamental flight control reflex without depending on assumptions about the underlying physiological mechanisms. The results are relevant for future investigations of the underlying neuromotor processing mechanisms, as well as for the design of biomimetic robots, such as micro-air vehicles.

Pseudo-random binary sequence closed-loop system identification error with integration control

2009 ◽  
Vol 223 (6) ◽  
pp. 877-884 ◽  
Author(s):  
Z Ren ◽  
G G Zhu
Keyword(s):  
System Identification ◽  
Closed Loop ◽  
Binary Sequence ◽  
Open Loop ◽  
Loop System ◽  
Loop Model ◽  
Closed Loop System ◽  
Identification Error ◽  
Closed Loop Identification ◽  
Integral Controller

This paper studies the closed-loop system identification (ID) error when a dynamic integral controller is used. Pseudo-random binary sequence (PRBS) q-Markov covariance equivalent realization (Cover) is used to identify the closed-loop model, and the open-loop model is obtained based upon the identified closed-loop model. Accurate open-loop models were obtained using PRBS q-Markov Cover system ID directly. For closed-loop system ID, accurate open-loop identified models were obtained with a proportional controller, but when a dynamic controller was used, low-frequency system ID error was found. This study suggests that extra caution is required when a dynamic integral controller is used for closed-loop system identification. The closed-loop identification framework also has significant effects on closed-loop identification error. Both first- and second-order examples are provided in this paper.

Measurements of the vacuum-plasma response in EXTRAP T2R using generic closed-loop subspace system identification

2012 ◽  
Vol 87 (12) ◽  
pp. 1926-1929 ◽  
Author(s):  
K. Erik J. Olofsson ◽  
Per R. Brunsell ◽  
James R. Drake
Keyword(s):  
System Identification ◽  
Closed Loop ◽  
Vacuum Plasma ◽  
Plasma Response ◽  
Subspace System Identification

Comparison of Open Loop and Closed Loop System Identification of a UAV

2018 ◽  
Author(s):  
Orkun Simsek ◽  
Ayse Ilden Bayrak ◽  
Sinem Karatoprak ◽  
Atilla Dogan
Keyword(s):  
System Identification ◽  
Closed Loop ◽  
Open Loop ◽  
Loop System ◽  
Closed Loop System

Closed-Loop System Identification Based on Data Correlation

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Hassene Jammoussi ◽  
Matthew Franchek ◽  
Karolos Grigoriadis ◽  
Martin Books
Keyword(s):  
System Identification ◽  
Instrumental Variables ◽  
Closed Loop ◽  
Linear Time ◽  
Open Loop ◽  
Loop System ◽  
Loop Model ◽  
Closed Loop System ◽  
Engine Operation ◽  
Single Input Single Output

A closed-loop system identification method is developed to estimate the parameters of a single input single output (SISO) linear time invariant system (LTI) operating within a feedback loop. The method uses the reference command in addition to the input–output data and establishes a correlation framework to structure the system. The correlation-based method is capable of delivering consistent estimates provided that the specific conditions on the signals are met. The method parallels the instrumental variables four step algorithm (IV4) and is comprised of three steps. First a model is estimated using cross correlation calculations between the reference input signal and the control and measured output signals. In the second step, a prefilter is identified to reduce estimation bias. In the final step, the prefilter, the instrumental variables and the measured signals are employed to estimate the final model. A consistency proof is provided for the proposed estimation process. The method is demonstrated on two examples. The first uses data collected from a diesel engine operation, and an open-loop model relating fueling to engine speed is sought. The identification process is complicated by the presence of nonmeasurable external torque disturbances and stochastic sensor noise. The second example uses data obtained from a time domain simulation of a closed-loop system where high levels of nonmeasured noise and disturbances were considered and a comparison with existing methods is made.

scholarly journals Spontaneous and Evoked Eye Movements in Polyphemus Pediculus (Cladocera: Crustacea): A Case of Open-Loop Tracking?

1987 ◽  
Vol 131 (1) ◽  
pp. 323-336
Author(s):  
STEPHEN YOUNG ◽  
VICTORIA A. TAYLOR
Keyword(s):  
Eye Movements ◽  
Closed Loop ◽  
Open Loop ◽  
Small Object ◽  
Average Amplitude ◽  
Movement Response ◽  
Loop Control ◽  
Closed Loop Control System ◽  
Loop Control System ◽  
Moving Stimulus

1. Polyphemus eye movements were recorded in both pitching and yawing planes, both in a static visual environment and with a sinusoidally moving stimulus. 2. Spontaneous eye movements (average amplitude 1.7°) had different properties in the two planes, with trembling movements predominating in the pitching plane. A contour-sharpening function is proposed for these movements. 3. An attempt to analyse the eye movement response system using a Bode diagram shows a very poor fit to the data, leading to the conclusion that a closed-loop control system is an inappropriate model in this case. 4. The evoked eye movements are most convincingly represented by a model in which the time the stimulus takes to traverse a restricted sensitive zone in the central region of the eye controls the duration of a subsequent constant angular velocity saccade. The direction of the response movement follows the direction of the stimulus. A small-object tracking function is proposed for these movements.

Integrated System Identification and Control Design for an IC Engine Variable Valve Timing System

2010 ◽  
Author(s):  
Zhen Ren ◽  
Guoming G. Zhu
Keyword(s):  
System Identification ◽  
Closed Loop ◽  
Test Bench ◽  
Open Loop ◽  
Integrated System ◽  
Variable Valve Timing ◽  
Valve Timing ◽  
Closed Loop System ◽  
Variable Valve ◽  
And Control

This paper applies integrated system modeling and control design process to a continuously variable valve timing (VVT) actuator system that has different control input and cam position feedback sample rates. Due to high cam shaft torque disturbance and high actuator open-loop gain, it is fairly difficult to maintain the cam phase at the desired constant level with an open-loop controller. As a result, multirate closed-loop system identification is a necessity. For this study, multirate closed-loop system identification, PRBS q-Markov Cover, was used for obtaining linearized system models at different engine operational conditions; and the output covariance constraint (OCC) controller, an H2 controller, was designed based upon the identified model and evaluated on the VVT test bench. Performances of the designed OCC controller was compared with those of the baseline PI controller on the test bench. Results show that the OCC controller uses less control effort and has less overshoot than those of PI ones.

Algorithm for Closed Loop System Identification Based on Data Correlation

2008 ◽  
Author(s):  
H. Jammoussi ◽  
M. A. Franchek ◽  
K. Grigoriadis ◽  
M. Books
Keyword(s):  
System Identification ◽  
Instrumental Variables ◽  
Closed Loop ◽  
Open Loop ◽  
Loop System ◽  
Loop Model ◽  
Closed Loop System ◽  
Final Model ◽  
Cross Covariance ◽  
Step Algorithm

A closed loop system identification method is developed in which estimation bias from sensor noise and external disturbances is minimized. The method, based on the instrumental variables four step algorithm (IV4), uses three steps. The first step estimates a model using cross covariance calculations between the reference input signal and the control and measured output signals. The second step employs the prefilter identification process from the IV4 process. The third and final step uses the prefilter, the instrumental variables and the reference, control and output signals to estimate the final model. The method is demonstrated on a diesel engine where an open loop model relating fueling to engine speed is sought. The identification example is complicated by the presence of nonmeasurable external torque disturbances due to vehicle accessories.

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