On-line process-model-based control of a plasma etcher

1992 ◽  
Author(s):  
Michael E. Parten ◽  
R. R. Rhinehart ◽  
Vikram Singh
Keyword(s):  
Process Model ◽  
Model Based Control ◽  
Model Based ◽  
On Line ◽  
Line Process

In-line process-model-based control of wastewater pH using dual base injection

1990 ◽  
Vol 29 (7) ◽  
pp. 1254-1259 ◽  
Author(s):  
Gaylon L. Williams ◽  
R. Russell Rhinehart ◽  
James B. Riggs
Keyword(s):  
Process Model ◽  
Model Based Control ◽  
Model Based ◽  
Line Process ◽  
Dual Base

scholarly journals Hand interception of occluded motion in humans: a test of model-based vs. on-line control

2015 ◽  
Vol 114 (3) ◽  
pp. 1577-1592 ◽  
Author(s):  
Barbara La Scaleia ◽  
Myrka Zago ◽  
Francesco Lacquaniti
Keyword(s):  
Visual Information ◽  
Target Motion ◽  
Physical Models ◽  
Model Based Control ◽  
Projectile Motion ◽  
Starting Position ◽  
Model Based ◽  
Model Free ◽  
Ball Velocity ◽  
On Line

Two control schemes have been hypothesized for the manual interception of fast visual targets. In the model-free on-line control, extrapolation of target motion is based on continuous visual information, without resorting to physical models. In the model-based control, instead, a prior model of target motion predicts the future spatiotemporal trajectory. To distinguish between the two hypotheses in the case of projectile motion, we asked participants to hit a ball that rolled down an incline at 0.2 g and then fell in air at 1 g along a parabola. By varying starting position, ball velocity and trajectory differed between trials. Motion on the incline was always visible, whereas parabolic motion was either visible or occluded. We found that participants were equally successful at hitting the falling ball in both visible and occluded conditions. Moreover, in different trials the intersection points were distributed along the parabolic trajectories of the ball, indicating that subjects were able to extrapolate an extended segment of the target trajectory. Remarkably, this trend was observed even at the very first repetition of movements. These results are consistent with the hypothesis of model-based control, but not with on-line control. Indeed, ball path and speed during the occlusion could not be extrapolated solely from the kinematic information obtained during the preceding visible phase. The only way to extrapolate ball motion correctly during the occlusion was to assume that the ball would fall under gravity and air drag when hidden from view. Such an assumption had to be derived from prior experience.

Building Energy Consumption On-Line Forecasting Using System Identification and Data Fusion

2014 ◽  
Author(s):  
Xiwang Li ◽  
Jin Wen
Keyword(s):  
System Identification ◽  
Data Fusion ◽  
Building Energy ◽  
Integrated System ◽  
System Structure ◽  
Estimation Accuracy ◽  
Energy Estimation ◽  
Model Based Control ◽  
Model Based ◽  
On Line

Model based control has been proven to have significant building energy saving potentials through operation optimization. Accurate and computationally efficient, and cost-effective building energy model are essential for model based control. Existing studies in this area have mostly been focusing on reducing computation burden using simplified physics based modeling approach. However, creating and identification the simplified physics based model is often challenging and requires significant engineering efforts. Therefore, this study proposes a novel methodology to develop building energy estimation models for on-line building control and optimization using an integrated system identification and data fusion approach. System identification model has been developed based on frequency domain spectral density analysis. Eigensystem realization algorithm is used to generate the state space model from the Markov parameters. Kalman filter based data fusion technique has also been implemented to improve the accuracy and robustness of the model by incorporating with real measurements. A systematic analysis of system structure, system excitation selection as well as data fusion implementation is also demonstrated. The developed strategies are evaluated using a simulated testing building (simulated in EnergyPlus environment). The overall building energy estimation accuracy from this proposed model can reach to above 95% within 2 minutes calculation time, when compared against detailed physics based simulation results from the EnergyPlus model.

A model-based approach to on-line process disturbance management: The application

1990 ◽  
Vol 29 (2) ◽  
pp. 185-239 ◽  
Author(s):  
Inn S. Kim ◽  
Mohammad Modarres ◽  
R.Nial M. Hunt
Keyword(s):  
Model Based ◽  
On Line ◽  
Line Process ◽  
Disturbance Management

Distributed Network System for Real-Time Model Based Control of Industrial Gas Turbines

2011 ◽  
Author(s):  
V. Panov
Keyword(s):  
Control System ◽  
Real Time ◽  
Gas Turbine ◽  
Distributed Network ◽  
Hardware In The Loop ◽  
Time Model ◽  
Model Based Control ◽  
Model Based ◽  
On Line ◽  
Dedicated Hardware

This paper describes the development of a distributed network system for real-time model based control of industrial gas turbine engines. Distributed control systems contribute toward improvements in performance, testability, control system maintainability and overall life-cycle cost. The goal of this programme was to offer a modular platform for improved model based control system. Hence, another important aspect of this programme was real-time implementation of non-linear aero-thermal gas turbine models on a dedicated hardware platform. Two typical applications of real-time engine models, namely hardware-in-the-loop simulations and on-line co-simulations, have been considered in this programme. Hardware-in-the-loop platform has been proposed as a transitional architecture, which should lead towards a fully distributed on-line model based control system. Distributed control system architecture offers the possibility of integrating a real-time on-line engine model embedded within a dedicated hardware platform. Real-time executing models use engine operating conditions to generate expected values for measured and non-measured engine parameters. These virtual measurements can be used for the development of model based control methods, which can contribute towards improvements in engine stability, performance and life management. As an illustration of model based control concept, the example of gas turbine transient over-temperature protection is presented in this study.

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