Enabling Self-Managing Applications using Model-based Online Control Strategies

2006 ◽  
Author(s):  
V. Bhat ◽  
M. Parashar ◽  
Hua Liu ◽  
M. Khandekar ◽  
N. Kandasamy ◽  
...  
Keyword(s):  
Control Strategies ◽  
Online Control ◽  
Model Based

scholarly journals The Role of Trip Lengths Calibration in Model-Based Perimeter Control Strategies

2021 ◽  
pp. 1-11
Author(s):  
Sergio F. A. Batista ◽  
Deepak Ingole ◽  
Ludovic Leclercq ◽  
Monica Menendez
Keyword(s):  
Control Strategies ◽  
Model Based

scholarly journals Model-Based and Data-Driven HVAC Control Strategies for Residential Demand Response

2021 ◽  
pp. 1-1
Author(s):  
Xiao Kou ◽  
Yan Du ◽  
Fangxing Li ◽  
Hector Pulgar-Painemal ◽  
Helia Zandi ◽  
...  
Keyword(s):  
Demand Response ◽  
Control Strategies ◽  
Data Driven ◽  
Model Based ◽  
Hvac Control ◽  
Residential Demand

scholarly journals Estimation of Ice Cream Mixture Viscosity during Batch Crystallization in a Scraped Surface Heat Exchanger

Processes ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 167 ◽  
Author(s):  
Alejandro De la Cruz Martínez ◽  
Rosa E. Delgado Portales ◽  
Jaime D. Pérez Martínez ◽  
José E. González Ramírez ◽  
Alan D. Villalobos Lara ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Control Strategies ◽  
Coupled Model ◽  
Ice Cream ◽  
Surface Heat ◽  
Model Based ◽  
Scraped Surface Heat Exchanger ◽  
Surface Heat Exchanger ◽  
Ice Fraction

Ice cream viscosity is one of the properties that most changes during crystallization in scraped surface heat exchangers (SSHE), and its online measurement is not easy. Its estimation is necessary through variables that are easy to measure. The temperature and power of the stirring motor of the SSHE turn out to be this type of variable and are closely related to the viscosity. Therefore, a mathematical model based on these variables proved to be feasible. The development of this mathematical relationship involved the rheological study of the ice cream base, as well as the application of a method for its in situ melting in the rheometer as a function of the temperature, and the application of a mathematical model correlating the SSHE stirring power and the ice cream viscosity. The result was a coupled model based on both the temperature and stirring power of the SSHE, which allowed for online viscosity estimation with errors below 10% for crystallized systems with a 30% ice fraction at the exit of the SSHE. The model obtained is a first step in the search for control strategies for crystallization in SSHE.

A physics-based approach to flow control using system identification

2012 ◽  
Vol 702 ◽  
pp. 26-58 ◽  
Author(s):  
Aurelien Hervé ◽  
Denis Sipp ◽  
Peter J. Schmid ◽  
Manuel Samuelides
Keyword(s):  
System Identification ◽  
Control Strategies ◽  
A Priori ◽  
Operating Conditions ◽  
Noise Sources ◽  
Subsequent Performance ◽  
Model Based ◽  
Noise Characteristics ◽  
Identification Technique ◽  
Auto Regressive

AbstractControl of amplifier flows poses a great challenge, since the influence of environmental noise sources and measurement contamination is a crucial component in the design of models and the subsequent performance of the controller. A model-based approach that makes a priori assumptions on the noise characteristics often yields unsatisfactory results when the true noise environment is different from the assumed one. An alternative approach is proposed that consists of a data-based system-identification technique for modelling the flow; it avoids the model-based shortcomings by directly incorporating noise influences into an auto-regressive (ARMAX) design. This technique is applied to flow over a backward-facing step, a typical example of a noise-amplifier flow. Physical insight into the specifics of the flow is used to interpret and tailor the various terms of the auto-regressive model. The designed compensator shows an impressive performance as well as a remarkable robustness to increased noise levels and to off-design operating conditions. Owing to its reliance on only time-sequences of observable data, the proposed technique should be attractive in the design of control strategies directly from experimental data and should result in effective compensators that maintain performance in a realistic disturbance environment.

Model-Based Design and Hardware-in-the-Loop Simulation of Engine Lean Operation

2014 ◽  
Author(s):  
Pushkar Agashe ◽  
Yang Li ◽  
Bo Chen
Keyword(s):  
Gasoline Engine ◽  
Control Strategies ◽  
Electronic Control Unit ◽  
Control Unit ◽  
Simulation Models ◽  
Operating Conditions ◽  
Hardware In The Loop ◽  
Vehicle Performance ◽  
Model Based ◽  
Hil Simulation

This paper presents model-based design and hardware-in-the-loop (HIL) simulation of engine lean operation. The functionalities of the homogeneous combustion subsystem in engine Electronic Control Unit (ECU) in dSPACE Automotive Simulation Models (ASM) are first analyzed. To control the gasoline engine in lean operation without the drop of output torque, the combustion subsystem in engine ECU is modified by introducing two control loops, torque modifier and fuel multiplier. The performance of these two controllers is evaluated by HIL simulation using a dSPACE HIL simulator. The HIL simulation models, including vehicle plant model and softECUs in HIL simulator and engine lean control model in hardware engine ECU are modeled using model-based design. With HIL simulation, the designed engine control strategies can be immediately tested to evaluate the overall vehicle performance. The HIL simulation results show that the designed lean combustion control strategy can reduce fuel consumption and is able to meet the torque requirement at lean engine operating conditions.

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