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.

GA-Based Fuzzy Logic Control for a Smart Fin of a Projectile

2007 ◽  
Author(s):  
Venkat Mudupu ◽  
Mohamed B. Trabia ◽  
Woosoon Yim ◽  
Paul Weinacht
Keyword(s):  
Fuzzy Logic ◽  
System Identification ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Operating Conditions ◽  
Piezoelectric Bimorph ◽  
Logic Control ◽  
Identification Technique ◽  
Simulation Results ◽  
Design And Testing

This paper presents the design and testing of a smart fin for a subsonic projectile. The smart fin is activated using a piezoelectric bimorph with a substrate that is completely enclosed within the fin. A linear model of the actuator and fin system is created using the frequency response identification technique within MATLAB System Identification Toolbox. A procedure for designing a GA-based fuzzy logic controller for the fin is presented. Experimental and simulation results show that the proposed controller achieved the fin angle control under different operating conditions.

An Annular Pulsed Detonation Combustor Mockup: System Identification and Misfiring Detection

2015 ◽  
Vol 138 (4) ◽  
Author(s):  
Sascha Wolff ◽  
Rudibert King
Keyword(s):  
Kalman Filter ◽  
System Identification ◽  
Initial Conditions ◽  
Environmental Parameters ◽  
Modal Identification ◽  
Experimental Setup ◽  
Lower Performance ◽  
Model Based ◽  
Pulsed Detonation ◽  
Identification Technique

An annular pulsed detonation combustor (PDC) basically consists of a number of detonation tubes which are firing in a predetermined sequence into a common downstream annular plenum. Fluctuating initial conditions and fluctuating environmental parameters strongly affect the detonation. Operating such a setup without misfiring is delicate. Misfiring of individual combustion tubes will significantly lower performance or even stop the engine. Hence, an operation of such an engine requires a misfiring detection. Here, a model-based approach is used which exploits the innovation sequence calculated by a Kalman filter. The model necessary for the Kalman filter is determined based on a modal identification technique. A surrogate, nonreacting experimental setup is considered in order to develop and test these methods.

Simulation and System Identification of a Biomethanol Production Plant

2009 ◽  
Author(s):  
Jaime R. Garci´a ◽  
Iva´n D. Romero ◽  
Jose D. Posada ◽  
Antonio J. Bula ◽  
Marco E. Sanjua´n
Keyword(s):  
System Identification ◽  
Production Process ◽  
Black Liquor ◽  
Operating Conditions ◽  
Biodiesel Production ◽  
Production Plant ◽  
Biomass Waste ◽  
Identification Technique ◽  
Subspace System Identification ◽  
Final Amount

Biomethanol is simply methanol produced from non-fossil feedstock. The process requires gasification of biomass, black liquor, or the gas can be obtained from landfill or animal waste. Typically, Biodiesel production requires methanol as a main feedstock, and the latter can be obtained from biomass waste, turning the biodiesel production process even more sustainable, as the necessary materials involved come from renewable sources. This investigation addresses the need to develop a simulation model of a biomethanol production process, to determine the feasibility of the plant by analyzing different operating conditions at the process units. Also, a subspace system identification technique is used to capture the dynamics of the process at one operating condition for further optimization of operational costs. The results showed that H2/CO ratio significantly affects the final amount of biomethanol (CH3OH) obtained.

An Annular Pulsed Detonation Combustor Mockup: System Identification and Misfiring Detection

2015 ◽  
Author(s):  
Sascha Wolff ◽  
Rudibert King
Keyword(s):  
Kalman Filter ◽  
System Identification ◽  
Initial Conditions ◽  
Environmental Parameters ◽  
Modal Identification ◽  
Lower Performance ◽  
Model Based ◽  
Pulsed Detonation ◽  
Identification Technique ◽  
Set Up

An annular pulsed detonation combustor basically consists of a number of detonation tubes which are firing in a predetermined sequence into a common downstream annular plenum. Fluctuating initial conditions and fluctuating environmental parameters strongly affect the detonation. Operating such a set-up without misfiring is delicate. Misfiring of individual combustion tubes will significantly lower performance or even stop the engine. Hence, an operation of such an engine requires a misfiring detection. Here, a model-based approach is used which exploits the innovation sequence calculated by a Kalman filter. The model necessary for the Kalman filter is determined based on a modal identification technique. A surrogate, nonreacting experimental set-up is considered in order to develop and test these methods.

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