144 research outputs found
Probing Control : Analysis and Design with Application to Fed-Batch Bioreactors
In most control problems the objective is to control the output at a desired value in spite of disturbances. In some cases, the best setpoint is not known a priori and it should be found online to optimize the process performance. This thesis examines a probing strategy that can be applied for this class of problems. The focus is on the application of the technique to the control of feed supply in fed-batch fermentations of the bacterium Escherichia coli. The thesis is divided into three parts. In the first part, the convergence properties of the probing algorithm are examined. The analysis is limited to processes modeled by a linear time-invariant dynamic in series with a static nonlinearity. Stability and performance analysis taking into account the process dynamic is performed. Tuning guidelines that help the user for the design are also derived. The second part presents a novel cultivation technique based on the probing approach. The fermentation technique combines the advantages of probing control and temperature-limited fed-batch technique. The feeding strategy is well adapted for prolonged operation at the maximum oxygen transfer capacity of the reactor. The efficiency of the method is demonstrated by simulations and experimental results. The strategy leads to a high biomass and it limits the degradation of the recombinant protein activity in the late production phase. In the third part, the probing feeding strategy is evaluated in industrial-scale bioreactors. Based on experimental results the influence of scale and complex medium is discussed. It is shown that the flexibility and robustness of the technique makes it a useful tool for process development
Data-Driven Nonlinear Control Designs for Constrained Systems
Systems with nonlinear dynamics are theoretically constrained to the realm of nonlinear analysis and design, while explicit constraints are expressed as equalities or inequalities of state, input, and output vectors of differential equations. Few control designs exist for systems with such explicit constraints, and no generalized solution has been provided. This dissertation presents general techniques to design stabilizing controls for a specific class of nonlinear systems with constraints on input and output, and verifies that such designs are straightforward to implement in selected applications. Additionally, a closed-form technique for an open-loop problem with unsolvable dynamic equations is developed. Typical optimal control methods cannot be readily applied to nonlinear systems without heavy modification. However, by embedding a novel control framework based on barrier functions and feedback linearization, well-established optimal control techniques become applicable when constraints are imposed by the design in real-time. Applications in power systems and aircraft control often have safety, performance, and hardware restrictions that are combinations of input and output constraints, while cryogenic memory applications have design restrictions and unknown analytic solutions. Most applications fall into a broad class of systems known as passivity-short, in which certain properties are utilized to form a structural framework for system interconnection with existing general stabilizing control techniques. Previous theoretical contributions are extended to include constraints, which can be readily applied to the development of scalable system networks in practical systems, even in the presence of unknown dynamics. In cases such as these, model identification techniques are used to obtain estimated system models which are guaranteed to be at least passivity-short. With numerous analytic tools accessible, a data-driven nonlinear control design framework is developed using model identification resulting in passivity-short systems which handles input and output saturations. Simulations are presented that prove to effectively control and stabilize example practical systems
Modélisation dynamique et commande optimale d'un système de réfrigération à base d'éjecteur
Recently, the ejector-based refrigeration system (ERS) has been widely used in the cooling
industry as an appropriate alternative to the compressor-based cooling systems. However,
the advantages of ERS such as the reliable operation and low operation and maintenance
costs are overshadowed by its low efficiency and design complexity. In this context, this
thesis presents the efforts to develop a control model enabling the ERS to operate in its
optimal operational conditions. The extensive experimental studies of ERS revealed that at
a fixed condenser inlet condition, there exists an optimal primary stream mass flow
rate (generating pressure) that simultaneously maximizes the compression ratio (Cr) and
exergy efficiency and minimizes the evaporating pressure. Then, the steady state
models of the heat exchangers were developed and used to investigate the influence of the
increase in generating pressure on the coefficient of performance (COP) of the system and
it showed that increasing the generating pressure reduces the COP, linearly. In order to
predict the choking regime of the ejector and explain the reasons of observed physical
phenomenon, the 1D model of a fixed geometry ejector installed within an R245fa ERS was
developed. The developed model demonstrated that the ejector operates in the subcritical
mode when the generating pressure is below the Cr optimum point, while it operates in
critical mode at or above the optimum generating pressure. Next, a dynamic model of the
ERS was built to evaluate the ERS transient response to an increase in the primary stream
mass flow rate. Since the ERS dynamics is mainly dominated by the thermal dynamics of
the heat exchangers, the dynamic models of the heat exchangers were developed using the
moving boundary approach and connected to the developed models of the ejector and steady
state models of the pump and expansion valve to build a single dynamic model of the system.
The built dynamic model of an ERS was used to estimate the time response of the system in
the absence of accurate experimental data of the system’s dynamics. Finally, a control model
was designed to drive an ERS towards its optimal operation condition. A self-optimizing, model-free control strategy known as Extremum seeking control (ESC) was adopted to
minimize evaporating pressure in a fixed condenser thermal fluid inlet condition. The innovative ESC model named batch phasor ESC (BPESC) was proposed based on estimating the gradient by
evaluating the phasor of the output, in batch time. The simulation results indicated that
the designed BPESC model can seek and find the optimum evaporating pressure with good performance in terms of predicting the steady state optimal values and the convergence rates.Récemment, le système de réfrigération à éjecteur (SRE) a été largement utilisé dans l'industrie du refroidissement en tant que solution de remplacement appropriée aux systèmes de refroidissement à compresseur. Cependant, les avantages du SRE, tels que le fonctionnement fiable et les faibles couts d'exploitation et de maintenance, sont éclipsés par son faible rendement et sa complexité de conception. Dans ce contexte, ce projet de recherche de doctorat a détaillé les efforts déployés pour développer une stratégie de commande permettant au système de fonctionner dans ses conditions opérationnelles optimales. Les études expérimentales approfondies du SRE ont révélé que, dans une condition d'entrée de condensateur constante, il existe un débit massique optimal du flux primaire (générant une pression) qui maximise simultanément le taux de compression
(Cr) et l'efficacité exergétique, et minimise la pression d’évaporation. Ensuite, les modèles à l’état d’équilibre des échangeurs de chaleur ont été développés et utilisés pour étudier l’influence de l’augmentation de la pression générée sur le coefficient de performance (COP) du système et il en ressort que l'augmentation de la pression génératrice réduit le COP de manière linéaire. Afin de prédire le régime d'étouffement de l'éjecteur et d'expliquer les raisons du phénomène physique observé, le modèle 1D d'un éjecteur à géométrie fixe installé dans un système SRE R245fa a été développé. Le modèle développé a démontré que l'éjecteur fonctionne en mode sous-critique lorsque la pression génératrice est inférieure au point optimal de Cr, alors qu'il fonctionne en mode critique à une pression égale ou supérieure à la pression génératrice optimale. Ensuite, un modèle dynamique du SRE a été développé pour étudier la réponse transitoire du SRE lors d’une augmentation du débit massique du flux primaire. Puisque la dynamique du SRE est principalement dominée par la dynamique thermique des échangeurs de chaleur, les modèles dynamiques des échangeurs de chaleur ont été développés à l'aide de l'approche des limites mobiles et connectés aux modèles développés de l'éjecteur et des modèles à l'état stationnaire de la pompe et de la vanne un seul modèle dynamique du système. En l’absence de données expérimentales précises sur la dynamique d’un système SRE, le modèle dynamique développé du SRE a été simulé numériquement pour étudier sa réponse temporelle. Enfin, une stratégie de commande extrêmale (ESC) a été élaboré pour régler automatiquement le SRE à ses conditions de fonctionnement optimales, c’est-à -dire pour trouver la vitesse de la pompe qui minimise la pression dans des conditions d'entrée de condenseur fixes. Afin de proposer une ESC implémentable en temps discret sur une installation réelle sujette à un bruit de mesure important et un traitement hors-ligne par trame, une nouvelle commande extrémale basée sur une approche par phaseur avec une procédure de traitement de signal par trame (BPESC) a été développée et simulée avec le modèle numérique. Les résultats de la simulation ont indiqué que le modèle BPESC peut trouver la vitesse optimale de la pompe avec de bonnes performances en termes de précision et de vitesse de convergence
An adaptive weighted least square support vector regression for hysteresis in piezoelectric actuators
© 2017 Elsevier B.V. To overcome the low positioning accuracy of piezoelectric actuators (PZAs) caused by the hysteresis nonlinearity, this paper proposes an adaptive weighted least squares support vector regression (AWLSSVR) to model the rate-dependent hysteresis of PZA. Firstly, the AWLSSVR hyperparameters are optimized by using particle swarm optimization. Then an adaptive weighting strategy is proposed to eliminate the effects of noises in the training dataset and reduce the sample size at the same time. Finally, the proposed approach is applied to predict the hysteresis of PZA. The results show that the proposed method is more accurate than other versions of least squares support vector regression for training samples with noises, and meanwhile reduces the sample size and speeds up calculation
Deep Learning-Based Machinery Fault Diagnostics
This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis
Experimental study of substrate limitation and light acclimation in cultures of the microalgae Scenedesmus obliquus—Parameter identification and model predictive control
In this study, the parameters of a dynamic model of cultures of the microalgae Scenedesmus obliquus are estimated from datasets collected in batch photobioreactors operated with various initial conditions and light illumination conditions. Measurements of biomass, nitrogen quota, bulk substrate concentration, as well as chlorophyll concentration are achieved, which allow the determination of parameters with satisfactory confidence intervals and model cross-validation against independent data. The dynamic model is then used as a predictor in a nonlinear model predictive control strategy where the dilution rate and the incident light intensity are simultaneously manipulated in order to optimize the cumulated algal biomass production.Fil: Gorrini, Federico Alberto. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - BahĂa Blanca. Instituto de Investigaciones en IngenierĂa ElĂ©ctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de IngenierĂa ElĂ©ctrica y de Computadoras. Instituto de Investigaciones en IngenierĂa ElĂ©ctrica "Alfredo Desages"; ArgentinaFil: Lara, JesĂşs Miguel Zamudio. UniversitĂ© de Mons; BĂ©lgica. Universidad de Guanajuato; MĂ©xicoFil: Biagiola, Silvina Ines. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - BahĂa Blanca. Instituto de Investigaciones en IngenierĂa ElĂ©ctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de IngenierĂa ElĂ©ctrica y de Computadoras. Instituto de Investigaciones en IngenierĂa ElĂ©ctrica "Alfredo Desages"; ArgentinaFil: Figueroa, Jose Luis. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - BahĂa Blanca. Instituto de Investigaciones en IngenierĂa ElĂ©ctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de IngenierĂa ElĂ©ctrica y de Computadoras. Instituto de Investigaciones en IngenierĂa ElĂ©ctrica "Alfredo Desages"; ArgentinaFil: Escoto, HĂ©ctor Hernández. Universidad de Guanajuato; MĂ©xicoFil: Hantson, Anne Lise. UniversitĂ© de Mons; BĂ©lgicaFil: Wouwer, Alain Vande. UniversitĂ© de Mons; BĂ©lgic
Model-Guided Data-Driven Optimization and Control for Internal Combustion Engine Systems
The incorporation of electronic components into modern Internal Combustion, IC, engine systems have facilitated the reduction of fuel consumption and emission from IC engine operations. As more mechanical functions are being replaced by electric or electronic devices, the IC engine systems are becoming more complex in structure. Sophisticated control strategies are called in to help the engine systems meet the drivability demands and to comply with the emission regulations. Different model-based or data-driven algorithms have been applied to the optimization and control of IC engine systems. For the conventional model-based algorithms, the accuracy of the applied system models has a crucial impact on the quality of the feedback system performance. With computable analytic solutions and a good estimation of the real physical processes, the model-based control embedded systems are able to achieve good transient performances. However, the analytic solutions of some nonlinear models are difficult to obtain. Even if the solutions are available, because of the presence of unavoidable modeling uncertainties, the model-based controllers are designed conservatively
- …