Robust servo control of a novel type 1 diabetic model

Kovács, Levente; Kulcsár, Balázs; György, András; Benyó, Zoltán

doi:10.1002/oca.963

Cited by 29 publications

(7 citation statements)

References 41 publications

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“…Future research will be focused on the extension of the simulation results with experimental validations by adding some comparison data with different other nature-inspired and classical algorithms [1]- [12], [34]- [37], and the analysis of the computational complexity of the proposed approach will be included.…”

Section: Discussionmentioning

confidence: 99%

Performance analysis of torque motor systems with PID controllers tuned by Bacterial Foraging Optimization algorithms

Precup

Borza

Rădac

et al. 2014

2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applicati

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This paper deals with the optimal tuning of proportional-integral-derivative (PID) controllers for a pancake direct current (DC) torque motor system that belongs to a Diesel engine exhaust gas recirculation valve in automotive applications. The Bacterial Foraging Optimization (BFO) algorithms solve an optimization problem which targets the minimization of an objective function expressed as the weighted sum of overshoot plus the integral of squared control error, and the parameters of the PID controllers are the variables of the objective function. Our BFO algorithms are characterized by the validation of the position of bacteria only if the PID control system response is in a valid range. A digitally simulated case study which deals with the shaft angle control of a DC torque motor system is considered. The impact of four parameters of one BFO algorithm on the objective function values is discussed.

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Section: Discussionmentioning

confidence: 99%

Performance analysis of torque motor systems with PID controllers tuned by Bacterial Foraging Optimization algorithms

Precup

Borza

Rădac

et al. 2014

2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applicati

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“…The idea was partially adapted and modified from Liu and Tang (2008) and Kovács et al (2011). The idea is to take uncertain parameters in the nonlinear model.…”

Section: H ∞ Control Designmentioning

confidence: 99%

Model-based angiogenic inhibition of tumor growth using modern robust control method

Kovács

Szeles

Sápi

et al. 2014

Computer Methods and Programs in Biomedicine

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Cancer is one of the most destructive and lethal illnesses of the modern civilization. In the last decades, clinical cancer research shifted towards molecular targeted therapies which have limited side effects in comparison to conventional chemotherapy and radiation therapy. Anti-angiogenic therapy is one of the most promising cancer treatment methods. The dynamical model for tumor growth under angiogenic stimulator/inhibitor control was posed by Hahnfeldt et al. (1999), and it was investigated and partly modified many times. In this paper, a modified version of the originally published model is used in order to describe a continuous infusion therapy. To generalize individualized therapies a robust control method is proposed using H ∞ methodology. Uncertainty weighting functions are determined based on the real pathophysiological case and simulations are performed on different tumor volumes to demonstrate the robustness of the proposed method.

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“…Such algorithms can be adapted from other applications [19]- [25] such that to fit our image processing purposes. …”

Section: B Aam Search Algorithmmentioning

confidence: 99%

Initialization and lost track recovery performance analysis of face features tracking

Voisan

Precup

Drăgan

2014

2014 IEEE 9th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)

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Face feature tracking is an important element for detection and interpretation of human face expression and for further human mood-based interactions. This paper discusses the current status in face modeling techniques and performs a performance analysis. The analysis concerns the initialization and it also includes the lost track recovery for an Active Appearance Model-based face tracking algorithm. I.INTRODUCTION Face tracking and, more precisely, face parameters tracking is based on matching a predefined model or template to an image of a real face. This approach has a wide area of applications ranging from base human expression detection to more advanced forms of human to machine interactions.Such a model-based approach can be used to match unlearned data on the basis of a correct set of parameters. The model-based approach can also generate good results being able to represent face characteristics using a smaller set of parameters, which is important in this area of intensive computing based recognition tasks.Since this approach is a good candidate to be deployed on a wide range of computing systems, this paper proposes a literature analysis on the current status in face modeling techniques. The general objective of integration in robotics applications is targeted. Therefore, the paper performs a performance analysis of these techniques from the initialization point of view. The lost track recovery is taken into consideration in the analysis. The analysis concerns a popular face tracking algorithm, namely the Active Appearance Model (AAM) search.The paper is structured as follows: the overview on face modeling and feature tracking techniques is conducted in the next section. Section III presents the performance analysis in the case of statistical models of appearance approach and in the case of AAM search algorithm. The conclusions are highlighted in Section IV.

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Robust servo control of a novel type 1 diabetic model

Cited by 29 publications

References 41 publications

Performance analysis of torque motor systems with PID controllers tuned by Bacterial Foraging Optimization algorithms

Performance analysis of torque motor systems with PID controllers tuned by Bacterial Foraging Optimization algorithms

Model-based angiogenic inhibition of tumor growth using modern robust control method

Initialization and lost track recovery performance analysis of face features tracking

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