In case of physiological systems state and parameter estimation is a crucial question. It is key to describe given patient population with appropriate accuracy. Furthermore, state feedback kind of applications also require some sort of estimation procedure in order to get internal information about the controlled system. Linear parameter varying (LPV) framework is beneficial for controller design as well. However, to realize the necessary scheduling parameters, estimation of both state variables and model parameters is needed. A possible solution is the application of Dual Extended Kalman Filter (DEKF) which is able to estimate these signals. The developed framework can be used to design LPV based controller in our further work. In this study we introduce our developed DEKF solution by using the widely applied Cambridge Type 1 Diabetes Mellitus (TIDM) model for virtual patient generation. We have found that our solution is able to estimate the state variables with good accuracy. The variation of parameters can also be tracked by using the proposed solution.

The utilization of modern and advanced control engineering related methods for the control, estimation and assessment of physiological applications is widespread. It is also well-known that this engineering apparatus is executed on digital computers. The current insufficiency of available and accurate discretized models, especially in case of Diabetes Mellitus (DM), provides incentive for this research. The researchers typically approximate the continuous solutions which may not be the best alternative in many cases, in particular considering numerical stability and cost-effectiveness. In this paper we performed an analysis of the available discretization options in order to develop discrete models with a special focus on the Linear Parameter Varying (LPV) systems. LPV techniques are very useful frameworks which allow the application of linear controller, observer and estimator design. In this study, three LPV discretization and two Jacobian based discretization methods are introduced and analyzed to provide a basis for our further investigations in the topic.

The application of the Soft Computing based methods, especially, the Tensor Product (TP) transformation has several beneficial properties from the biological modeling and control point of view, because complex, nonlinear processes can be handled by them effectively. Another advantage of these tools consist on the Linear Parameter Varying (LPV) and Linear Matrix Inequality (LMI) based techniques can be easily connected to them. The aim of this study is to develop TP models, which can describe the tumor growth beside anti-angiogenic treatment. The role of the anti-angiogenic therapies is to decrease the size of the tumor to operable or maintainable level. From control engineering point of view, the treatment process can be formulated as a control task. In this work, we realized two TP models, which approximates the initial transformed model with high accuracy, regardless the kind of input load and without stability problems. The TP models will be used for TP-based controller design on LMI basis.

This paper deals with the application of adaptive model objects for integration of modeling products and robot systems. Product modeling is an advanced method for integration of product related computer descriptions. Product model describes engineering related information about parts and assemblies in a mechanical system. Robots use this information for flexible automated manufacturing of the modeled product. Consequence of definition or change of model objects may be unacceptable change in production. To prevent similar problems by improper design at production, two-way communication is needed between product design and production planning. The authors proposed highly integrated model objects for this purpose. Model objects evaluate and react inside and outside changes by analysis of the related behaviors. Adaptivity features are created and then applied for behavior driven generation of modification of model entities. This paper analyzes process-oriented product modeling of robot application related mechanical systems. The proposed objects are composed by elementary, structural, relationship, behavior, knowledge and adaptivity features. This paper also discusses integration of multi layered robot process model with shape models, procedure of modification handling by behavior features in a product-robot model system, general architecture of the proposed model objects and behavior feature driven activities of active model objects on four levels.

In this paper, the authors discuss their recent contribution to the methodology of active product modeling and propose integrated model objects for engineering activities in mechanical systems. The purpose of the proposed model objects is to react to changes in the inside and the related modeled world by analysis of behaviors and behavior driven generation of adaptivity features for modification of model entities inside and outside the object. They are composed of elementary, structural, relationship, behavior, knowledge and adaptivity features. The proposed model objects are inherently highly integrated. An overview of product modeling introduces the authors' approach to modeling by using the proposed model objects. Following this, the architecture of integrated, environment adaptive model objects is detailed. Then activities of integrated objects are placed on four levels of the model. Finally, integration and implementation issues are discussed.