Convex Polytopic Modeling of Diabetes Mellitus: A Tensor Product based approach

Tensor Product (TP) transformation based modeling and control can be useful in biomedical engineering, since complex nonlinear control tasks can be handled easier with it. Moreover, the modeling approach can handle the Linear Parameter Varying (LPV) models and produces a tensor based system description, which can be used during Linear Matrix Inequality (LMI) based controller design. The TP property makes the usability of the method beneficial as LMI connected techniques allows using the Lyapunov theorems. The aim of the current work is to demonstrate the usability of TP models in biomedical applications, i.e. diabetes modeling. The core model, the minimal model is investigated and simulation results are presented under Matlab

Control of T1DM via Tensor Product-based framework

With this study our goal was to investigate and prove the usability of the Tensor Product (TP)-transformation based modeling and control regarding the control of Diabetes Mellitus (DM). In details, we examined the TP-based modeling possibilities of Type 1 DM (T1DM) by using the simple well-known Minimal Model. We provided a TP-based controller design solution, where the main focus was on the disturbance rejection. Generally, the TP-based framework using the Parallel Distributed Control (PDC)- and Linear Parameter Varying (LPV) theorems via wellconditioned Linear Matrix Inequalities (LMI) to realize the TPbased controller. In this manner the basis of our controller design procedure was the development of the control oriented qLPV model form, the selection and application of appropriate LMIs through which the PDC-based TP controller can be developed and the realization of the control environment. We prove the usability of the developed control solution on the mentioned T1DM model beside unfavorable disturbances

Gain-scheduled H∞ control via parameter-dependent Lyapunov functions

Synthesising a gain-scheduled output feedback H∞ controller via parameter-dependent Lyapunov functions for linear parameter-varying (LPV) plant models involves solving an infinite number of linear matrix inequalities (LMIs). In practice, for affine LPV models, a finite number of LMIs can be achieved using convexifying techniques. This paper proposes an alternative approach to achieve a finite number of LMIs. By simple manipulations on the bounded real lemma inequality, a symmetric matrix polytope inequality can be formed. Hence, the LMIs need only to be evaluated at all vertices of such a symmetric matrix polytope. In addition, a construction technique of the intermediate controller variables is also proposed as an affine matrix-valued function in the polytopic coordinates of the scheduled parameters. Computational results on a numerical example using the approach were compared with those from a multi-convexity approach in order to demonstrate the impacts of the approach on parameter-dependent Lyapunov-based stability and performance analysis. Furthermore, numerical simulation results show the effectiveness of these proposed techniques

Stable Hybrid Fuzzy Controller-based Architecture for Robotic Telesurgery Systems

Robotic surgery and remotely controlled teleoperational systems are on the rise. However, serious limitations arise on both the hardware and software side when traditional modeling and control approaches are taken. These limitations include the incomplete modeling of robot dynamics, tool–tissue interaction, human– machine interfaces and the communication channel. Furthermore, the inherent latency of long-distance signal transmission may endanger the stability of a robot controller. All of these factors contribute to the very limited deployment of real robotic telesurgery. This paper describes a stable hybrid fuzzy controller-based architecture that is capable of handling the basic challenges. The aim is to establish high fidelity telepresence systems for medical applications by easily handled modern control solution

AUTOMOTIVE APPLICATIONS OF EVOLVING TAKAGI-SUGENO-KANG FUZZY MODELS

This paper presents theoretical and application results concerning the development of evolving Takagi-Sugeno-Kang fuzzy models for two dynamic systems, which will be viewed as controlled processes, in the field of automotive applications. The two dynamic systems models are nonlinear dynamics of the longitudinal slip in the Anti-lock Braking Systems (ABS) and the vehicle speed in vehicles with the Continuously Variable Transmission (CVT) systems. The evolving Takagi-Sugeno-Kang fuzzy models are obtained as discrete-time fuzzy models by incremental online identification algorithms. The fuzzy models are validated against experimental results in the case of the ABS and the first principles simulation results in the case of the vehicle with the CVT