Paper Info
Title
Tumor Growth Control By Tp-Lpv-Lmi Based Controller
Abstract
The advantages of using advanced control techniques related to physiological applications are unquestionable as it was proven in many cases in the recent times. Although, there are several challenges that practitioners need to face. For example, the lack of precise information about the internal state of the patients, i.e. the inter- and intra-patient variabilities which cause uncertainties that need to be tolerated by the applied controllers. In this study an alternative solution is presented for control of tumor growth. Uncertainties and nonlinearities are handled by the applied Linear Parameter Varying (LPV) methodology completed by Tensor Product (TP) model transformation. Linear Matrix Inequalities (LMI) based optimization are used for controller design. The lack of information about the internal state is solved by using Extended Kalman Filter (EKF) to estimate the non-measurable state variables. The developed control structure is able to enforce the controlled system to behave as a predefined reference system. We show that the control framework operates well and reaches the determined aims of the control.
Year
DOI
Venue
2018
10.1109/SMC.2018.00439
2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
Keywords
Field
DocType
Tensor Model transformation, Linear Parameter Varying, Linear Matrix Inequality, Parallel Distribution Control, tumor control
Tensor product,Control theory,Extended Kalman filter,Model transformation,Computer science,Matrix (mathematics),Control theory,Controller design,State variable,Linear matrix inequality
Conference
ISSN
Citations 
PageRank 
1062-922X
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Gyorgy Eigner1137.50
Dániel Andras Drexler2176.29
Levente Kovács3146.44