Name
Title | ||
|---|---|---|
Tackling Complexity And Missing Information In Adaptive Control By Fixed Point Transformation-Based Approach |
Abstract | ||
|---|---|---|
Complexity is the most common feature of the practical tasks to be solved in control technology. Grasping only a little particular segment of reality, often referred to as "model building", provides us with imprecise and incomplete models of certain subsystems that operate in dynamic interaction with their neglected environment. The classical method of Model Predictive Control (MPC) assumes that our models, though they are not perfect, at least well describe the most important features of the system under control, therefore these models can be used for controller design and the remaining errors can be treated as uncertainties and/or unknown external perturbations.Other practical fact is that the controller normally cannot be provided with complete information on the dynamic state of the controlled system. In the models of biological systems certain "artificial compartments" often occur that are introduced to qualitatively mirror and more or less quantitatively describe the behavior of such systems without assuming that they really "cover" actually existing anatomic components. Consequently, even in principle, it is impossible to directly measure the actual state of such dynamical systems.Further problem is that these variables are assumed to be related to directly measurable quantities by nonlinear functions of uncertain parameters. This fact excludes the possibility of using classical state estimators.Furthermore, these systems normally are "underactuated", i.e. the number of the state variables is greater than that of the independent control signals.The aim of this paper is to show that it is possible to defy these problems by the fixed point transformations-based adaptive controller in controlling depth of hypnosis. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | Complexity, Missing Information, Underactuation, Anaesthesia Models, Bispectral Index (BIS), Propofol Administration, Adaptive Control, Robust Fixed Point Transformation |
Field | DocType | ISSN |
Convergence (routing),Controller design,Control theory,Computer science,Model building,Model predictive control,Artificial intelligence,Control system,Fixed point,Underactuation,Mathematical optimization,Adaptive control,Machine learning | Conference | 1062-922X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| József K. Tar | 1 | 104 | 25.33 |
| Imre J. Rudas | 2 | 388 | 63.89 |
| László Nádai | 3 | 0 | 0.34 |
| Imre Felde | 4 | 3 | 6.57 |
| Bertalan Csanadi | 5 | 0 | 1.01 |