RHC method for application to BMI‐based systems

RHC method for application to BMI‐based systems Purpose – The purpose of this paper is to present research in the area of control method for the man‐machine systems with brain machine interface (BMI). Concrete target system is, for instance, a car cruising system and so on. Design/methodology/approach – The improved receding horizon control (RHC) method for the sampled‐data systems and the adaptive digital‐to‐analog (DA) converter which has the way to switch the sampling functions according to the system status are used. The feature selection method based on the kernel support vector machines with the backward stepwise selection for the BMI signals are also used. Findings – This paper proposes the new improved RHC method with the adaptive DA converter for the application of the BMI‐based systems. The proposed method is illustrated as useful and effective method for the systems to which switch of control laws is indispensable by the simulations. Research limitations/implications – Although the proposed method is effective for the BMI‐based systems with switching of control laws, the faster algorithm for RHC will be need to apply to the man‐machine systems with the BMI in practical use. Practical implications – The basic concept or framework of the proposed method can be used for the real man‐machine systems with the BMI, for examples, car crusing systems, wheel‐chaired systems and so on. Originality/value – The paper contributes to the development of the new effective control method for the BMI‐based man‐machine systems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Kybernetes Emerald Publishing

RHC method for application to BMI‐based systems

Kybernetes, Volume 37 (5): 15 – Jun 17, 2008

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Publisher
Emerald Publishing
Copyright
Copyright © 2008 Emerald Group Publishing Limited. All rights reserved.
ISSN
0368-492X
DOI
10.1108/03684920810873272
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to present research in the area of control method for the man‐machine systems with brain machine interface (BMI). Concrete target system is, for instance, a car cruising system and so on. Design/methodology/approach – The improved receding horizon control (RHC) method for the sampled‐data systems and the adaptive digital‐to‐analog (DA) converter which has the way to switch the sampling functions according to the system status are used. The feature selection method based on the kernel support vector machines with the backward stepwise selection for the BMI signals are also used. Findings – This paper proposes the new improved RHC method with the adaptive DA converter for the application of the BMI‐based systems. The proposed method is illustrated as useful and effective method for the systems to which switch of control laws is indispensable by the simulations. Research limitations/implications – Although the proposed method is effective for the BMI‐based systems with switching of control laws, the faster algorithm for RHC will be need to apply to the man‐machine systems with the BMI in practical use. Practical implications – The basic concept or framework of the proposed method can be used for the real man‐machine systems with the BMI, for examples, car crusing systems, wheel‐chaired systems and so on. Originality/value – The paper contributes to the development of the new effective control method for the BMI‐based man‐machine systems.

Journal

KybernetesEmerald Publishing

Published: Jun 17, 2008

Keywords: Cybernetics; Man‐machine systems; Man machine interface; Control systems

References

  • A continuation/GMRES method for fast computation of nonlinear receding horizon control
    Ohtsuka, T.
  • Statistical Learning Theory
    Vapnik, V.N.

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