TY - JOUR
T1 - RKH space methods for low level monitoring and control of nonlinear systems
AU - Cover, Alan
AU - Reneke, James
AU - Fryer, Michael
AU - Lenhart, Suzanne
AU - Protopopescu, Vladimir
PY - 1996/2
Y1 - 1996/2
N2 - A monitor or controller is smart provided that the device is equipped with local computational resources for analyzing data, detecting changes, and making decisions. The problem for the monitoring function is to design algorithms to flag model shifts for dynamic systems in a context requiring many interacting system components and system reconfigurations. The problem for the control function is to improve system performance by updating control feedbacks after the model shift has been detected. Therefore it is desirable that smart monitors and controllers be adaptive and update with minimal intervention from a central director. We present here an approach to designing smart monitoring and control devices based on a stochastic linearization of the system whose dynamics is noisy and unknown. This linearization is obtained by factoring the discrete system covariance matrix, estimated from observations, and applying reproducing kernel Hilbert space techniques. The method is nonparametric which allows the smart devices to operate with only a low level logic.
AB - A monitor or controller is smart provided that the device is equipped with local computational resources for analyzing data, detecting changes, and making decisions. The problem for the monitoring function is to design algorithms to flag model shifts for dynamic systems in a context requiring many interacting system components and system reconfigurations. The problem for the control function is to improve system performance by updating control feedbacks after the model shift has been detected. Therefore it is desirable that smart monitors and controllers be adaptive and update with minimal intervention from a central director. We present here an approach to designing smart monitoring and control devices based on a stochastic linearization of the system whose dynamics is noisy and unknown. This linearization is obtained by factoring the discrete system covariance matrix, estimated from observations, and applying reproducing kernel Hilbert space techniques. The method is nonparametric which allows the smart devices to operate with only a low level logic.
UR - http://www.scopus.com/inward/record.url?scp=0030535798&partnerID=8YFLogxK
U2 - 10.1142/S0218202596000067
DO - 10.1142/S0218202596000067
M3 - Article
AN - SCOPUS:0030535798
SN - 0218-2025
VL - 6
SP - 77
EP - 96
JO - Mathematical Models and Methods in Applied Sciences
JF - Mathematical Models and Methods in Applied Sciences
IS - 1
ER -