Abstract
In this paper, the problem of predicting the output of a system at regular periods from irregularly sampled signals is addressed. A model based predictor that takes into account the possibly delayed measurement signals is used. Robustness of the predictor to the time-delays and data availability as well as disturbance and noise attenuation is dealt with via Hinfin performance. A design strategy is proposed based on the available disturbances information
- and
- attenuation,availability,control
- attenuation,output
- availability,delayed
- cost
- delays,virtual
- distributed
- fusion,signal
- fusion,stability,time
- inequalities,networked
- information,irregularly
- instrumentation,virtual
- matrix
- measure-,networked
- measure-,sensor
- measurement
- measurement,lmi,linear
- measurement,time-varying
- measurements,distributed
- measurements,noise
- measurements,output
- measurements,sensor
- models,scarce
- parameter
- prediction,predictive
- prediction,predictor
- robustness,scarce
- sampled
- sampling,time-varying
- sensor
- sensor,data
- sensors,ments,networked
- signals,delays,distributed
- signals,lmi,low
- systems,costs,delay,frequency
- systems,disturbance
- virtual
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