Development of moving window state and parameter estimators under maximum likelihood and Bayesian frameworks. (December 2017)

• Record Type: Journal Article
• Authors: Valluru, Jayaram; Lakhmani, Piyush; Patwardhan, Sachin C.; Biegler, Lorenz T.
• Is Part Of: Journal of process control. Volume 60(2017)
• Page Start: 48
• Page End: 67
• Publication Date: 2017-12
• Subjects: State and parameter estimation -- Maximum likelihood estimation -- Maximum a posteriori estimation -- Moving window estimation -- Recursive Bayesian estimators -- Multirate systems; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2017.08.007
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Moving window maximum likelihood and maximum a posteriori state and parameter estimators. Necessary modifications to handle multi-rate and delayed measurements. The efficacy of the proposed estimators is demonstrated through the case studies. The proposed moving window estimators track the drifting parameters fairly accurately. Abstract: Estimation of slowly varying model parameters/unmeasured disturbances is of paramount importance in process monitoring, fault diagnosis, model based advanced control and online optimization. The conventional approach to estimate drifting parameters is to artificially model them as a random walk process and estimate them simultaneously with the states. However, this may lead to a poorly conditioned problem, where the tuning of the random walk model becomes a non-trivial exercise. In this work, the moving window parameter estimator of Huang et al.[1] is recast as a moving window maximum likelihood (ML) estimator. The state can be estimated within the window using any recursive Bayesian estimator. It is assumed that, when the model parameters are perfectly known, the innovation sequence generated by the chosen Bayesian estimator is a Gaussian white noise process and is further used to construct a likelihood function that treats the model parameters as unknowns. This leads to a well conditioned problem where the only tuning parameter is the length of the moving window, which is much easier to select than selecting the covariance of the random walk model. The ML formulation is further modified to develop a maximum a posteriori (MAP) cost function by including arrival cost for the parameter. Efficacy of the proposed ML and MAP formulations has been demonstrated by conducting simulation studies and experimental evaluation. Analysis of the simulation and experimental results reveals that the proposed moving window ML and MAP estimators are capable of tracking the drifting parameters/unmeasured disturbances fairly accurately even when the measurements are available at multiple rates and with variable time delays.