Retrospective Cost Optimization for Adaptive State Estimation, Input Estimation, and Model Refinement

AbstractRetrospective cost optimization was originally developed for adaptive control. In this paper, we show how this technique is applicable to three distinct but related problems, namely, state estimation, input estimation, and model refinement. To illustrate these techniques, we give two examples. In the first example, retrospective cost model refinement is used with synthetic data to estimate the cooling dynamics that are missing from a model of the ionosphere-thermosphere. In the second example, retrospective cost adaptive state estimation is used with data from a satellite to estimate a solar driver in the ionosphere- thermosphere, with performance gauged by using data from a second satellite

Damage Localization for Structural Health Monitoring Using Retrospective Cost Model Refinement

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83574/1/AIAA-2010-2628-530.pd

Retrospective‐cost‐based adaptive model refinement for the ionosphere and thermosphere

Mathematical models of physical phenomena are of critical importance in virtually all applications of science and technology. This paper addresses the problem of how to use data to improve the fidelity of a given model. We approach this problem using retrospective cost optimization, which uses data to recursively update an unknown subsystem interconnected to a known system. Applications of this technique are relevant to applications that depend on large‐scale models based on first‐principles physics, such as the global ionosphere–thermosphere model (GITM). Using GITM as the truth model, we demonstrate that measurements can be used to identify unknown physics. Specifically, we estimate static thermal conductivity parameters, as well as a dynamic cooling process. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 446–458, 2011Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86874/1/10127_ftp.pd

Adaptive Input Reconstruction with Application to Model Refinement, State Estimation, and Adaptive Control.

Input reconstruction is the process of using the output of a system to estimate its input. In some cases, input reconstruction can be accomplished by determining the output of the inverse of a model of the system whose input is the output of the original system. Inversion, however, requires an exact and fully known analytical model, and is limited by instabilities arising from nonminimum-phase zeros. The main contribution of this work is a novel technique for input reconstruction that does not require model inversion. This technique is based on a retrospective cost, which requires a limited number of Markov parameters. Retrospective cost input reconstruction (RCIR) does not require knowledge of nonminimum-phase zero locations or an analytical model of the system. RCIR provides a technique that can be used for model refinement, state estimation, and adaptive control. In the model refinement application, data are used to refine or improve a model of a system. It is assumed that the difference between the model output and the data is due to an unmodeled subsystem whose interconnection with the modeled system is inaccessible, that is, the interconnection signals cannot be measured and thus standard system identification techniques cannot be used. Using input reconstruction, these inaccessible signals can be estimated, and the inaccessible subsystem can be fitted. We demonstrate input reconstruction in a model refinement framework by identifying unknown physics in a space weather model and by estimating an unknown film growth in a lithium ion battery. The same technique can be used to obtain estimates of states that cannot be directly measured. Adaptive control can be formulated as a model-refinement problem, where the unknown subsystem is the idealized controller that minimizes a measured performance variable. Minimal modeling input reconstruction for adaptive control is useful for applications where modeling information may be difficult to obtain. We demonstrate adaptive control of a seeker-guided missile with unknown aerodynamics.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91520/1/amdamato_1.pd

Data-Based Model Refinement Using Retrospective Cost Optimization

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83642/1/AIAA-2010-7889-194.pd

Retrospective Cost Model Refinement for On-Line Estimation of Constant and Time-Varying Flight Parameters

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106506/1/AIAA2013-5193.pd

Retrospective-Cost-Based Adaptive Input and State Estimation for the Ionosphere–Thermosphere

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140666/1/1.I010286.pd

Structural Health Determination and Model Refinement for a Deployable Composite Boom

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77171/1/AIAA-2009-2373-948.pd

On the Accuracy of State Estimators for Constant and Time-Varying Parameter Estimation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106504/1/AIAA2013-5192.pd

A Model-Based Framework for the Smart Manufacturing of Polymers

It is hard to point a daily activity in which polymeric materials or plastics are not involved. The synthesis of polymers occurs by reacting small molecules together to form, under certain conditions, long molecules. In polymer synthesis, it is mandatory to assure uniformity between batches, high-quality of end-products, efficiency, minimum environmental impact, and safety. It remains as a major challenge the establishment of operational conditions capable of achieving all objectives together. In this dissertation, different model-centric strategies are combined, assessed, and tested for two polymerization systems. The first system is the synthesis of polyacrylamide in aqueous solution using potassium persulfate as initiator in a semi-batch reactor. In this system, the proposed framework integrates nonlinear modelling, dynamic optimization, advanced control, and nonlinear state estimation. The objectives include the achievement of desired polymer characteristics through feedback control and a complete motoring during the reaction. The estimated properties are close to experimental values, and there is a visible noise reduction. A 42% improvement of set point accomplishment in average is observed when comparing feedback control combined with a hybrid discrete-time extended Kalman filter (h-DEKF) and feedback control only. The 4-state geometric observer (GO) with passive structure, another state estimation strategy, shows the best performance. Besides achieving smooth signal processing, the observer improves 52% the estimation of the final molecular weight distribution when compared with the h-DEKF. The second system corresponds to the copolymerization of ethylene with 1,9-decadiene using a metallocene catalyst in a semi-batch reactor. The evaluated operating conditions consider different diene concentrations and reaction temperatures. Initially, the nonlinear model is validated followed by a global sensitivity analysis, which permits the selection of the important parameters. Afterwards, the most important kinetic parameters are estimated online using an extended Kalman filter (EKF), a variation of the GO that uses a preconditioner, and a data-driven strategy referred as the retrospective cost model refinement (RCMR) algorithm. The first two strategies improve the measured signal, but fail to predict other properties. The RCMR algorithm demonstrates an adequate estimation of the unknown parameters, and the estimates converge close to theoretical values without requiring prior knowledge