Xunyuan Yin

Distributed moving horizon state estimation of two-time-scale nonlinear systems

Abstract In this paper, we focus on distributed moving horizon estimation (DMHE) for a class of two-time-scale nonlinear systems described in the framework of singularly perturbed systems. By taking advantage of the time-scale separation property, a two-time-scale system is first decomposed into a reduced-order fast system and a reduced-order slow system. The slow system is further decomposed into several interconnected slow subsystems. In the proposed distributed state estimation scheme, a local estimator is designed for each slow subsystem and for the reduced-order fast system. The slow subsystem estimators communicate with each other to exchange information and they are only required to send information to the fast system one-directionally. The fast system estimator does not send out any information. The local estimators are designed as observer-enhanced moving horizon estimators. Sufficient conditions on the convergence of the estimation error of the DMHE are derived. The application of the proposed DMHE to a chemical process example demonstrates its applicability and effectiveness.

From decentralized to distributed state estimation

In this paper, we consider distributed state estimation of nonlinear process networks. It is assumed that a decentralized state estimation system already exists for the nonlinear system, where the local estimators can be of different types. We propose a systematic approach to take advantage of the existing decentralized estimators to form a distributed state estimation network. A compensator is designed for each subsystem, and is connected to the corresponding decentralized estimator to obtain an augmented estimator. The augmented estimators communicate with each other to exchange information. We derive sufficient conditions on the convergence and boundedness of the estimation error of the distributed estimation network. The proposed approach is demonstrated via the application to a chemical process example.

State estimation of wastewater treatment plants based on model approximation

Abstract In this article, we consider state estimation of wastewater treatment plants based on model approximation. In particular, we consider a wastewater treatment plant described by the Benchmark Simulation Model No.1 which consists of a five-chamber reactor and a settler. We propose to use the proper orthogonal decomposition approach with re-identification of output equations to obtain a reduced-order model of the original system. Then, the reduced-order model is taken advantage of in state estimation. An approach on how to determine an appropriate minimum measurement set is also proposed based on degree of observability. A continuous-discrete extended Kalman filtering algorithm is used to design the estimator based on the reduced-order model. We show through extensive simulations under different weather conditions that the estimator based on the reduced-order model with re-identified output equations gives good state estimates of the actual process.

Subsystem decomposition for distributed state estimation of nonlinear systems

In this work, we investigate the subsystem decomposition problem for distributed state estimation of nonlinear systems. A systematic procedure for subsystem decomposition for distributed state estimation is proposed. Key steps in the procedure include observability test of the entire system, observable states identification for each output measurement, relative degree analysis and sensitivity analysis between measured outputs and states. A few examples used to illustrate the methods used in different steps and the entire procedure demonstrate the effectiveness and applicability of the proposed methods/procedure.