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13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS)

作者： Singh, Shrita Kumar, Kunal Patwardhan, Sachin C. Indian Inst Technol Dept Energy Sci & Engn Mumbai 400076 Maharashtra India Indian Inst Technol Dept Syst & Control Engn Mumbai 400076 Maharashtra India Indian Inst Technol Dept Chem Engn Mumbai 400076 Maharashtra India

In the process industry, black box linear perturbation models are often used for the development of model predictive controllers. Maintaining a high-quality model so as to achieve good control performance in the face of changing operating conditions is a difficult task. In adaptive control schemes, the model parameters are updated online using recursive least square schemes. These recursive schemes typically update the model parameters at every sampling instant. Since in many chemical/biological processes, the model parameters change at a relatively slow rate compared to state variables, it is beneficial to update the model parameters using blocks of data points instead of updating at each sampling instant. In this work, a constrained update scheme based on blocks of data is proposed for updating ARMAX model parameters online. The inclusion of constraints ensures that the noise model is stable and inversely stable. The constrained formulation is further simplified to arrive at two unconstrained recursive parameter update schemes. The efficacy of the proposed schemes is demonstrated using a simulation study on an artificial system and experimental data obtained from a temperature control system and benchmark quadruple tank system. Copyright (C) 2022 The Authors.

关键词： Recursive least squares Block recursive estimation constrained parameter estimation

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Development of Block Oriented Recursive and constrained parameter estimation Schemes for ARMAX Models

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IFAC-PapersOnLine 2022年第7期55卷 7-12页

作者： Shrita Singh Kunal Kumar Sachin C. Patwardhan Department of Energy Science and Engineering Indian Institute of Technology Bombay Mumbai-400076 India Department of Systems and Control Engineering Indian Institute of Technology Bombay Mumbai-400076 India Department of Chemical Engineering Indian Institute of Technology Bombay Mumbai-400076 India

In the process industry, black box linear perturbation models are often used for the development of model predictive controllers. Maintaining a high-quality model so as to achieve good control performance in the face of changing operating conditions is a difcult task. In adaptive control schemes, the model parameters are updated online using recursive least square schemes. These recursive schemes typically update the model parameters at every sampling instant. Since in many chemical/biological processes, the model parameters change at a relatively slow rate compared to state variables, it is beneficial to update the model parameters using blocks of data points instead of updating at each sampling instant. In this work, a constrained update scheme based on blocks of data is proposed for updating ARMAX model parameters online. The inclusion of constraints ensures that the noise model is stable and inversely stable. The constrained formulation is further simplifed to arrive at two unconstrained recursive parameter update schemes. The efficacy of the proposed schemes is demonstrated using a simulation study on an artificial system and experimental data obtained from a temperature control system and benchmark quadruple tank system.

关键词： Recursive least squares Block recursive estimation constrained parameter estimation

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Adaptive parameter Identification for Nonlinear Sandwich Systems with Hysteresis Nonlinearity Based Guaranteed Performance

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2021年第2期19卷 942-952页

作者： Li, Linwei Zhang, Huanlong Wang, Fengxian Ren, Xuemei Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450000 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

The paper presents an adaptive identification algorithm via data filtering and improved prescribed performance function for Sandwich systems with hysteresis nonlinearity. By developing a filter in which the filter is simple and easy to realize online and several variables, the estimation error vector can be derived. To improve the transient performance of estimator, a modified prescribed performance function is proposed to constrain the estimation error data through the usage of the predefined domain. For the constrained estimation error condition, the error transformation technique is utilized to simplify the design of the estimator thanks to that the restricted condition is transformed into unconstrained condition. To achieve the convergence of the parameter estimation and assure the predetermined property, a fresh adaptive law is developed. Moreover, the theoretical analysis indicates that the error can converge to a small region based on martingale difference theorem. According to the numerical verification and experimental results, the advantage and practicability of the invented estimator are inspected by comparing the estimators with unconstrained condition.

关键词： constrained parameter estimation data filtering error transformation idea hysteresis prescribed performance function Sandwich systems

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Active Model-Based Fault Diagnosis in Reconfigurable Battery Systems

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2021年第3期36卷 2584-2597页

作者： Schmid, Michael Gebauer, Emanuel Hanzl, Christian Endisch, Christian TH Ingolstadt Inst Innovat Mobil D-85049 Ingolstadt Germany

With the increasing demand for electric vehicles, the interest in battery systems is growing. In order to enable safe operation of these complex energy storage systems, methods of fault diagnosis are needed. Particularly, reconfigurable battery systems (RBSs) with switches are promising on the way to fault tolerance as they allow the system to be reconfigured in the event of a fault. In this article, a model-based fault diagnosis algorithm is developed and validated that uses the switches of an RBS to improve the fault isolability. Since the algorithm changes the structure of the system in order to differentiate between nonisolable faults, it is classified as an active fault diagnosis algorithm. The deviations between sensor measurements and model, called residuals, are stochastically analyzed. For fault isolation, a fuzzy clustering approach is used. A constrained sigma-point Kalman filter minimizes model uncertainties and therefore increases the sensitivity and robustness of the fault diagnosis approach. Furthermore, the filter allows estimating the fault amplitude in case of a fault. Based on active sequential hypothesis testing, a policy to calculate the next switch position is proposed and investigated. It is shown simulatively and experimentally that additional faults are isolated by the presented active approach.

关键词： Fault diagnosis Batteries Testing Fault tolerance Fault tolerant systems Battery charge measurement Circuit faults Active fault isolation active hypothesis testing constrained parameter estimation fault tolerance fuzzy clustering reconfigurable battery system (RBS)

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parameter estimation of Electromechanical Oscillation Based on a constrained EKF with C&I-PSO

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ENERGIES 2018年第8期11卷 2059页

作者： Sun, Yonghui Wang, Yi Bai, Linquan Hu, Yinlong Sidorov, Denis Panasetsky, Daniil Hohai Univ Coll Energy & Elect Engn Nanjing 210098 Jiangsu Peoples R China ABB Inc Raleigh NC 27606 USA Russian Acad Sci Melentiev Energy Syst Inst Irkutsk 664033 Russia

By combining together the extended Kalman filter with a newly developed C&I particle swarm optimization algorithm (C&I-PSO), a novel estimation method is proposed for parameter estimation of electromechanical oscillation, in which critical physical constraints on the parameters are taken into account. Based on the extended Kalman filtering algorithm, the constrained parameter estimation problem is formulated via the projection method. Then, by utilizing the penalty function method, the obtained constrained optimization problem could be converted into an equivalent unconstrained optimization problem;finally, the C&I-PSO algorithm is developed to address the unconstrained optimization problem. Therefore, the parameters of electromechanical oscillation with physical constraints can be successfully estimated and better performed. Finally, the effectiveness of the obtained results has been illustrated by several test systems.

关键词： constrained parameter estimation extended Kalman filter power systems C&I particle swarm optimization ringdown detection

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Model-based multi-component adaptive prognosis for hybrid dynamical systems

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CONTROL ENGINEERING PRACTICE 2018年 72卷 1-18页

作者： Prakash, Om Samantaray, Arun Kumar Bhattacharyya, Ranjan Indian Inst Technol Dept Mech Engn Syst Dynam & Control Lab Kharagpur 721302 W Bengal India

A bond graph model-based prognosis method for multiple components with unknown degradation patterns in a hybrid dynamical system is proposed. The traditional approach for remaining useful life prediction with single degradation model is inappropriate for hybrid systems where the dynamics changes according to operating mode. Therefore, multiple degradation models are suggested and these are adapted with new information of the degradation states of the monitored system. Sensitivity-based dynamic signature matrix is utilized for degradation hypothesis generation which provides the deviation directions of fewer hypothesized degradation parameters and thereby accelerates parameter and degradation trend estimation. The results are supported by experiments. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Hybrid dynamical system Dynamic degradation patterns Hybrid bond graph Analytical redundancy relations Sensitivity signatures constrained parameter estimation Adaptive prognosis

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Development of a recursive time series model for fed-batch mammalian cell culture

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COMPUTERS & CHEMICAL ENGINEERING 2018年 109卷 289-298页

作者： [Anonymous] Department of Chemical & Biological Engineering Illinois Institute of Technology Chicago IL 60616 United States

Recursive time series models are developed in this work for a fed-batch mammalian cell culture producing monoclonal antibodies, with key culture variables measured at different sampling frequencies. Glucose and glutamine feed rates are considered as inputs. A composite of an autoregressive moving average with exogenous input model and a dual rate-autoregressive with exogenous input model is used. Appropriate parameter constraints are imposed in parameter estimation algorithms and stability of these is examined and ensured. The data required for parameter estimation are generated from simulated fed-batch experiments using a well-tested first principles model. The predictions for glucose, glutamine, and viable cell concentrations track very well the data for these, with the errors for the high prediction horizons considered being limited to 10% or less. The prediction accuracy can be increased further if data from prior experiments with dynamic similarities are available. The models can be used reliably for model predictive control. (c) 2017 Elsevier Ltd. All rights reserved.

关键词： Mammalian cell culture Monoclonal antibody Recursive time series models constrained parameter estimation Stability

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Equivalence of Regression Curves

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2018年第522期113卷 711-729页

作者： Dette, Holger Moellenhoff, Kathrin Volgushev, Stanislav Bretz, Frank Ruhr Univ Bochum Fak Math D-44780 Bochum Germany Univ Toronto Dept Stat Sci Toronto ON Canada Univ Toronto Mississauga Dept Math & Computat Sci Mississauga ON Canada Novartis Pharma AG Basel Switzerland Shanghai Univ Finance & Econ Sch Stat & Management Shanghai Peoples R China

This article investigates the problem whether the difference between two parametric models m(1), m(2) describing the relation between a response variable and several covariates in two different groups is practically irrelevant, such that inference can be performed on the basis of the pooled sample. Statistical methodology is developed to test the hypotheses H-0: d(m(1), m(2)) >= epsilon versus H-1: d(m(1), m(2)) <= epsilon to demonstrate equivalence between the two regression curves m(1), m(2) for a prespecified threshold E, where d denotes a distance measuring the distance between m(1) and m(2). Our approach is based on the asymptotic properties of a suitable estimator d (m(1)) over cap, (m(2)) over cap of this distance. To improve the approximation of the nominal level for small sample sizes, a bootstrap test is developed, which addresses the specific form of the interval hypotheses. In particular, data have to be generated under the null hypothesis, which implicitly defines a manifold for the parameter vector. The results are illustrated by means of a simulation study and a data example. It is demonstrated that the new methods substantially improve currently available approaches with respect to power and approximation of the nominal level.

关键词： constrained parameter estimation Equivalence of curves Keywords and phrases: Dose-response studies Nonlinear regression Parametric bootstrap

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LIMITATIONS OF constrained CRB AND AN ALTERNATIVE BOUND 20

LIMITATIONS OF CONSTRAINED CRB AND AN ALTERNATIVE BOUND

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IEEE Statistical Signal Processing Workshop (SSP)

作者： Nitzan, Eyal Routtenberg, Tirza Tabrikian, Joseph Ben Gurion Univ Negev Dept Elect & Comp Engn IL-84105 Beer Sheva Israel

ISBN: (纸本)9781538615713

The constrained Cramer-Rao bound (CCRB) is a mean-squared-error (MSE) lower bound for non-Bayesian constrained parameter estimation under some unbiasedness conditions. In this paper, we demonstrate limitations of this bound in the case of nonlinear parametric constraints. We consider the problem of constant modulus signal estimation. It is shown that in this problem the CCRB unbiasedness conditions are too restrictive and that the commonly-used constrained maximum likelihood (CML) estimator does not satisfy them and has lower MSE than the CCRB. An alternative lower bound, which is based on the Lehmann-unbiasedness conditions, is used as an alternative benchmark for constrained parameter estimation. As opposed to the CCRB, it is shown that this alternative bound is valid for the CML estimator in the considered problem.

关键词： constrained Cramer-Rao bound (CCRB) mean-squared-error (MSE) constrained parameter estimation constant modulus signal estimation Lehmann-unbiasedness

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A unified framework for estimating parameters of kinetic biological models

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BMC BIOINFORMATICS 2015年第1期16卷 1-21页

作者： Baker, Syed Murtuza Poskar, C. Hart Schreiber, Falk Junker, Bjoern H. Univ Manchester Manchester Inst Biotechnol Manchester Lancs England Leibniz Inst Plant Genet & Crop Plant Res IPK Syst Biol Grp Gatersleben Germany Univ Halle Wittenberg Inst Pharm D-06108 Halle Germany Monash Univ Clayton Sch Informat Technol Clayton Vic Australia Univ Halle Wittenberg Inst Comp Sci D-06108 Halle Germany

Background: Utilizing kinetic models of biological systems commonly require computational approaches to estimate parameters, posing a variety of challenges due to their highly non-linear and dynamic nature, which is further complicated by the issue of non-identifiability. We propose a novel parameter estimation framework by combining approaches for solving identifiability with a recently introduced filtering technique that can uniquely estimate parameters where conventional methods fail. This framework first conducts a thorough analysis to identify and classify the non-identifiable parameters and provides a guideline for solving them. If no feasible solution can be found, the framework instead initializes the filtering technique with informed prior to yield a unique solution. Results: This framework has been applied to uniquely estimate parameter values for the sucrose accumulation model in sugarcane culm tissue and a gene regulatory network. In the first experiment the results show the progression of improvement in reliable and unique parameter estimation through the use of each tool to reduce and remove non-identifiability. The latter experiment illustrates the common situation where no further measurement data is available to solve the non-identifiability. These results show the successful application of the informed prior as well as the ease with which parallel data sources may be utilized without increasing the model complexity. Conclusion: The proposed unified framework is distinct from other approaches by providing a robust and complete solution which yields reliable and unique parameter estimation even in the face of non-identifiability.

关键词： constrained parameter estimation Identifiability analysis Kalman filter Kinetic models parameter estimation framework

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