scholarly journals Distributed Finite-Time State Estimation of Interconnected Complex Metabolic Networks

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-20
Author(s):  
Alfonso Sepulveda-Galvez ◽  
Jesus A. Badillo-Corona ◽  
Isaac Chairez
Keyword(s):  
Escherichia Coli ◽  
State Estimation ◽  
Finite Time ◽  
Metabolic Networks ◽  
Complex Structure ◽  
High Gain ◽  
Observer Design ◽  
Superior Performance ◽  
State Observers ◽  
Metabolic Behavior

A set of distributed robust finite-time state observers was developed and tested to estimate the main biochemical substances in interconnected metabolic networks with complex structure. The finite-time estimator was designed by composing several supertwisting based step-by-step state observers. This segmented structure was proposed accordingly to the partition of metabolic network obtained as a result of applying the observability analysis of the model used to represent metabolic networks. The observer was developed under the assumption that a sufficient and small number of intracellular compounds can be obtained by some feasible analytic techniques. These techniques are enlisted to demonstrate the feasibility of designing the proposed observer. A set of numerical simulations was proposed to test the observer design over the hydrogen producing metabolic behavior of Escherichia coli. The numerical evaluations showed the superior performance of the observer (on recovering immeasurable state values) over classical approaches (high gain). The variations of internal metabolites inserted in the hydrogen productive metabolic networks were collected from databases. This information supplied to the observer served to validate its ability to recover the time evolution of nonmeasurable metabolites.

Observer Design for State Estimation of UV Curing Processes

2014 ◽  
Author(s):  
Adamu Yebi ◽  
Beshah Ayalew ◽  
Satadru Dey
Keyword(s):  
State Estimation ◽  
Online Monitoring ◽  
Uv Curing ◽  
Observer Design ◽  
Nonlinear Observer ◽  
Advanced Manufacturing ◽  
Monitoring And Control ◽  
State Observers ◽  
State Measurement ◽  
And Control

This article discusses the challenges of non-intrusive state measurement for the purposes of online monitoring and control of Ultraviolet (UV) curing processes. It then proposes a two-step observer design scheme involving the estimation of distributed temperature from boundary sensing cascaded with nonlinear cure state observers. For the temperature observer, backstepping techniques are applied to derive the observer partial differential equations along with the gain kernels. For subsequent cure state estimation, a nonlinear observer is derived along with analysis of its convergence characteristics. While illustrative simulation results are included for a composite laminate curing application, it is apparent that the approach can also be adopted for other UV processing applications in advanced manufacturing.

scholarly journals Model-Based Observer Design Considering Unequal Measurement Delays

Actuators ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 281
Author(s):  
Yousef Alipouri ◽  
Lexuan Zhong
Keyword(s):  
State Estimation ◽  
Time Delays ◽  
Observer Design ◽  
Estimation Methods ◽  
Simulation Studies ◽  
State Observers ◽  
Essential Components ◽  
Continuous Stirred Tank ◽  
A Chain ◽  
Communication Lines

State observers are essential components of a modern control system. It is often designed based on a mathematical model of the process, thus requiring detailed process knowledge. However, in the existing state estimation methods, equal delays are commonly assumed for all communication lines, which is unrealistic and poses problems such as instability and a degraded performance of observers when unequal time delays exist. In this paper, a design of observers considering the measurement delays is presented. To deal with this problem, a chain-based observer has been proposed in which each chain deals with one output delay, performs prediction for the unavailable output value, and passes it to the next chain. Convergence of each chain observer as well as overall state estimation were proven. To illustrate the performance of the proposed scheme, simulation studies were performed on a benchmark continuous stirred tank heater (CSTH) process.

scholarly journals Robust State Estimation for a Nonlinear Hybrid Model of the Alternating Activated Sludge Process Using Filtered High-Gain Observers

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Afef Boudagga ◽  
Habib Dimassi ◽  
Salim Hadj-Said ◽  
Faouzi M’Sahli
Keyword(s):  
Activated Sludge ◽  
State Estimation ◽  
Estimation Method ◽  
High Gain ◽  
Observer Design ◽  
Measurement Noise ◽  
Observation Error ◽  
Activated Sludge Process ◽  
High Gain Observer ◽  
Nonlinear Hybrid

In this paper, a robust state estimation method based on a filtered high-gain observer is developed for the alternating activated sludge process (AASP) considered as a nonlinear hybrid system. Indeed, we assume that the biodegradable substrate and the ammonia concentrations in the AASP model are unmeasured due to the high cost of their sensors whose maintenance is also very expensive. The observer design is based on the association of the classical high-gain observer and the idea of the application of linear filters on the observation error to deal with measurement noise. It is shown through a Lyapunov analysis that the designed observer ensures the estimation of the unmeasured states (the biodegradable substrate and the ammonia concentrations) based on the measured dissolved oxygen and nitrate concentrations subject to noise. A comparison with the classical high-gain observer is performed via numerical simulations in order to show the robustness of the suggested estimation approach against Gaussian measurement noise.

scholarly journals Finite-Time State Estimation for an Inverted Pendulum under Input-Multiplicative Uncertainty

Robotics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 87
Author(s):  
Anu Kossery Jayaprakash ◽  
Krishna Bhavithavya Kidambi ◽  
William MacKunis ◽  
Sergey V. Drakunov ◽  
Mahmut Reyhanoglu
Keyword(s):  
State Estimation ◽  
Control Systems ◽  
Finite Time ◽  
Inverted Pendulum ◽  
Sliding Mode ◽  
Parametric Uncertainty ◽  
Sliding Mode Observer ◽  
Observer Design ◽  
Rigorous Analysis ◽  
Inverted Pendulum System

A sliding mode observer is presented, which is rigorously proven to achieve finite-time state estimation of a dual-parallel underactuated (i.e., single-input multi-output) cart inverted pendulum system in the presence of parametric uncertainty. A salient feature of the proposed sliding mode observer design is that a rigorous analysis is provided, which proves finite-time estimation of the complete system state in the presence of input-multiplicative parametric uncertainty. The performance of the proposed observer design is demonstrated through numerical case studies using both sliding mode control (SMC)- and linear quadratic regulator (LQR)-based closed-loop control systems. The main contribution presented here is the rigorous analysis of the finite-time state estimator under input-multiplicative parametric uncertainty in addition to a comparative numerical study that quantifies the performance improvement that is achieved by formally incorporating the proposed compensator for input-multiplicative parametric uncertainty in the observer. In summary, our results show performance improvements when applied to both SMC- and LQR-based control systems, with results that include a reduction in the root-mean square error of up to 39% in translational regulation control and a reduction of up to 29% in pendulum angular control.

Asynchronous finite-time state estimation for semi-Markovian jump neural networks with randomly occurred sensor nonlinearities

2021 ◽  
Vol 432 ◽  
pp. 240-249
Author(s):  
Yao Wang ◽  
Shengyuan Xu ◽  
Yongmin Li ◽  
Yuming Chu ◽  
Zhengqiang Zhang
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Finite Time ◽  
Markovian Jump ◽  
Markovian Jump Neural Networks

scholarly journals Genome-scale target identification in Escherichia coli for high-titer production of free fatty acids

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lixia Fang ◽  
Jie Fan ◽  
Shulei Luo ◽  
Yaru Chen ◽  
Congya Wang ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Fatty Acids ◽  
Free Fatty Acids ◽  
Stress Responses ◽  
Metabolic Networks ◽  
Target Identification ◽  
High Titer ◽  
Cell Factory ◽  
Microbial Cell Factory ◽  
Ffa Metabolism

AbstractTo construct a superior microbial cell factory for chemical synthesis, a major challenge is to fully exploit cellular potential by identifying and engineering beneficial gene targets in sophisticated metabolic networks. Here, we take advantage of CRISPR interference (CRISPRi) and omics analyses to systematically identify beneficial genes that can be engineered to promote free fatty acids (FFAs) production in Escherichia coli. CRISPRi-mediated genetic perturbation enables the identification of 30 beneficial genes from 108 targets related to FFA metabolism. Then, omics analyses of the FFAs-overproducing strains and a control strain enable the identification of another 26 beneficial genes that are seemingly irrelevant to FFA metabolism. Combinatorial perturbation of four beneficial genes involving cellular stress responses results in a recombinant strain ihfAL−-aidB+-ryfAM−-gadAH−, producing 30.0 g L−1 FFAs in fed-batch fermentation, the maximum titer in E. coli reported to date. Our findings are of help in rewiring cellular metabolism and interwoven intracellular processes to facilitate high-titer production of biochemicals.

scholarly journals Robust Interval Observer Design for Fractional-Order Models with Applications to State Estimation of Batteries

2020 ◽  
Vol 53 (2) ◽  
pp. 3683-3688
Author(s):  
Erik Hildebrandt ◽  
Julia Kersten ◽  
Andreas Rauh ◽  
Harald Aschemann
Keyword(s):  
State Estimation ◽  
Fractional Order ◽  
Observer Design ◽  
Interval Observer

A Robust Control Design Approach Combining Exact Linearization and High-Gain PI-Observer

2007 ◽  
Author(s):  
Yan Liu ◽  
Dirk So¨ffker
Keyword(s):  
Feedback Linearization ◽  
Control Method ◽  
High Gain ◽  
Observer Design ◽  
Input State ◽  
Exact Linearization ◽  
Practical Applications ◽  
Robust Control Design ◽  
Nonlinear Control Method ◽  
Feedback Linearization Approach

This paper introduces a robust nonlinear control method combining classical feedback linearization and a high-gain PI-Observer (Proportional-Integral Observer) approach that can be applied to control a nonlinear single-input system with uncertainties or unknown effects. It is known that the lack of robustness of the feedback linearization approach limits its practical applications. The presented approach improves the robustness properties and extends the application area of the feedback linearization control. The approach is developed analytically and fully illustrated. An example which uses input-state linearization and PI-Observer design is given to illustrate the idea and to demonstrate the advantages.

scholarly journals Simultaneous Parameters Identifiability and Estimation of anE. coliMetabolic Network Model

2015 ◽  
Vol 2015 ◽  
pp. 1-21 ◽  
Author(s):  
Kese Pontes Freitas Alberton ◽  
André Luís Alberton ◽  
Jimena Andrea Di Maggio ◽  
Vanina Gisela Estrada ◽  
María Soledad Díaz ◽  
...  
Keyword(s):  
Experimental Data ◽  
Escherichia Coli ◽  
Metabolic Networks ◽  
Initial Conditions ◽  
Model Fit ◽  
Real Problem ◽  
Kinetic Rates ◽  
K 12 ◽  
Procedure Model

This work proposes a procedure for simultaneous parameters identifiability and estimation in metabolic networks in order to overcome difficulties associated with lack of experimental data and large number of parameters, a common scenario in the modeling of such systems. As case study, the complex real problem of parameters identifiability of theEscherichia coliK-12 W3110 dynamic model was investigated, composed by 18 differential ordinary equations and 35 kinetic rates, containing 125 parameters. With the procedure, model fit was improved for most of the measured metabolites, achieving 58 parameters estimated, including 5 unknown initial conditions. The results indicate that simultaneous parameters identifiability and estimation approach in metabolic networks is appealing, since model fit to the most of measured metabolites was possible even when important measures of intracellular metabolites and good initial estimates of parameters are not available.

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