scholarly journals A Two-Stage Method for Parameter Identification of a Nonlinear System in a Microbial Batch Process

2019 ◽  
Vol 9 (2) ◽  
pp. 337
Author(s):  
Gongxian Xu ◽  
Dongxue Lv ◽  
Wenxin Tan
Keyword(s):  
Parameter Identification ◽  
Dynamic Optimization ◽  
Optimization Problem ◽  
Batch Process ◽  
Single Stage ◽  
Least Square ◽  
Model Parameters ◽  
Dynamic Optimization Problem ◽  
Concentration Data ◽  
Two Stage

This paper deals with the parameter identification of a microbial batch process of glycerol to 1,3-propanediol (1,3-PD). We first present a parameter identification model for the excess kinetics of a microbial batch process of glycerol to 1,3-PD. This model is a nonlinear dynamic optimization problem that minimizes the sum of the least-square and slope errors of biomass, glycerol, 1,3-PD, acetic acid, and ethanol. Then, a two-stage method is proposed to efficiently solve the presented dynamic optimization problem. In this method, two nonlinear programming problems are required to be solved by a genetic algorithm. To calculate the slope of the experimental concentration data, an integral equation of the first kind is solved by using the Tikhonov regularization. The proposed two-stage method could not only optimally identify the model parameters of the biological process, but could also yield a smaller error between the measured and computed concentrations than the single-stage method could, with a decrease of about 52.79%. A comparative study showed that the proposed two-stage method could obtain better identification results than the single-stage method could.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

scholarly journals Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Yuan Chen ◽  
Feng Xiao ◽  
Silun Peng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Life ◽  
Model Parameters ◽  
Simulation Results

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

scholarly journals Tracking Moving Optima Using Kalman-Based Predictions

2008 ◽  
Vol 16 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Claudio Rossi ◽  
Mohamed Abderrahim ◽  
Julio César Díaz
Keyword(s):  
Evolutionary Algorithms ◽  
Dynamic Optimization ◽  
Optimization Problem ◽  
Moving Objects ◽  
Fitness Landscape ◽  
Experimental Comparison ◽  
Motion Information ◽  
Dynamic Optimization Problem ◽  
First Order ◽  
Robotic Application

The dynamic optimization problem concerns finding an optimum in a changing environment. In the field of evolutionary algorithms, this implies dealing with a time-changing fitness landscape. In this paper we compare different techniques for integrating motion information into an evolutionary algorithm, in the case it has to follow a time-changing optimum, under the assumption that the changes follow a nonrandom law. Such a law can be estimated in order to improve the optimum tracking capabilities of the algorithm. In particular, we will focus on first order dynamical laws to track moving objects. A vision-based tracking robotic application is used as testbed for experimental comparison.

A Moving Horizon Approach to a Chance Constrained Nonlinear Dynamic Optimization Problem*

2009 ◽  
Vol 42 (2) ◽  
pp. 103-107
Author(s):  
Abebe Geletu ◽  
Michael Klöppel ◽  
Pu Li ◽  
Armin Hoffmann
Keyword(s):  
Dynamic Optimization ◽  
Nonlinear Dynamic ◽  
Optimization Problem ◽  
Dynamic Optimization Problem

Cross-country sailplane flight as a dynamic optimization problem

1978 ◽  
Vol 12 (11) ◽  
pp. 1743-1759 ◽  
Author(s):  
Bion L. Pierson ◽  
Jan L. De Jong
Keyword(s):  
Dynamic Optimization ◽  
Optimization Problem ◽  
Dynamic Optimization Problem ◽  
Cross Country

scholarly journals Swarm algorithms in dynamic optimization problem of reactive power compensation units control

2019 ◽  
Vol 9 (5) ◽  
pp. 3967
Author(s):  
V.Z Manusov ◽  
P.V. Matrenin ◽  
N. Khasanzoda
Keyword(s):  
Dynamic Optimization ◽  
Power Supply ◽  
Optimization Problem ◽  
Power Supply System ◽  
Reactive Power ◽  
Reactive Power Compensation ◽  
Supply System ◽  
Bees Algorithm ◽  
Dynamic Optimization Problem ◽  
Engineering Research

Optimization of a power supply system is one of the main directions in power engineering research. The reactive power compensation reduces active power losses in transmission lines. In general, researches devoted to allocation and control of the compensation units consider this issue as a static optimization problem. However, it is dynamic and stochastic optimization problem that requires a real-time solution. To solve the dynamic optimization NP-hard problem, it is advisable to use Swarm Intelligence. This research deals with the problem of the compensation units power control as a dynamic optimization problem, considering the possible stochastic failures of the compensation units. The Particle Swarm Optimization and the Bees Algorithm were applied to solve it to compare the effectiveness of these algorithms in the dynamic optimization of a power supply system.

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