An efficient estimation algorithm for the model parameters of robotic manipulators

1989 ◽  
Vol 5 (3) ◽  
pp. 386-394 ◽  
Author(s):  
I.-J. Ha ◽  
M.-S. Ko ◽  
S.K. Kwon
Keyword(s):  
Estimation Algorithm ◽  
Robotic Manipulators ◽  
Efficient Estimation ◽  
Model Parameters

An Energy Efficient Estimation Algorithm for Wireless Sensor Network

2011 ◽  
Vol 216 ◽  
pp. 176-180
Author(s):  
Yong Ding ◽  
Yue Mei Su
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Estimation Algorithm ◽  
Efficient Estimation ◽  
Wireless Sensor ◽  
Estimation Algorithms ◽  
Current Energy

Wireless Sensor Networks functionality is closely related to network lifetime which depends on the energy consumption, so require energy- efficient protocols to improve the network lifetime. According to the analysis and summary of the current energy efficient estimation algorithms in wireless sensor network An energy-efficient algorithm is proposed,. Then this optimization algorithm proposed in the paper is adopted to improve the traditional diffusion routing protocol. Simulation results show that this algorithm is to effectively balance the network energy consumption, improve the network life-cycle and ensure the communication quality.

scholarly journals A Fuzzy State-of-Charge Estimation Algorithm Combining Ampere-Hour and an Extended Kalman Filter for Li-Ion Batteries Based on Multi-Model Global Identification

2018 ◽  
Vol 8 (11) ◽  
pp. 2028 ◽  
Author(s):  
Xin Lai ◽  
Dongdong Qiao ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Algorithm ◽  
State Of Charge ◽  
Distribution Area ◽  
Model Parameters ◽  
Model Accuracy ◽  
Soc Estimation ◽  
Fuzzy Fusion ◽  
Fuzzy State

The popular and widely reported lithium-ion battery model is the equivalent circuit model (ECM). The suitable ECM structure and matched model parameters are equally important for the state-of-charge (SOC) estimation algorithm. This paper focuses on high-accuracy models and the estimation algorithm with high robustness and accuracy in practical application. Firstly, five ECMs and five parameter identification approaches are compared under the New European Driving Cycle (NEDC) working condition in the whole SOC area, and the most appropriate model structure and its parameters are determined to improve model accuracy. Based on this, a multi-model and multi-algorithm (MM-MA) method, considering the SOC distribution area, is proposed. The experimental results show that this method can effectively improve the model accuracy. Secondly, a fuzzy fusion SOC estimation algorithm, based on the extended Kalman filter (EKF) and ampere-hour counting (AH) method, is proposed. The fuzzy fusion algorithm takes advantage of the advantages of EKF, and AH avoids the weaknesses. Six case studies show that the SOC estimation result can hold the satisfactory accuracy even when large sensor and model errors exist.

“Sintering” Models and In-Situ Experiments: Data Assimilation for Microstructure Prediction in SLS Additive Manufacturing of Nylon Components

MRS Advances ◽  
2020 ◽  
Vol 5 (29-30) ◽  
pp. 1593-1601
Author(s):  
W. Steven Rosenthal ◽  
Francesca C. Grogan ◽  
Yulan Li ◽  
Erin I. Barker ◽  
Josef F. Christ ◽  
...  
Keyword(s):  
Adaptive Sampling ◽  
Selective Laser Sintering ◽  
Phase Field Model ◽  
Estimation Algorithm ◽  
Model Parameters ◽  
Multiple Data ◽  
In Situ Experiments ◽  
Parameter Estimation Algorithm ◽  
Microstructure Prediction

ABSTRACTSelective laser sintering methods are workhorses for additively manufacturing polymer-based components. The ease of rapid prototyping also means it is easy to produce illicit components. It is necessary to have a data-calibrated in-situ physical model of the build process in order to predict expected and defective microstructure characteristics that inform component provenance. Toward this end, sintering models are calibrated and characteristics such as component defects are explored. This is accomplished by assimilating multiple data streams, imaging analysis, and computational model predictions in an adaptive Bayesian parameter estimation algorithm. From these data sources, along with a phase-field model, bulk porosity distributions are inferred. Model parameters are constrained to physically-relevant search directions by sensitivity analysis, and then matched to predictions using adaptive sampling. Using this feedback loop, data-constrained estimates of sintering model parameters along with uncertainty bounds are obtained.

scholarly journals Rotor flux oriented control of induction machine based drives with compensation for the variation of all machine parameters

2013 ◽  
Vol 61 (2) ◽  
pp. 309-324 ◽  
Author(s):  
G. Extremiana ◽  
G. Abad ◽  
J. Arza ◽  
J. Chivite-Zabalza ◽  
I. Torre
Keyword(s):  
Dynamic Performance ◽  
Estimation Algorithm ◽  
Model Parameters ◽  
Electrical Machine ◽  
Saturation Curve ◽  
Machine Model ◽  
Rotor Resistance ◽  
Iron Losses ◽  
Rotor Flux ◽  
On Line

Abstract The performance of rotor flux oriented induction motor drives, widely used these days, relies on the accurate knowledge of key machine parameters. In most industrial drives, the rotor resistance, subject to temperature variations, is estimated on-line due to its significant influence on the control behaviour. However, the rest of the model parameters are also subject to slow variations, determined mainly by the operating point of the machine, compromising the dynamic performance and the accuracy of the torque estimation. This paper presents an improved rotor-resistance on-line estimation algorithm that contemplates the iron losses of the electrical machine, the iron saturation curve and the mechanical losses. In addition, the control also compensates the rest of the key machine parameters such as the leakage and magnetizing inductances and the iron losses. These parameters are measured by an off-line estimation procedure and stored in look up-tables used by the control. The paper begins by presenting the machine model and the proposed rotor flux oriented control strategy. Subsequently, the off-line parameter measurement procedure is described. Finally, the algorithm is extensively evaluated and validated experimentally on a 15 kW test bench

scholarly journals Conditional Analysis for Mixed Covariates, with Application to Feed Intake of Lactating Sows

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
S. Y. Park ◽  
C. Li ◽  
S. M. Mendoza Benavides ◽  
E. van Heugten ◽  
A. M. Staicu
Keyword(s):  
Feed Intake ◽  
Dynamic Change ◽  
Estimation Algorithm ◽  
Joint Estimation ◽  
Efficient Estimation ◽  
Specific Information ◽  
Computationally Efficient ◽  
Modeling Framework ◽  
Lactating Sows ◽  
Information Vector

We propose a novel modeling framework to study the effect of covariates of various types on the conditional distribution of the response. The methodology accommodates flexible model structure, allows for joint estimation of the quantiles at all levels, and provides a computationally efficient estimation algorithm. Extensive numerical investigation confirms good performance of the proposed method. The methodology is motivated by and applied to a lactating sow study, where the primary interest is to understand how the dynamic change of minute-by-minute temperature in the farrowing rooms within a day (functional covariate) is associated with low quantiles of feed intake of lactating sows, while accounting for other sow-specific information (vector covariate).

Comparison of three different statistical approaches (non-linear least-squares regression, survival analysis and Bayesian inference) in their usefulness for estimating hydrothermal time models of seed germination

2020 ◽  
Vol 30 (1) ◽  
pp. 64-72 ◽  
Author(s):  
Elena Moltchanova ◽  
Shirin Sharifiamina ◽  
Derrick J. Moot ◽  
Ali Shayanfar ◽  
Mark Bloomberg
Keyword(s):  
Seed Germination ◽  
Linear Regression ◽  
Least Squares ◽  
Time Course ◽  
Efficient Estimation ◽  
Model Parameters ◽  
Least Squares Regression ◽  
Hydrothermal Time ◽  
Linear Least Squares ◽  
Non Linear

AbstractHydrothermal time (HTT) models describe the time course of seed germination for a population of seeds under specific temperature and water potential conditions. The parameters of the HTT model are usually estimated using either a linear regression, non-linear least squares estimation or a generalized linear regression model. There are problems with these approaches, including loss of information, and censoring and lack of independence in the germination data. Model estimation may require optimization, and this can have a heavy computational burden. Here, we compare non-linear regression with survival and Bayesian methods, to estimate HTT models for germination of two clover species. All three methods estimated similar HTT model parameters with similar root mean squared errors. However, the Bayesian approach allowed (1) efficient estimation of model parameters without the need for computation-intensive methods and (2) easy comparison of HTT parameters for the two clover species. HTT models that accounted for a species effect were superior to those that did not. Inspection of credibility intervals and estimated posterior distributions for the Bayesian HTT model shows that it is credible that most HTT model parameters were different for the two clover species, and these differences were consistent with known biological differences between species in their germination behaviour.

Identification of Certain Polynomial Nonlinear Structures by Adaptive Selectively-Sensitive Excitation

1993 ◽  
Vol 115 (3) ◽  
pp. 246-255 ◽  
Author(s):  
Y. Ben-Haim
Keyword(s):  
Vibrational Excitation ◽  
Model Identification ◽  
Elastic Beam ◽  
Estimation Procedure ◽  
Estimation Algorithm ◽  
Model Parameters ◽  
Nonlinear Part ◽  
Precise Knowledge ◽  
Low Dimensional ◽  
Selective Sensitivity

This paper presents a method for identification of certain polynomial nonlinear dynamic systems by adaptive vibrational excitation. The identification is based on the concept of selective sensitivity and is implemented by an adaptive multihypothesis estimation algorithm. The central problem addressed by this method is reduction of the dimensionality of the space in which the model identification is performed. The method of selective sensitivity allows one to design an excitation which causes the response to be selectively sensitive to a small set of model parameters and insensitive to all the remaining model parameters. The identification of the entire system thus becomes a sequence of low-dimensional estimation problems. The dynamical system is modelled as containing both a linear and a nonlinear part. The estimation procedure presumes precise knowledge of the linear model and knowledge of the structure, though not the parameter values, of the nonlinear part of the model. The theory is developed for three different polynomial forms of the nonlinear model: quadratic, cubic and hybrid polynomial nonlinearities. The estimation procedure is illustrated through simulated identification of quadratic nonlinearities in the small-angle vibrations of a uniform elastic beam.

ON CONSTRUCTION OF STOCHASTIC GENETIC NETWORKS BASED ON GENE EXPRESSION SEQUENCES

2005 ◽  
Vol 15 (04) ◽  
pp. 297-310 ◽  
Author(s):  
WAI-KI CHING ◽  
MICHAEL M. NG ◽  
ERIC S. FUNG ◽  
TATSUYA AKUTSU
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Boolean Networks ◽  
Genetic Regulatory Networks ◽  
Efficient Estimation ◽  
Estimation Methods ◽  
Series Data ◽  
Model Parameters ◽  
Proposed Model

Reconstruction of genetic regulatory networks from time series data of gene expression patterns is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been proposed as an effective model for gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and discover the sensitivity of genes in their interactions with other genes. However, PBNs are unlikely to use directly in practice because of huge amount of computational cost for obtaining predictors and their corresponding probabilities. In this paper, we propose a multivariate Markov model for approximating PBNs and describing the dynamics of a genetic network for gene expression sequences. The main contribution of the new model is to preserve the strength of PBNs and reduce the complexity of the networks. The number of parameters of our proposed model is O(n2) where n is the number of genes involved. We also develop efficient estimation methods for solving the model parameters. Numerical examples on synthetic data sets and practical yeast data sequences are given to demonstrate the effectiveness of the proposed model.

scholarly journals Cranking Capability Estimation Algorithm Based on Modeling and Online Update of Model Parameters for Li-Ion SLI Batteries

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3365
Author(s):  
Tae-Won Noh ◽  
Jung-Hoon Ahn ◽  
Byoung Kuk Lee
Keyword(s):  
Equivalent Circuit ◽  
Voltage Drop ◽  
Equivalent Circuit Model ◽  
Estimation Algorithm ◽  
Circuit Model ◽  
Estimation Accuracy ◽  
Voltage Response ◽  
Model Parameters ◽  
Voltage Range ◽  
Terminal Voltage

The terminal voltage of a starting–lighting–ignition (SLI) battery can decrease to a value lower than the allowable voltage range because of the high discharge current required to crank the engine of a vehicle. To avoid the safety problems generated by this voltage drop, this paper proposes a cranking capability estimation algorithm. The proposed algorithm includes an equivalent circuit model for describing the instantaneous voltage response to the cranking current profile. This algorithm predicts the minimum value of the terminal voltage for the cranking transient period by analyzing the polarization voltage and dynamic characteristic of the equivalent circuit model. The estimation accuracy is adjusted by an online update for the parameters of the equivalent circuit model, which varies with temperature, aging, and other factors. The proposed algorithm was validated by experiments with a 60Ah LiFePO4-type SLI battery.

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