scholarly journals A recursive estimation algorithm to track aircraft model parameters

2016 ◽  
Author(s):  
G. Hardier ◽  
G. Ferreres ◽  
C. Seren
Keyword(s):  
Estimation Algorithm ◽  
Recursive Estimation ◽  
Model Parameters ◽  
Aircraft Model

scholarly journals Dual-Input Slope Seeking Control of Continuous Micro-Algae Cultures with Experimental Validation

2021 ◽  
Vol 11 (16) ◽  
pp. 7451
Author(s):  
Christian Feudjio Letchindjio ◽  
Jesús Zamudio Lara ◽  
Laurent Dewasme ◽  
Héctor Hernández Escoto ◽  
Alain Vande Wouwer
Keyword(s):  
Performance Index ◽  
Degrees Of Freedom ◽  
Incident Light ◽  
Experimental Studies ◽  
Estimation Algorithm ◽  
Recursive Estimation ◽  
Hammerstein Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Actual Process

This paper investigates the application of adaptive slope-seeking strategies to dual-input single output dynamic processes. While the classical objective of extremum seeking control is to drive a process performance index to its optimum, this paper also considers slope seeking, which allows driving the performance index to a desired level (which is thus sub-optimal). Moreover, the consideration of more than one input signal allows minimizing the input energy thanks to the degrees of freedom offered by the additional inputs. The actual process is assumed to be locally approachable by a Hammerstein model, combining a nonlinear static map with a linear dynamic model. The proposed strategy is based on the interplay of three components: (i) a recursive estimation algorithm providing the model parameters and the performance index gradient, (ii) a slope generator using the static map parameter estimates to convert the performance index setpoint into slope setpoints, and (iii) an adaptive controller driving the process to the desired setpoint. The performance of the slope strategy is assessed in simulation in an application example related to lipid productivity optimization in continuous cultures of micro-algae by acting on both the incident light intensity and the dilution rate. It is also validated in experimental studies where biomass production in a continuous photo-bioreactor is targeted.

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

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.

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