scholarly journals Parameter Identification of Inverter-Fed Induction Motors: A Review

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2194 ◽  
Author(s):  
Jing Tang ◽  
Yongheng Yang ◽  
Frede Blaabjerg ◽  
Jie Chen ◽  
Lijun Diao ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Induction Motor ◽  
High Performance ◽  
Least Square ◽  
Recursive Least Square ◽  
Identification Methods ◽  
Voltage Signal ◽  
High Frequency Ac ◽  
Identification Techniques ◽  
Model Reference Adaptive

Induction motor parameters are essential for high-performance control. However, motor parameters vary because of winding temperature rise, skin effect, and flux saturation. Mismatched parameters will consequently lead to motor performance degradation. To provide accurate motor parameters, in this paper, a comprehensive review of offline and online identification methods is presented. In the implementation of offline identification, either a DC voltage or single-phase AC voltage signal is injected to keep the induction motor standstill, and the corresponding identification algorithms are discussed in the paper. Moreover, the online parameter identification methods are illustrated, including the recursive least square, model reference adaptive system, DC and high-frequency AC voltage injection, and observer-based techniques, etc. Simulations on selected identification techniques applied to an example induction motor are presented to demonstrate their performance and exemplify the parameter identification methods.

Parameter Identification of Spatial–Temporal Varying Processes by a Multi-Robot System in Realistic Diffusion Fields

Robotica ◽  
2020 ◽  
pp. 1-20 ◽  
Author(s):  
Wencen Wu ◽  
Jie You ◽  
Yufei Zhang ◽  
Mingchen Li ◽  
Kun Su
Keyword(s):  
Parameter Identification ◽  
Mobile Robots ◽  
Sensor Network ◽  
Nonlinear Partial Differential Equation ◽  
Least Square ◽  
Diffusion Field ◽  
Recursive Least Square ◽  
Robot System ◽  
Parameterized Model ◽  
Multi Robot

SUMMARY In this article, we investigate the problem of parameter identification of spatial–temporal varying processes described by a general nonlinear partial differential equation and validate the feasibility and robustness of the proposed algorithm using a group of coordinated mobile robots equipped with sensors in a realistic diffusion field. Based on the online parameter identification method developed in our previous work using multiple mobile robots, in this article, we first develop a parameterized model that represents the nonlinear spatially distributed field, then develop a parameter identification scheme consisting of a cooperative Kalman filter and recursive least square method. In the experiments, we focus on the diffusion field and consider the realistic scenarios that the diffusion field contains obstacles and hazard zones that the robots should avoid. The identified parameters together with the located source could potentially assist in the reconstruction and monitoring of the field. To validate the proposed methods, we generate a controllable carbon dioxide (CO2) field in our laboratory and build a static CO2 sensor network to measure and calibrate the field. With the reconstructed realistic diffusion field measured by the sensor network, a multi-robot system is developed to perform the parameter identification in the field. The results of simulations and experiments show satisfactory performance and robustness of the proposed algorithms.

EVALUATION OF DISCRETE ADAPTIVE FAIL-SAFE ACTIVE SUSPENSION

1997 ◽  
Vol 21 (3) ◽  
pp. 205-220 ◽  
Author(s):  
R.V. Dukkipati ◽  
S.S. Vallurupalli ◽  
M.O.M. Osman
Keyword(s):  
Adaptive Control ◽  
Active Suspension ◽  
Full Scale ◽  
Least Square ◽  
Recursive Least Square ◽  
Single Degree Of Freedom ◽  
Computer Controlled ◽  
Fail Safe ◽  
Model Reference Adaptive ◽  
First Time

Hardware implementation of discrete adaptive control for a full scale vehicular single degree of freedom (SDOF) active suspension has been discussed in this paper. This paper describes an experimental evaluation of full scale fail-safe adaptive active (SDOF) suspension system that has been performed for the first time. A servo hydraulic force actuator is installed along with passive suspension components to form a fail-safe active suspension. A discrete model reference adaptive control (DMRAC) approach with recursive least square (RLS) estimation and covariance modification has been used for the software/hardware based digital control. A real time computer controlled adaptive active suspension software which shows the experimental response and animation of the results has been developed.

scholarly journals Structural Dynamic Load and Parameter Identification Based on Dummy Measurements of Displacement

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ting Wang ◽  
Zhimin Wan ◽  
Weiguang Zheng ◽  
Shuilong He
Keyword(s):  
Parameter Identification ◽  
Structural Dynamics ◽  
Information Integration ◽  
Integration Method ◽  
Covariance Matrices ◽  
Least Square ◽  
Structural Dynamic ◽  
Identification Methods ◽  
Uncertain Structure ◽  
Previous Identification

Structural load and parameter identification are the essential contents in the field of structural dynamics, and some studies pay attention to the coupled recognition of uncertain structure parameters as well as unacquainted loads. Gillijns and De Moor presented the Kalman-type filter, which was used in the work for coupled identification as the unabbreviated form of GDF. However, it has been demonstrated that it is unstable for the extended GDF (EGDF) method, drifting in the identified unacquainted loads as well as displacement, just like most previous identification methods based on the least-square algorithm. In order to deal with this unstable issue, this paper applied the dummy measurements of displacements on a position level and modifies the EGDF algorithm using the information integration method about the accelerated measurements with dummy measurements. Numerical example of a truss is used for validating the applicability of the method in the work, and an influence of covariance matrices in dummy displacements is also considered.

scholarly journals Online Parameter Identification of Lithium-Ion Batteries Using a Novel Multiple Forgetting Factor Recursive Least Square Algorithm

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3180 ◽  
Author(s):  
Bizhong Xia ◽  
Rui Huang ◽  
Zizhou Lao ◽  
Ruifeng Zhang ◽  
Yongzhi Lai ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Lithium Ion ◽  
Least Square ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Temperature State

The model parameters of the lithium-ion battery are of great importance to model-based battery state estimation methods. The fact that parameters change in different rates with operation temperature, state of charge (SOC), state of health (SOH) and other factors calls for an online parameter identification algorithm that can track different dynamic characters of the parameters. In this paper, a novel multiple forgetting factor recursive least square (MFFRLS) algorithm was proposed. Forgetting factors were assigned to each parameter, allowing the algorithm to capture the different dynamics of the parameters. Particle swarm optimization (PSO) was utilized to determine the optimal forgetting factors. A state of the art SOC estimator, known as the unscented Kalman filter (UKF), was combined with the online parameter identification to create an accurate estimation of SOC. The effectiveness of the proposed method was verified through a driving cycle under constant temperature and three different driving cycles under varied temperature. The single forgetting factor recursive least square (SFFRLS)-UKF and UKF with fixed parameter were also tested for comparison. The proposed MFFRLS-UKF method obtained an accurate estimation of SOC especially when the battery was running in an environment of changing temperature.

A UAV MODEL PARAMETER IDENTIFICATION METHOD: A SIMULATION STUDY

2009 ◽  
Vol 06 (04) ◽  
pp. 225-238 ◽  
Author(s):  
K. S. HATAMLEH ◽  
O. MA ◽  
R. PAZ
Keyword(s):  
Parameter Identification ◽  
Control Strategies ◽  
Least Square ◽  
Model Parameters ◽  
Dynamics Modeling ◽  
Recursive Least Square ◽  
Nonlinear Dynamical ◽  
Model Parameter ◽  
Identification Method ◽  
Model Parameter Identification

Dynamics modeling of Unmanned Aerial Vehicles (UAVs) is an essential step for design and evaluation of an UAV system. Many advanced control strategies for nonlinear dynamical or robotic systems which are applicable to UAVs depend upon known dynamics models. The accuracy of a model depends not only on the mathematical formulae or computational algorithm of the model but also on the values of model parameters. Many model parameters are very difficult to measure for a given UAV. This paper presents the results of a simulation based study of an in-flight model parameter identification method. Assuming the motion state of a flying UAV is directly or indirectly measureable, the method can identify the unknown inertia parameters of the UAV. Using the recursive least-square technique, the method is capable of updating the model parameters of the UAV while the vehicle is in flight. A scheme of estimating an upper bound of the identification error in terms of the input data errors (or sensor errors) is also discussed.

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