scholarly journals Energy Modeling and Parameter Identification of Dual-Motor-Driven Belt Conveyors without Speed Sensors

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3313 ◽  
Author(s):  
Chunyu Yang ◽  
Jinhao Liu ◽  
Heng Li ◽  
Linna Zhou
Keyword(s):  
Parameter Identification ◽  
Energy Model ◽  
Least Square ◽  
Recursive Least Square ◽  
Extended Kalman Filtering ◽  
Identification Method ◽  
Driven System ◽  
New Energy ◽  
Least Square Approach ◽  
Belt Conveyors

The energy model of belt conveyors plays a key role in the energy efficiency optimization problem of belt conveyors. However, the existing energy models and parameter identification methods are mainly limited to single-motor-driven belt conveyors and require speed sensors. This paper will present an energy model and a parameter identification method for dual-motor-driven belt conveyors whose speed sensors are not available. Firstly, a new energy model of dual-motor-driven belt conveyors is established by combining the traditional energy model with the dynamic model of a dual-motor-driven system. Then, a parameter identification method based on an extended Kalman filtering algorithm and recursive least square approach is proposed. Finally, the feasibility and effectiveness of the method are demonstrated by simulation experiments.

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.

Kernel Recursive Least Square Approach for Power System Harmonic Estimation

2021 ◽  
pp. 1-16
Author(s):  
Omar Avalos ◽  
Erik Cuevas ◽  
Héctor G. Becerra ◽  
Jorge Gálvez ◽  
Salvador Hinojosa ◽  
...  
Keyword(s):  
Power System ◽  
Least Square ◽  
Recursive Least Square ◽  
Least Square Approach

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.

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