Correlation analysis-based error compensation recursive least-square identification method for the Hammerstein model

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

Energies ◽

10.3390/en11123313 ◽

2018 ◽

Vol 11 (12) ◽

pp. 3313 ◽

Cited By ~ 3

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.

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Predictive control of permanent magnet synchronous motor with non‐sinusoidal flux distribution for torque ripple minimisation using the recursive least square identification method

IET Electric Power Applications ◽

10.1049/iet-epa.2016.0315 ◽

2017 ◽

Vol 11 (5) ◽

pp. 847-856 ◽

Cited By ~ 13

Author(s):

Alireza Abbaszadeh ◽

Davood Arab Khaburi ◽

José Rodríguez

Keyword(s):

Permanent Magnet ◽

Predictive Control ◽

Flux Distribution ◽

Permanent Magnet Synchronous Motor ◽

Torque Ripple ◽

Synchronous Motor ◽

Least Square ◽

Recursive Least Square ◽

Identification Method

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Recursive Identification for MIMO Fractional-Order Hammerstein Model Based on AIAGS

Mathematics ◽

10.3390/math10020212 ◽

2022 ◽

Vol 10 (2) ◽

pp. 212

Author(s):

Qibing Jin ◽

Bin Wang ◽

Zeyu Wang

Keyword(s):

Fractional Order ◽

Search Strategy ◽

Least Square Method ◽

Global Search ◽

Least Square ◽

Hammerstein Model ◽

Recursive Identification ◽

Recursive Least Square ◽

Original Algorithm ◽

Initial Values

In this paper, adaptive immune algorithm based on a global search strategy (AIAGS) and auxiliary model recursive least square method (AMRLS) are used to identify the multiple-input multiple-output fractional-order Hammerstein model. The model’s nonlinear parameters, linear parameters, and fractional order are unknown. The identification step is to use AIAGS to find the initial values of model coefficients and order at first, then bring the initial values into AMRLS to identify the coefficients and order of the model in turn. The expression of the linear block is the transfer function of the differential equation. By changing the stimulation function of the original algorithm, adopting the global search strategy before the local search strategy in the mutation operation, and adopting the parallel mechanism, AIAGS further strengthens the original algorithm’s optimization ability. The experimental results show that the proposed method is effective.

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