Input-output data filtering based recursive least squares identification algorithm for Hammerstein OEAR models

2014 ◽  
Author(s):  
Junsheng Zhao ◽  
Xingjiang Yu ◽  
Jianwei Xia
Keyword(s):  
Least Squares ◽  
Recursive Least Squares ◽  
Identification Algorithm ◽  
Data Filtering ◽  
Input Output ◽  
Output Data

A Modified Recursive Identifier Not Requiring Initial Condition Estimates

1982 ◽  
Vol 104 (3) ◽  
pp. 264-266
Author(s):  
J. M. Mocenigo ◽  
A. E. Pearson
Keyword(s):  
Least Squares ◽  
Initial Conditions ◽  
Least Squares Estimator ◽  
Recursive Least Squares ◽  
Stochastic Noise ◽  
Initial Condition ◽  
Limited Data ◽  
Input Output ◽  
Output Data

A recursive least-squares estimator is developed in an identification observer format with the property that the need for estimating initial conditions is eliminated for time limited data. Estimators are developed that work with observed input/output data corrupted by deterministic, piecewise deterministic, or stochastic noise.

scholarly journals Payload Parameter Identification of a Flexible Space Manipulator System via Complex Eigenvalue Estimation

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Zhiyu Ni ◽  
Shunan Wu ◽  
Yewei Zhang ◽  
Zhigang Wu
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
State Space Model ◽  
Recursive Least Squares ◽  
Mode Shapes ◽  
Identification Algorithm ◽  
Complex Eigenvalue ◽  
Nonlinear Dynamic Model ◽  
Space Manipulator ◽  
Affine Projection

Manipulator systems are widely used in payload capture and movement in the ground/space operation due to their dexterous manipulation capability. In this study, a method for identifying the payload parameters of a flexible space manipulator using the estimated system of complex eigenvalue matrix is proposed. The original nonlinear dynamic model of the manipulator is linearized at a selected working point. Subsequently, the system state-space model and corresponding complex eigenvalue parameters are determined by the observer/Kalman filter identification algorithm using the torque input signal of the motor and the vibration output signals of the link. Therefore, the inertia parameters of the payload, that is, the mass and the moment of inertia, can be derived from the identified complex eigenvalue system and mode shapes by solving a least-squares problem. In numerical simulations, the proposed parameter identification method is implemented and compared with the classical recursive least-squares and affine projection sign algorithms. Numerical results demonstrate that the proposed method can effectively estimate the payload parameters with satisfactory accuracy.

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