Adaptive speed control of hydrogenerators by recursive least squares identification algorithm

1995 ◽  
Vol 10 (1) ◽  
pp. 162-168 ◽  
Author(s):  
S.C. Bonnett ◽  
L. Wozniak
Keyword(s):  
Least Squares ◽  
Speed Control ◽  
Recursive Least Squares ◽  
Identification Algorithm

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.

Real-Time Battery Model Identification Using a Two Time-Scaled Approach

2013 ◽  
Author(s):  
Yiran Hu ◽  
Yue-Yun Wang
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
State Estimation ◽  
Real Time ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Model Parameter ◽  
Battery Model ◽  
Model Parameter Identification

Battery state estimation (BSE) is one of the most important design aspects of an electrified propulsion system. It includes important functions such as state-of-charge estimation which is essentially for the energy management system. A successful and practical approach to battery state estimation is via real time battery model parameter identification. In this approach, a low-order control-oriented model is used to approximate the battery dynamics. Then a recursive least squares is used to identify the model parameters in real time. Despite its good properties, this approach can fail to identify the optimal model parameters if the underlying system contains time constants that are very far apart in terms of time-scale. Unfortunately this is the case for typical lithium-ion batteries especially at lower temperatures. In this paper, a modified battery model parameter identification method is proposed where the slower and faster battery dynamics are identified separately. The battery impedance information is used to guide how to separate the slower and faster dynamics, though not used specifically in the identification algorithm. This modified algorithm is still based on least squares and can be implemented in real time using recursive least squares. Laboratory data is used to demonstrate the validity of this method.

scholarly journals The parameter identification of the autonomous underwater vehicle based on multi-innovation least squares identification algorithm

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142092101
Author(s):  
Zhang Huajun ◽  
Tong Xinchi ◽  
Guo Hang ◽  
Xia Shou
Keyword(s):  
Least Squares ◽  
Degrees Of Freedom ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Six Degrees Of Freedom ◽  
Recursive Least Squares Algorithm ◽  
Least Squares Algorithm

An accurate model is important for the engineer to design a robust controller for the autonomous underwater vehicle. There are two factors that make the identification difficult to get accurate parameters of an AUV model in practice. Firstly, the autonomous underwater vehicle model is a coupled six-degrees-of-freedom model, and each state of the kinetic model influences the other five states. Secondly, there are more than 100 hydrodynamic coefficients which have different effects, and some parameters are too small to be identified. This article proposes a simplified six-degrees-of-freedom model that contains the essential parameters and employs the multi-innovation least squares algorithm based on the recursive least squares algorithm to obtain the parameters. The multi-innovation least squares algorithm leverages several past errors to identify the parameters, and the identification results are more accurate than those of the recursive least squares algorithm. It collects the practical data through an experiment and designs a numerical program to identify the model parameters. Meanwhile, it compares the performances of the multi-innovation least squares algorithm with those of the recursive least squares algorithm and the least square method, the results show that the multi-innovation least squares algorithm is the most effective way to identify parameters for the simplified six-degrees-of-freedom model.

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