The parameter identification of the autonomous underwater vehicle based on multi-innovation least squares identification 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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System Identification and Model Validation of Recursive Least Squares Algorithm for Box–Jenkins Systems

Recent Innovations in Mechatronics ◽

10.17667/riim.2019.1/4. ◽

2019 ◽

Vol 6 (1.) ◽

Author(s):

Nasar Aldian Ambark Shashoa ◽

Abdurrezag S. Elmezughi ◽

Ibrahim N. Jleta ◽

Nasser B. Ekreem

Keyword(s):

Least Squares ◽

Recursive Least Squares ◽

System Model ◽

Noise Model ◽

Model Parameters ◽

Recursive Least Squares Algorithm ◽

New Type ◽

Least Squares Algorithm ◽

Best Fit ◽

High Computational Efficiency

In this paper, a new-type recursive least squares algorithm is proposed for identifying the system model parameters and the noise model parameters of Box–Jenkins Systems. The basic idea is based on replacing the unmeasurable variables in the information vectors with their estimates. The proposed algorithm has high computational efficiency because the dimensions of the involved covariance matrices in each subsystem become small. Validation of the model is evaluated using some statistical methods, Which, best-fit criterion and Histogram. Simulation results are presented to illustrate the effectiveness of the proposed algorithm.

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Parameter Estimation for Wiener Systems with Preload Nonlinearities

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.1460 ◽

2014 ◽

Vol 989-994 ◽

pp. 1460-1463

Author(s):

Yun Xia Ni ◽

Jian Dong Cao

Keyword(s):

Parameter Estimation ◽

Least Squares ◽

Recursive Least Squares ◽

Switching Function ◽

Identification Algorithm ◽

Unknown Parameters ◽

Recursive Least Squares Algorithm ◽

Wiener Systems ◽

Least Squares Algorithm ◽

Identification Model

This paper proposes a recursive least squares algorithm for Wiener systems. We use a switching function to turn the modelof the nonlinear Wiener systems into an identification model, then propose a recursive least squares identification algorithm toestimate all the unknown parameters of the systems. Finally, an example is provided to show the effectiveness of the proposed algorithm.

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Modeling of adaptations to physical training by using a recursive least squares algorithm

Journal of Applied Physiology ◽

10.1152/jappl.1997.82.5.1685 ◽

1997 ◽

Vol 82 (5) ◽

pp. 1685-1693 ◽

Cited By ~ 42

Author(s):

Thierry Busso ◽

Christian Denis ◽

Régis Bonnefoy ◽

André Geyssant ◽

Jean-René Lacour

Keyword(s):

Least Squares ◽

Physical Training ◽

Recursive Least Squares ◽

Model Parameters ◽

Time Varying ◽

Varying Parameters ◽

Recursive Least Squares Algorithm ◽

Time Varying Parameters ◽

Least Squares Algorithm ◽

Time Invariant

Busso, Thierry, Christian Denis, Régis Bonnefoy, André Geyssant, and Jean-René Lacour. Modeling of adaptations to physical training by using a recursive least squares algorithm. J. Appl. Physiol. 82(5): 1685–1693, 1997.—The present study assesses the usefulness of a systems model with time-varying parameters for describing the responses of physical performance to training. Data for two subjects who undertook a 14-wk training on a cycle ergometer were used to test the proposed model, and the results were compared with a model with time-invariant parameters. Two 4-wk periods of intensive training were separated by a 2-wk period of reduced training and followed by a 4-wk period of reduced training. The systems input ascribed to the training doses was made up of interval exercises and computed in arbitrary units. The systems output was evaluated one to five times per week by using the endurance time at a constant workload. The time-invariant parameters were fitted from actual performances by using the least squares method. The time-varying parameters were fitted by using a recursive least squares algorithm. The coefficients of determination r 2 were 0.875 and 0.879 for the two subjects using the time-varying model, higher than the values of 0.682 and 0.666, respectively, obtained with the time-invariant model. The variations over time in the model parameters resulting from the expected reduction in the residuals appeared generally to account for changes in responses to training. Such a model would be useful for investigating the underlying mechanisms of adaptation and fatigue.

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