Parametric identification and structure searching for underwater vehicle model using symbolic regression

Abstract Models for underwater vehicle explaining its relationship between movement and the force exerting on the robot permit a wide range of development to be used in control and navigation. Yet currently no general method arrives a better model with structure and parameters for vehicles automatically. Based on the empirical data, symbolic regression method inspired by natural selection is conducted to automatically detect realistic structure and parameters of vehicle model. The proposed method is completely general and does not assume any pre-existing models before identification, it can be applied “out of the box” to any given vehicle experiment data. To validate and compare our approach with parameter identification methods like Levenberg–Marquardt Algorithm and Genetic Algorithm, we systematically rediscover the laws underlying underwater vehicle models and neglected laws for reflect the environments. Predicted results for datasets show that we are able to find programs that are simple enough to lead to an actual accurate model for describing the mechanisms of the vehicle.

[1]  Kang Tai,et al.  Mathematical modelling of burr height of the drilling process using a statistical-based multi-gene genetic programming approach , 2014 .

[2]  Aude Billard,et al.  Estimating the non-linear dynamics of free-flying objects , 2012, Robotics Auton. Syst..

[3]  Hod Lipson,et al.  Distilling Free-Form Natural Laws from Experimental Data , 2009, Science.

[4]  Michael D. Schmidt,et al.  Symbolic Regression of Implicit Equations , 2010 .

[5]  Ming Li,et al.  Modeling of a complex-shaped underwater vehicle , 2014, 2014 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC).

[6]  Camille Roth,et al.  Symbolic regression of generative network models , 2014, Scientific Reports.

[7]  Guang-Bin Huang,et al.  Trends in extreme learning machines: A review , 2015, Neural Networks.

[8]  David R. Stoutemyer,et al.  Can the Eureqa symbolic regression program, computer algebra and numerical analysis help each other? , 2012, ArXiv.

[9]  L. Coelho,et al.  Nonlinear model identification of an experimental ball-and-tube system using a genetic programming approach , 2009 .

[10]  Xia-Ting Feng,et al.  Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm , 2006 .

[11]  Feng Xu,et al.  Sensitivity analysis and parametric identification for ship manoeuvring in 4 degrees of freedom , 2014 .

[12]  Amir Hossein Gandomi,et al.  A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems , 2011, Neural Computing and Applications.

[13]  José Antonio Cruz-Ledesma,et al.  Modelling, Design and Robust Control of a Remotely Operated Underwater Vehicle , 2014 .

[14]  Norbert Link,et al.  Learn More about Your Data: A Symbolic Regression Knowledge Representation Framework , 2012 .

[15]  Amir Hossein Gandomi,et al.  A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems , 2011, Neural Computing and Applications.

[16]  Timothy Prestero,et al.  Verification of a six-degree of freedom simulation model for the REMUS autonomous underwater vehicle , 2001 .

[17]  Leandro dos Santos Coelho,et al.  A genetic programming approach based on Lévy flight applied to nonlinear identification of a poppet valve , 2014 .

[18]  Luca Maria Gambardella,et al.  zePPeLIN: Distributed Path Planning Using an Overhead Camera Network , 2014 .

[19]  Tamaki Ura,et al.  Estimation of the hydrodynamic coefficients of the complex-shaped autonomous underwater vehicle TUNA-SAND , 2009 .

[20]  Pouria Sarhadi,et al.  Extended and Unscented Kalman filters for parameter estimation of an autonomous underwater vehicle , 2014 .

[21]  Wei-Der Chang,et al.  Nonlinear system identification and control using a real-coded genetic algorithm , 2007 .

[22]  Thor I. Fossen,et al.  Guidance and control of ocean vehicles , 1994 .

[23]  Yangquan Chen,et al.  Genetic Algorithm-Based Identification of Fractional-Order Systems , 2013, Entropy.

[24]  Ieroham S. Baruch,et al.  A levenberg–marquardt learning applied for recurrent neural identification and control of a wastewater treatment bioprocess , 2009, HIS 2009.