Abstract
Arequipa region holds the largest extension of the Peruvian littoral at the Pacific sea, has also fresh water resources composed of rivers and lagoons from the coast to the Andes highland. The ALBA vehicle is a low-cost autonomous surface vessel with open source architecture that is being developed to support water monitoring tasks in the region. This article deals with the nonlinear identification problem for an autonomous surface craft and the maximum likelihood estimation approach is used to estimate its parameters. The parametric nonlinear model is considered with simulated and experimental data. The results shows good fitting values when two, three and a maximum four parameters are estimated.
Highlights
Water, the most precious resource for human being, is being vulnerable to contamination at present since there is enough evidence [1]
The main contribution of this paper is to estimate a greater number of parameters for the ALBA autonomous surface crafts (ASCs), using the maximum likelihood approach with simulated and actual data, the method used in this paper is called maximum likelihood estimation (MLE) and allows us to identify many parameters at a time, MLE is used for large samples and is very versatile and accurate because it works estimating from the values obtained of the inertial sensor and of the position sensor so it is reliable and even being able to have initial conditions
The identification of parameters and the importance of using the MLE method is done; Section 2 presents the work done on parameter estimation and ASCs; Section 3 presents the mathematical model of the ALBA ASC in nonlinear representation; Section 4 presents the maximum likelihood parameter estimation approach to identify the ALBA ASC; Section 5 presents the experimental tests and their achieved estimated parameters; Section 6 provides the conclusion
Summary
The most precious resource for human being, is being vulnerable to contamination at present since there is enough evidence [1]. The monitoring of sea conditions is commonly carried out using manned vessels, following standard international procedures and agreements. These large vessels cannot work in coastal areas and estuary locals due to the risk of crashing with rocks, irregularities in seabed, and currents. The main contribution of this paper is to estimate a greater number of parameters for the ALBA ASC, using the maximum likelihood approach with simulated and actual data, the method used in this paper is called maximum likelihood estimation (MLE) and allows us to identify many parameters at a time, MLE is used for large samples and is very versatile and accurate because it works estimating from the values obtained of the inertial sensor and of the position sensor so it is reliable and even being able to have initial conditions. The identification of parameters and the importance of using the MLE method is done; Section 2 presents the work done on parameter estimation and ASCs; Section 3 presents the mathematical model of the ALBA ASC in nonlinear representation; Section 4 presents the maximum likelihood parameter estimation approach to identify the ALBA ASC; Section 5 presents the experimental tests and their achieved estimated parameters; Section 6 provides the conclusion
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