Analysis of Parameter Sensitivity Using Robust Design Techniques for a Flatfish Type Autonomous Underwater Vehicle

Abstract

Hydrodynamic parameters play a major role in the dynamics and control of Autonomous Underwater Vehicles (AUVs). The performance of an AUV is dependent on the parameter variations and a proper understanding of these parametric influences is essential for the design, modeling, and control of high-performance AUVs. In this paper, the sensitivity of hydrodynamic parameters on the control of a flatfish type AUV is analyzed using robust design techniques such as Taguchi's design method and statistical analysis tools such as Pareto-ANOVA. Since the pitch angle of an AUV is one of the crucial variables in the control applications, the sensitivity analysis of pitch angle variation is studied here. Eight prominent hydrodynamic coefficients are considered in the analysis. The results show that there are two critical hydrodynamic parameters, that is, hydrodynamic force and hydrodynamic pitching moment in the heave direction that influence the performance of a flatfish type AUV. A near-optimal combination of the parameters was identified and the simulation results have shown the effectiveness of the method in reducing the pitch error. These findings are significant for the design modifications as well as controller design of AUVs.

Combining Lean and Six Sigma in the context of Systems Engineering design

“…Their work presents the application of conjoint and Taguchi techniques to assess design features using parametric studies. This is similar to the use of response surface techniques seen in feature-based designs (Thompson et al, 2004;Santhakumar et al, 2009). Other than the classical approaches of Six Sigma as witnessed in DMAIC or DFSS, there also have been recent applications of the same in a more hybrid manner to achieve superior results.…”

“…The selection was narrowed down based on the requirements on higher payload and better stability. Further optimization was carried out using Taguchi's robust design concepts (Santhakumar et al, 2009). Based on the design performance, the AUV design was finalized and verified through experimentation.…”

A Neural-Network-Based Sensitivity Analysis Approach for Data-Driven Modeling of Ship Motion

“…Xu [20] used numerical simulation as a basis for a simple sensitivity study analyzing the sensitivity of an autonomous underwater vehicle. Santhakumar et al [21] performed an SA of hydrodynamic parameters on the basis of an underwater robot. Shenoi et al conducted SA of all hydrodynamic derivatives in a four-DOF of container ship [22].…”