Stable adaptive identification of fully‐coupled second‐order 6 degree‐of‐freedom nonlinear plant models for underwater vehicles: Theory and experimental evaluation

• Published in: International journal of adaptive control and signal processing 2021-05, Vol.35 (5), p.786-810
• Main Authors: McFarland, Christopher J., Whitcomb, Louis L.
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; Adaptive control; Algorithms; Angular acceleration; Control equipment; Control theory; Dynamic models; Identification methods; Instruments; learning systems and adaptive modeling; marine robotics; nonlinear dynamics; nonlinear multivariable system identification and estimation; Parameter estimation; Parameter identification; Stability analysis; Tracking control; Trajectory control; Underwater vehicles
• ISSN: 0890-6327; 1099-1115
• DOI: 10.1002/acs.3235

This article reports the development, stability analysis, and experimental evaluation of a novel adaptive identification (AID) algorithm for underwater vehicles (UVs) for on‐line estimation of plant parameters (hydrodynamic mass, quadratic drag, righting moment, and buoyancy parameters) that enter linearly into 6 degree‐of‐freedom (6‐DOF) second‐order rigid‐body UV plant dynamic models. The reported UV AID method does not require instrumentation of vehicle acceleration as is required of other standard plant parameter identification methods such as conventional least squares. All but one previously reported adaptive methods for second‐order nonlinear plants have addressed the problem of model‐based adaptive tracking control—approaches in which adaptive plant model identification is performed simultaneously with model‐based trajectory‐tracking control of fully‐actuated second‐order plants; however, these approaches are not applicable when the plant is either uncontrolled, under open‐loop control, underactuated, or using any control law other than an algorithm‐specific adaptive tracking controller. The UV AID algorithm reported herein does not require simultaneous reference trajectory‐tracking control, nor does it require instrumentation of linear acceleration or angular acceleration; thus this novel approach complements previously reported adaptive tracking methods and is applicable to a broader class of UV applications for which fully‐actuated tracking control is impractical or infeasible. We report a experimental performance analysis of the UV AID algorithm in comparison to conventional least‐square identification methods, including comparison in cross‐validation where the performance of the experimentally identified plant models obtained in identification trials are compared to experimental trials differing from the identification trials.