Robust Model Identification Methods for Nonlinear Second-Order Plant Models for Underwater Vehicles

This thesis addresses the problem of plant parameter model identification for nonlinear finite-dimensional second-order plant models for underwater vehicles from experimental data. These models are necessary for predictive simulation studies, model-based control algorithms, and model-based approaches to fault detection. The structure of these dynamical models can be derived using first principles, but the model parameters such as mass, drag, and thrust coefficients must be determined experimentally. This thesis provides solutions to several parts of this problem. First, this thesis reports a derivation of a finite dimensional second-order using Newtonian dynamics. The general form of these equation of motion are widely accepted in the research literature, yet their full and detailed derivation using Newtonian dynamics is often omitted. This thesis seeks to address this lacuna. Second, this thesis reports an extension of an adaptive identifier (AID) to underactuated, three degree of freedom underwater vehicles. Results of a simulation study are reported. Additionally, the same AID is extended to simultaneously identify plant and control parameters for six degree of freedom underwater vehicles. Another extension of the same AID is reported for plant models with diagonal mass and drag matrices. Stability proofs are reported for all new AIDs. Finally, this thesis reports a new algorithm to estimate the parameters of dynamical plants using framework of the random sample consensus (RANSAC) algorithm. This new algorithm is shown in simulation to outperform traditional least squares parameter identification methods with respect to accuracy when observational data is corrupted by non-Gaussian noise