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Conventional physics-based modeling is a time-consuming bottleneck in controldesign for complex nonlinear systems like autonomous underwater vehicles(AUVs). In contrast, purely data-driven models, though convenient and quick toobtain, require a large number of observations and lack operational guaranteesfor safety-critical systems. Data-driven models leveraging available partiallycharacterized dynamics have potential to provide reliable systems models in atypical data-limited scenario for high value complex systems, thereby avoidingmonths of expensive expert modeling time. In this work we explore thismiddle-ground between expert-modeled and pure data-driven modeling. We presentcontrol-oriented parametric models with varying levels of domain-awareness thatexploit known system structure and prior physics knowledge to createconstrained deep neural dynamical system models. We employ universaldifferential equations to construct data-driven blackbox and grayboxrepresentations of the AUV dynamics. In addition, we explore a hybridformulation that explicitly models the residual error related to imperfectgraybox models. We compare the prediction performance of the learned models fordifferent distributions of initial conditions and control inputs to assesstheir accuracy, generalization, and suitability for control.

Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles