Accurate External Angular Rate Sensing Method for Gyrowheel Using a Hybrid Dual-Module Model

Gyrowheel is a promising attitude control system (ACS) integrating the functions of attitude measurement and actuation. Unlike the traditional attitude sensors, its special operating manner makes it difficult to achieve satisfactory rate sensing performance. This article attempts to provide a practical and effective solution to the external rate sensing problem of gyrowheel. A hybrid dual-module rate sensing approach is developed, which includes a rough-sensing module derived from the gyrowheel dynamics and a precise-sensing module constructed in a data-driven way. Concretely, an encoder-regressor deep learning architecture is designed to constitute the precise-sensing module, which is capable of dealing with the nonlinearities, coupling characteristics, and system uncertainties of the gyrowheel and providing accurate compensation of the rate sensing errors. Besides, the rough-sensing module further guarantees the robustness and interpretability of the proposed method with the adoption of physical knowledge. The proposed rate sensing approach takes both advantages of the two modules, thus obtaining significant performance improvement under large tilt conditions and the influence of system uncertainties. Experimental studies show that the rate sensing accuracy is improved by 2–3 orders of magnitude, which illustrates the feasibility and superiority of the proposed method.