Self-Triggered Fuzzy Data-Driven Learning-Based Test Mass Suspension Control for Space Inertia Sensor

To propose an ultrahigh-precision test mass suspension control scheme for the space inertia sensor in the mission of space detection, a self-triggered data-driven adaptive control approach is investigated in this article, utilizing the sampling measurements to construct a data-driven control strategy. The controller is provided merely utilizing input/output measurement datasets, and we introduce the fuzzy logic systems into the data-driven controller, to build a compensator for the system nonlinearities caused by the voltage actuation process of the electrostatic suspension. Different from the existing research, this work is concerned to be more challenging when fading stochastic measurement noise, input saturation, and communication bandwidth limitation are taken into account for the test mass suspension system. With the proposed self-triggered fuzzy data-driven learning-based control scheme, each closed-loop signal is proved to be bounded, and the efficient control performance is verified by numerical simulations both in the time and frequency domains.