Abstract
This article investigates the attitude stabilization control problem with low-frequency communication to actuators in the framework of sampled-data control. A novel event-triggered sampling control policy is proposed by employing an integral-type triggering function where the determination of all sampling times is by judging the properties of this function including the integration of measurement errors. Using the Lyapunov-based approach, we show that the stability of the closed-loop system can be guaranteed in the presence of external disturbance, inertia uncertainty, and actuator fault. Compared with conventional attitude control policies, the proposed algorithm significantly reduces the data-rate requirement in updating the actuator while providing high reliability and accurate performance for attitude stabilization. Compared with the traditional event-triggered sampling, the proposed policy is no longer by judging the instantaneous state of measurement errors, which reduces the sampling frequency and does not increase the computational burden. Numerical simulations are conducted to show a decent performance of the algorithm.
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