Alpha stable noise, determined by four parameters, has been found in the random error of a laser gyroscope. Accurate estimation of the four parameters is the key process for analyzing the properties of alpha stable noise. Three widely used estimation methods—quantile, empirical characteristic function (ECF) and logarithmic moment method—are analyzed in contrast with Monte Carlo simulation in this paper. The estimation accuracy and the application conditions of all methods, as well as the causes of poor estimation accuracy, are illustrated. Finally, the highest precision method, ECF, is applied to 27 groups of experimental data to estimate the parameters of alpha stable noise in a laser gyroscope’s random error. The cumulative probability density curve of the experimental data fitted by an alpha stable distribution is better than that by a Gaussian distribution, which verifies the existence of alpha stable noise in a laser gyroscope’s random error.

Tiltrotor aircraft possesses redundant actuators in take-off phase and its flight control is more complicated than ordinary aircraft because the structural and dynamic characteristics keep changing due to tilting rotors. One of the fundamental bases for flight control is trim, which provides steady flight states under various conditions and then constructs the reference trajectory. Tiltrotor aircraft trim models are described by multivariate nonlinear equations whose initial values are difficult to determine and bad initials could lead to incorrect solution for flight control. Therefore, an innovative trim method is proposed to solve this issue. Firstly, genetic algorithm (GA), which possesses strong capability in searching global optimum, is adopted to identify a coarse solution. Secondly, the coarse solution is further refined by the Levenberg-Marquardt (LM) method for precise local optimum. The innovative trim method combines the advantages of these two algorithms and is applied to a tiltrotor aircraft’s flight control in the transition process of incline take-off. The limitation of trajectory is discussed, and tilt corridor is constructed. Finally, the incline take-off simulations are conducted and the effectiveness of the proposed trim method is verified through good match with the designed reference trajectory.

Microelectromechanical systems (MEMS) are core components in unmanned aerial vehicles (UAV). The precision of MEMS sensors is very important when the GPS signal is invalid. However, the precision and performance of MEMS sensors will be degraded by the changing of environment. Therefore, estimation and identification of the various noise terms existing in MEMS sensors are deemed to be necessary. The Allan variance is a common and standard method to analyze MEMS sensors, but it cannot be used to analyze the dynamic characteristics. The dynamic Allan variance (DAVAR) is a sliding version of the Allan variance. It is a practical tool that could represent the nonstationary behavior of the MEMS signal. As the DAVAR needs to estimate the Allan variance at each time epoch, the computation time grows significantly with the length of the signal series. In this paper, in the case of MEMS gyroscope on UAV, an improved fast DAVAR algorithm based on the choice of relevant time is proposed to shorten the computation time. As an experimental validation, numerical experiments are conducted under the proposed method. The results demonstrate that the improved method could greatly increase the computation speed and does not affect the accuracy of estimation.

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Supervisory control and protection system of the neutral beam injector (NBI) in the HL-2A tokamak is presented. The system is used for a safe coordination of all the main NBI subsystems. Because the system is based on computer networks with its transmission medium of optical fiber, its advantages in high operational stability, reliability, security and flexible functional expandability are clearly shown during the NBI commissioning and heating experiment in HL-2A.