Neural network adaptive control is proposed for a class of nonlinear system preceded by hysteresis. A novel model is developed to represent the hysteresis characteristics in explicit form. Furthermore, the auxiliary variable of the proposed model is proved to be bounded, which is essential for controller design. Then, neural network adaptive controller is directly applied to mitigate the influence of the hysteresis without constructing the hysteresis inverse. The updated law and control law of the controllers are derived from Lyapunov stability theorem, so that the boundedness of the close-loop system is guaranteed. Finally, the experimental tests are carried out to validate the effectiveness of the proposed approach.

The most significant feature of the decision tree algorithm is to transform the complex decision-making process into a number of simple decision-making processes and then accumulate it. It's a tree structure similar to the flow chart. The decision tree can be applied to the fault diagnosis of wind turbine gearbox to data mining for gearbox and then find rules and reflect in the form of rules. Experiments show that the use of decision tree method to extract rules can be faster and more accurate.

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According to applying feature of power supply and distribution system in aerospace experiment field, the intelligent monitoring system is designed by adopting hierarchical distributed network structure which includes field layer, network layer, management layer, and command center layer. The monitoring software based on configuration software realized such functions as unify electric force management, optimize electric force balance, economize electrical energy loss, extend lifetime of equipments, improve qualities of environment protection, promote efficiency and managing level. Applying result indicates: the system satisfies the requirement of aero experimenting task.

To control a nonlinear system with both hysteresis and disturbance, it is necessary to establish a hysteresis model and improve the disturbance rejection ability. However, the input signal implicitly involved in the classical hysteresis model can lead to difficulty in constructing a compensator. In this study, a hysteresis model in explicit form is proposed with a bounded auxiliary variable. Then, a model‐based inverse is constructed for approximate compensation for the hysteresis. Moreover, the compensation error, which is considered a part of the disturbance, is proved to be bounded. Disturbance estimation triggered control (DETC) is utilized to address the compensation error coupled with the external disturbance. According to the disturbance effect indicator (DEI), DETC can improve the system control performance by considering the disturbance effect judgment. Experimental results are presented to illustrate the potential of the proposed technique.