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This paper presents a neural adaptive flight control for longitudinal dynamics of air-breathing hypersonic vehicles (AHVs) with constrained aerodynamic surfaces. Multiple actuator constraints including magnitude, rate, and first-order dynamic model in both the elevator and canard are transformed into a specific control allocation problem, which can be readily solved using the standard model predictive control (MPC) technique. Furthermore, an adaptive control scheme is developed combining with the above control allocation and the recurrent cerebellar model articulation controller (RCMAC), which well handles actuator constraints and uncertain factors including aerodynamic coefficients, external disturbances, and flexible dynamics. Numerous simulation results verify performance and robustness of the proposed neural adaptive control.

High speed machining is a promising technology for significantly increasing productivity and reducing production costs. Development of high-speed spindle technology is strategically critical to the implementation of high speed machining. Compared to conventional spindles, and motorized spindles are equipped with built-in motors for better power transmission and balance to achieve high-speed operation. However, the built-in motor introduces additional mass to the spindle shaft, besides, since its very high working speed, some high-speed rotational effects, including centrifugal forces and gyroscopic moments on the spindle shaft can not be neglected in the analysis as is done in conventional spindle, thus complicating its mechanical-dynamic behaviors. In this paper, the FEM model of motorized spindle is set up to research on its dynamic characteristics in theory with an eye to high-speed rotational effects, including centrifugal forces and gyroscopic moments on the motorized spindle shaft. The motorized spindle’s natural frequencies and corresponding vibration shapes are got through the modal analysis, and the effect of the axial preload on the natural frequency is programmed to be seen clearly.

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