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In order to solve the problems of low efficiency and large start-up shock of the belt conveyor main roller which is driven by an asynchronous motor with a hydraulic coupling, a direct drive roller control scheme of belt conveyor based on an external rotor permanent magnet synchronous motor(PMSM) is proposed. In order to realize low-speed high-torque control, a vector control strategy based on the rotating coordinate system with zero direct axis current is used. In order to solve the problem of small counter-electromotive force and low accuracy of rotor position and speed estimation during low-speed operation, the high frequency pulse signal injection method is applied to estimate the rotor position and speed. The experimental platform of direct-drive roller speed control system is established to conduct steady-state characteristic test, start-up characteristic test and load characteristic test. It is verified that the PMSM direct-drive roller control system has fast torque response and stable adjustable speed in the speed range of 0-90 r/min, which can meet the start-up speed regulation requirements of low-spreed high-torque belt conveyor.

A floating foundation vibration reduction system with an air spring as a vibration isolation element has been widely used in the foundation of large ultra-precision instruments. With the excellent vibration isolation performance of the air spring, its complex dynamic nonlinear behavior is always a research difficulty. In this paper, a dynamic model of a floating foundation vibration reduction system based on the restoring force of air spring is derived. The structure of the recurrent convolution neural network (RCNN) is proposed based on combining the working characteristics of a convolution neural network and a long short-term memory neural network, and the dynamic model of a floating foundation vibration reduction system is established with the restoring force of the air spring calculated by the RCNN as the input. Finally, a test experiment was designed to compare the dynamic characteristics of the traditional numerical model and three deep neural network models in the floating foundation. The results show that convolution neural network, long short-term memory, and RCNN models could predict the vibration response of floating foundation vibration reduction system, and the RCNN model had better performance for a floating foundation.

Steel plate concrete composite (SPCC) structure plays an essential role in modular construction, which has been widely applied in the floating foundation vibration reduction system. This study proposed a basis for designing an SPCC floating foundation vibration reduction system with an air spring as the main vibration isolation element. Based on the engineering thermodynamics and aerodynamics theories, the non-linear state equations and dynamic equations of each component of the air spring with auxiliary chamber were established, respectively. According to the operational characteristics of the air spring, the equations were linearized by using the small deviation linearization method under the condition of small amplitude, and the linearized model of the dynamic stiffness of the air spring with an auxiliary chamber was therefore proposed. The reliability of the proposed method was proved by a floating foundation vibration test, which was performed in a solid-state quantum laboratory. The constitutive equations of an air spring model and an SPCC floating foundation model were incorporated into the STERA_3D software. The simulation results showed good agreement with the low-frequency micro-vibration region test data.