Research On Intelligent Target Rotation Control Technology Of Theodolite

The detection technology of photoelectric theodolites is divided into infield detection and outfield detection,among which infield detection has gradually become the mainstream method for detecting the performance of photoelectric theodolites due to its advantages of low cost,simple preparation work,and low implementation difficulty.The rotating target is one of the devices for detecting the performance of the photoelectric theodolite in the inner field,and plays a very important role in the inner field detection.With the development of optoelectronic theodolites in recent years,their own performance has been continuously improved,which puts forward higher requirements for the detection device and technology of optoelectronic theodolites.In order to adapt to this situation,this article proposes a new design scheme for rotating targets and conducts research on control technology to improve the control performance and motion characteristics of rotating targets.The use of traditional rotating targets for infield detection has limitations.A novel scheme for rotating targets is proposed to address this issue,and the parameters that affect the motion characteristics of rotating targets are analyzed.The conclusion is that changing the rotation angular velocity of rotating targets to change the motion characteristics of rotating targets is the simplest and most effective method.In order to improve the control performance of the rotating target,advanced intelligent control technology is studied.Based on the traditional Sliding mode control algorithm with strong robustness,dynamic sliding mode surface,radial basis function(RBF)neural network and fuzzy compensator are introduced to optimize it.An adaptive dynamic sliding mode neural control algorithm is proposed,and Sliding mode control algorithms under different optimization methods are simulated and compared.The results show that the adaptive dynamic sliding mode neural control algorithm can significantly reduce the chattering phenomenon caused by the traditional Sliding mode control algorithm,reduce the Approximation error of RBF neural network,and improve the approximation speed.Using DDSM216-03 A brushless DC torque motor as the simulation model,the adaptive dynamic sliding mode neural control algorithm was used for speed simulation,and compared with the proportional integral differential(PID)control algorithm.The simulation results showed that the proposed algorithm was superior to the PID algorithm and could meet the indicator requirements of rotating target rotation angle speed.A brushless DC torque motor control system based on Field Programmable Gate Array(FPGA)was designed with adaptive dynamic sliding mode neural algorithm as the core,and an experimental platform was built to verify the feasibility of the algorithm.