Research On Servo Control System Of Two-axis Turntable In Airborne Pod

With As an airborne tracking and positioning device that integrates optics,control,and computer technologies,the airborne pod turntable is of great significance in promoting the modernization of my country’s national defense.In order to quickly capture the target and accurately track the target,higher requirements are put forward for its control accuracy.It is necessary to ensure the imaging quality while controlling the missed target within a reasonable range to guide the laser weapon to strike the target.Improve the control performance of PID controller through neural network and combine with traditional PID control to complete the double closed-loop control of turntable DC servo system.This article takes DC brushless torque motor as the controlled object,and uses ARM and FPGA as the main control chip.The above-mentioned closed-loop control algorithm has been simulated and experimentally verified on the overall performance of the airborne pod turntable.This article first analyzes the working conditions of the turntable of the airborne pod,proposes the adverse effect of nonlinear friction introduced by the dynamic seal of the optical device on the control accuracy of the turntable,and derives the mathematical model of the controlled object and the control system.System modeling work.Then,the simulation study of the dual closed-loop PID control of the DC servo system is carried out.Aiming at the nonlinear friction problem introduced by the dynamic seal of the optical equipment,a BP neural network self-tuning PID controller is proposed to improve the traditional PID controller,so as to achieve the PID parameters.The purpose of self-tuning is to suppress and compensate the above-mentioned nonlinear friction more accurately.The performance of the BP neural network self-tuning PID controller is analyzed by simulation,and the results show that the control performance of the controller is indeed improved compared to the traditional PID controller,and it can provide better compensation for nonlinear friction.Then complete the hardware circuit realization of the control system,the ARM chip realizes the main PID closed-loop control algorithm,and FPGA as its information processing unit.This article focuses on the deployment and configuration of FPGA chips.Finally,write Verilog code to realize each functional module designed in FPGA,and carry out simulation and experimental verification on it.Especially for the BP neural network module,because the backpropagation process of the BP neural network involves a large number of floating point operations such as difference and product,the computing power of the chip is high,and it needs to be verified whether its real-time performance can meet the requirements;and because of the FPGA’s For floating-point number processing accuracy,it is necessary to verify whether the algorithm can converge.Then,test the overall performance of the turntable.The speed loop and position loop of the position and pitch motors are given sinusoidal signals to observe their response.