Infrared Guidance Simulation System Based On ARM

Infrared guidance simulation technology has always been a research focus for precision guided weapon simulation technology at home and abroad.It has tremendous values and vital significance for national defence.And it is an advanced technology being developed by various nations around the world and directly determines the developing speed of infrared guided weapons.Especially in the battlefield environment,infrared-guided weapons,through positioning,tracking,and locking the targets,can constantly adjust the flying attitude of the aircraft to eventually destroy the targets.The infrared guidance simulation system employs infrared imaging system to obtain the coordinate information by processing the infrared image information of the captured target,after which the servo system of the seeker is controlled to adjust the orientation of the optical axis and of the seeker to achieve the tracking of the target.In this thesis,by intensively studying the principles of the infrared guided weapons in the real battlefield environment,an implementation project of the infrared guidance simulation system is designed.In this project,the offset position information of targets is obtained from infrared images which are captured by infrared imaging system and is transffered to the control unit,which is scheduled to control the analog seeker to achieve the target's tracking.The core part of this project is the design of the control unit and the generation of the unit's controlling strategies of the seeker driving motor.According to the requirements of the overall system design scheme,an ARM-based hardware platform is used to achieve the project.Owing to the fact that the hardware circuit of the control unit is relatively complicated,layered design idea is adopted to divide the hardware circuit of the control unit into a transition layer,a driver layer,a core control layer and a power layer.In order to control the motor displacement accurately and rapidly,a two-stage Proportion-Integral-Derivative(PID)closed-loop control scheme is used,in which the inner loop control servo system controls the motor's speed,while the external loop controls servo motor system to adjust its angular displacement.In this thesis,a mathematical model for a DC motor is established and several control strategies are simulated.Besides,the simulation results are analyzed and evaluated from two aspects including the step response adjustment time and overshoot,as well as the position tracking ability of the control system for time-varying signals.After analyzing the simulation results of the conventional PID control strategy,it can be seen that the conventional PID control has a large adjustment time,with an overshoot existing,and that the position tracking deviation and the time lag are large,failing to accord with the system design requirement.Aiming to overcome these intricate problems,the traditional PID control is combined with the neural network.And the single neuron PID control strategy is studied and simulated.Compared with the conventional PID control strategy,the single neuron PID control strategy accelerates the adjustment process,and simultaneously reduces the position tracking deviation and the time lag and improve the control perormances.In order to further improve the abilities of the single neuron PID control strategy to regulate the time and track the target's position,the system identification theory was combined with the single neuron to optimize the learning rules in single neuron PID,and the RBF neural network PID control strategy is obtained and simulated.The simulation results have shown that the adjustment time,overshoot,and position tracking capability are improved,and the control performance is slightly better.Since neural network PIDs use a neuron scaling factor to adjust the gain of the output control increment,the scaling factor is improved here so that it can be dynamically adjusted as the control process to improve control performance.Therefore,an improved RBF neural network PID control strategy is proposed.As the simulation results have shown the adjusting time is further reduced and the control performance is further improved.In the end,by using the integrated development environment of Qt Creator,the PID console software was developed.Under the Keil-MDK integrated development environment,combined with the operating mechanism of the main control chip,the conventional PID control strategy and single neuron PID were implemented.In addition,with the joint debugging of the software and the hardware systems,the control output curve according with the simulation outcome is achieved.