Research Of The Airborne Electro-optical Pod Control System Based On FPGA

The airborne electro-optical pod achieve the target detection, identification, targeting,tracking, navigation and other functions by using photoelectronic sensors. This paper mainlystudies on the airborne electro-optical pod control system, and the ALTERA Cyclone II series chipis used as the core of the control system pod, using NIOS II soft-core processor to create acustomizable system-on-a-programmable-chip (SOPC). NIOS II system itself has some IP cores,in order to better achieve the system functions, the paper also customizes some IP cores (PWM,CORDIC, FLOAD, etc.) by the HDL language, and adds them to the NIOS II system to form theNIOS II system, then this system will be downloaded to the FPGA implementation, last initialPID algorithm using software programming to realize the optical pod overall control.Due to the vibration of carrier, wind resistance, mechanical vibration and load disturbance,the system can be unstable, While PID controller with traditional fixed parameters can’t meet thecontrol requirements. To solve these problems, the fuzzy-PID control optimized by the cuckoosearch algorithm are proposed. In this paper, SIMULINK is used to simulate the pod controlsystem model with the optimized controller, compared the simulation results between the twooptimization methods, the result optimized by CS(the cuckoo search) are proved to be moreefficient than that with PSO(the particle swarm optimization) and the airborne electro-optical podcontrol system using optimized fuzzy-PID control has a good control effect.In order to apply the fuzzy controller better, the fuzzy controller is realized by hardwaremodules, we use the FPGA to implement the intelligent fuzzy controller. First, the fuzzy controlleris designed dividedly controller by FPGA hardware development environment Quartus II. In thispaper, the graphic design and the HDL text form are adopted to achieve the various functions. Andthe modules are simulated in Quartus II9.0, the simulation results show that the fuzzy controllerbased on FPGA achieve the envisaged results.