To ensure the stability of image guiding head control, an improved genetic and particle swarm hybrid algorithm optimized RBF fuzzy neural network tuned PID control strategy was proposed for the correction of the image guiding head stable loop. According to the kinematics and dynamics of the image guiding head, combined with the RBF fuzzy neural network PID control algorithm, a simulation model of the image guiding head stable loop was established. The simulation results showed that after the image guiding head stable loop adopted the RBF fuzzy neural network tuned PID controller optimized by the improved hybrid algorithm, its dynamic performance was better than the traditional PID controller, and the established simulation model could well eliminate the nonlinear disturbance and improve the robustness of the stable loop, which could provide certain reference in engineering application.
With the rapidly development of global military, missiles play an increasingly important role in modern war. The realization of precise guidance for missiles has become an important problem to be solved at present, and the seeker is an important equipment to realize precise guidance. The traditional gimbal seeker has many disadvantages such as large volume, high cost and complex structure, so the strapdown seeker appears. Although the strapdown seeker can effectively solve the problems above, there are problems such as high noise, attitude coupling, phase matching, etc. So, strapdown seeker requires higher estimation algorithm than traditional gimbal seeker. In order to improve the filtering accuracy, this paper adds an adaptive link to the unscented Kalman filter to form an adaptive unscented Kalman filter. Finally, through simulation and comparison, it is found that the filtering accuracy after adding adaptation is a little higher than that before adding adaptation.
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