Study On The Digital Controller Of The Platform Stabilization Loop

There are some unique strongpoints in the inertial navigation system, for example, well concealment, independence and it can work under any weather condition. It is now playing a role in the use of military and civil affairs. Platform serve loop is pivotal in the inertial navigation system, and its precision will directly affect the navigation precision. Traditional platform serve loop is composed of continuous system, and there are some disadvantages in it, for example, big volume, high cost, complicated making and difficult to upgrade. Therefore, digital control becomes the direction of the platform serve loop. This paper researches on the digital control of the DSP-based platform serve loop.This paper does some work in the theoretical analysis and emendation of the platform serve loop. It validates the feasibility of the single closed loop traditional PID control law in the control precision and response velocity by devising and simulation, and gets over the shortage in the anti-jamming performance of the single closed loop traditional PID control by introducing into the double closed loop control of the velocity feedback. In allusion to weak point of traditional PID control can't effectively tracking parametric variation of control system ,utilize neural network have predominance that self-study,self-organization,associative memory,concurrent processing and can get satisfactory result in complex control system on the basis of double closed loop,advance apply adaptive linear element network—Adaline network, which has self-learn and self-adjust ability. The self-aligning controller neural network-based not only has simple structure, but also has strong robust character and acclimatization. To make the parameters self-regulate, this paper introduces the LMS algorithm in the adaptive linear neuro controller, and optimizes the index performance by changing weight coefficients of the Adaline controller. A new method of selecting the initial weights of Neural Network is put forward, which is the coefficients of discrete equivalent of continuous transfer function in traditional control are used as the initial weights of Neural Network. Adaptive Neural Network control is combined with traditional control by using different methods in different segment. It is proved to be a good method and experimental results are given in this paper.The software and hardware of digital platform stabilization loop are also designed in this paper. Hardware interface and theory of digital controller are emphasized in hardware design section. Modularization design realized and PWM wave producing module closely related chip structure are emphasized in software design section. Because DSP has efficiency speed and high precision, TMS320F240, a product of Texas Instruments Inc., which can provide 12 PWM...