A Thrust Control Method of Solid Rocket Motor Based on Radial Basis Neural Network

Abstract

The invention provides a solid rocket motor thrust control method based on a radial basis neural network, which is characterized in that it includes the following steps: Step 1. Determine the control object, the executive mechanism adopts a gas regulating system and a pneumatic servo system, and selects a solid rocket motor as Power plant; Step 2. Establish the mathematical model of the controlled object of the solid rocket motor; Step 3. Establish the reference model and the actual model according to the expected control performance requirements; Step 4. Design the RBF neural network control for the thrust adaptive control of the solid rocket motor The device selects the learning index of the neural network; Step 5. utilizes the genetic algorithm to optimize the parameters of the RBF neural network controller; the present invention optimizes the parameters of the neural network through the genetic algorithm, so that the solid rocket motor control system based on the RBF neural network is given reference The model characteristics have good tracking ability, and have good anti-interference and robustness.

Description

technical field [0001] The invention relates to the field of automatic control, in particular to a thrust control method of a solid rocket motor based on a radial basis neural network. Background technique [0002] Solid rocket motor, also known as solid propellant rocket motor, uses solid propellant, which can meet the requirements of the new generation of tactical missiles for its power plant to the greatest extent. It has the advantages of small size, light weight, fast speed and good maneuverability. An ideal propulsion system is a strategic weapon that countries give priority to development. [0003] A large number of theories and experiments have been done for the solid rocket motor with controllable thrust at home and abroad. In order to ensure that the solid rocket can fly according to the predetermined orbit, it is necessary to make corresponding guidance and control. The attitude control of the rocket is realized through the thrust vector control system, and the ...

AI Technical Summary

Problems solved by technology

In recent years, due to the development of neural networks, many studies have applied radial basis neural networks to the control of dynamic objects, but from the perspective of control methods, it is still the first to use radial basis neural networks for solid rocket control. For rocket engine control, its parameters will change drastically with the environment, and the unmodeled dynamics will also bring certain difficulties to the control, and the radial basis neural network has a good adaptive function, which can better overcome this problem

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