High-Precision and Efficient Multi-Physics Modeling for Missile Servo Systems

The missile servo system is a crucial component controlling the flight trajectory of a missile, significantly impacting its guidance accuracy and maneuverability. Consequently, constructing a high-precision missile servo system model is essential for thorough missile performance analysis. However, the missile servo system is a complex entity, incorporating multiple disciplines like mechanics, electromagnetics, and control. Conventional servo system modeling approaches often concentrate on a single discipline, leading to drawbacks such as low simulation accuracy and extended product design cycles, which fall short of current demands for rapid, agile, and highly precise design. While multi-physics-based modeling methods offer the potential for highly accurate integrated simulation models, their requirement for massive datasets and low computational efficiency hinder their widespread adoption. Therefore, the need arises for a method that can maintain high simulation accuracy while significantly enhancing computational efficiency for multi-physics-based simulation models, addressing the conflict between high precision and high computational efficiency in missile servo system multi-physics modeling.

This study investigates the air-to-air missile servo system. By analyzing the composition and principles of the missile servo system, we model and optimize the servo system's motor, transmission mechanism, and control algorithm. We delve into model reduction technology and its application within the motor finite element system, culminating in the construction of a reduced-order model. Leveraging joint simulation technology, we achieve integrated multi-physics-based high-precision and high-computational efficiency modeling and analysis of the servo system. The primary research areas are as follows:

(1) We analyze the fundamental components and working principles of the missile servo system, conducting in-depth research on each sub-process of the electric servo system. This includes completing the modeling and analysis of key components such as motors, transmission mechanisms, and control. We employ Maxwell software to complete the motor design, validating the motor model's rationality through magnetic circuit calculation and finite element analysis results. We establish a multi-rigid body dynamic model of the transmission mechanism, importing a flexible body model into the finite element software to create a rigid-flexible coupling model, and conduct comparative verification. In the control aspect, we establish a fuzzy PID control algorithm and compare and analyze its system response with traditional PID control.

(2) To enhance the computational efficiency of the finite element model, we utilize the model reduction method based on the Kriging model to optimize the finite element model. By constructing a motor finite element model, we perform motor simulation and construct a training dataset from the simulation results. After collecting sample points that satisfy the requirements, we apply the Kriging method to construct a response surface model. Subsequently, we predict the response of unknown points through the built motor finite element model, assessing the reduced-order model's performance. We then replace the motor model with the trained reduced-order model for simulation and compare the reduced-order model with motor simulation results based on other methods, validating the effectiveness of the reduction method.

(3) Using joint simulation technology, we integrate the main component models on the Simplorer platform to establish a multi-physics-based model of the missile servo system. Through the working principles between the main component models, we achieve integrated simulation. We compare the simulation results with physical test results, not only verifying the reliability of the multi-physics-based model but also reducing the number of system iterations and optimizing the system design process.

This research establishes models of the main components of the servo system and conducts analysis. We build a multi-physics-based model through joint simulation and improve the model's computational efficiency through model reduction methods. Through the analysis of joint simulation results, we ultimately achieve the simulation and verification of the missile servo system multi-physics-based model.