Improved particle swarm algorithm for automatic optimization of control law parameters of unmanned aircraft

Abstract

The invention relates to an improved particle swarm algorithm for automatic optimization of control law parameters of an unmanned aircraft. The particle swarm algorithm is based on an iterative optimized algorithm; control parameters are initialized to be a group of random solutions; and optimal values are found by iteration; the improved particle swarm algorithm is characterized by comprising the following steps of: in the particle swarm algorithm, introducing a crossover operator, selecting a plurality of particles in which the optimal position value of single particle is positioned in the middle to carry out random two-two crossover; generating offspring particles with the same number; replacing parent particles with offspring particles; selecting crossover time according to convergent algebra of the standard particle swarm algorithm, namely, 17 to 20 generations for pitching and yawing channel and 7 to 10 generations for rolling channel; and selecting algebra of disturbing start as follows: starting from twelfth generation for the pitching and yawing channel and starting from fourth generation for the rolling channel. The improved particle swarm algorithm expands the range of understanding the space, has total searching capacity; the obtained parameters can meet given performance indexes; and the algorithm can save design time and has application value of engineering.

Description

Technical field: [0001] The invention relates to an improved particle swarm group (PSO) algorithm for automatic optimization of control law parameters of unmanned aircraft, which belongs to the flight control technology of unmanned aircraft. Background technique: [0002] The general design method of unmanned aircraft control law is to take a certain number of typical operating points in the entire flight envelope, and then perform small disturbance linearization on each operating point. For the linearization mathematical model, time domain or frequency domain can be used The design method in the paper obtains the control parameters that meet the performance requirements, and finally uses the method of gain adjustment to obtain the full-envelope flight control law. Therefore, when the control structure of the UAV is determined, designing control parameters with good performance becomes a major task. [0003] The performance indicators of the unmanned aircraft control system...

AI Technical Summary

Problems solved by technology

[0003] The performance indicators of the unmanned aircraft control system include: time domain indicators (rise time t r , peak time t p , steady state time t s , overshoot σ p , Steady-state error e ss etc.), frequency domain index (amplitude margin G m , phase margin P m , cut-off frequency ω c , bandwidth, etc.), when the above performance indicators cannot be fully satisfied, a compromise needs to be made to meet the most important indicators. Therefore, no matter using the classical design method in the time domain or the frequency domain, manually designing the control law parameters is the same. It is a meticulous and time-consuming work, and it is affected by subjective factors such as the designer's experience

For the new generation of unmanned combat aircraft, due to its characteristics of large angle of attack, large overload, and large airspace operations, the aerodynamic parameters change drastically, and the full-envelope linearization operating points may be as high as hundreds (more during operating point verification) , the manual design of control parameters becomes a very cumbersome and sometimes even unacceptable task

In this case, the automatic design of control parameters using optimization algorithms has become an effective way to solve this problem. Among them, the particle swarm optimization (PSO-Particle Swarm Optimization) algorithm has been paid more and more attention due to its excellent performance in PID control parameter optimization. Compared with GA, the particle swarm optimization algorithm has a simple structure and fast operation speed, and is especially suitable for the application of automatic design of control parameters. However, the standard particle swarm optimization algorithm is easy to fall into local optimum when optimizing the multi-channel control law parameters of unmanned aircraft. , the success rate is not high

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