基于BP PID改进的噪声稳健控制方法

邹晶晶

基于BP PID改进的噪声稳健控制方法

引用本文：邹晶晶.基于BP PID改进的噪声稳健控制方法[J].计算技术与自动化,2025,(2):66-73

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作者	单位
邹晶晶	（广东科学技术职业学院,广东珠海 519000）

中文摘要:针对BP PID控制器的控制性能受参数初值影响大、易陷入局部极值、对噪声敏感,且低信噪比条件下控制稳定性差等问题，提出了一种基于改进果蝇优化算法(Improved Fruit Fly Optimization Algorithm, IFOA)和径向基神经网络卡尔曼滤波(Radial Basis Function Network Kalman Filter, RBF KF)的噪声稳健BP PID控制方法。首先提出了一种IFOA随机搜索算法对BP PID初值进行全局寻优，自动获得全局最优解，提升系统控制精度。然后利用所提RBF KF对观测数据进行滤波平滑，降低量测和控制噪声对系统的影响，提升低信噪比条件下的控制稳定性。基于某智能车车速控制真实数据开展试验，结果表明，所提方法相对于传统方法控制精度提升超过50%，控制稳定性提升超过60%，并且在低信噪比条件下优势更加明显，更适合实际工程应用场景。

中文关键词:BP PID 车速控制果蝇优化算法噪声稳健卡尔曼滤波

Improved Noise Robust Control Method Based on BP PID

Abstract:The control performance of the BP PID controller is greatly affected by the initial parameter values, easily trapped in local extremum, sensitive to noise,and the control stability is poor under low signal to noise ratio conditions. Propose a noise robust BP PID control method based on the improved fruit fly optimization algorithm (IFOA) and radial basis function network Kalman filter (RBF KF). Firstly, propose an IFOA random search algorithm to globally optimize the initial value of BP PID, automatically obtain the global optimal solution, and improve the system control accuracy, Then, the proposed RBF KF is used to filter and smooth the observed data, reduce the impact of measurement and control noise on the system, and improve control stability under low signal to noise ratio conditions. Based on real data of a certain intelligent vehicle’s speed control, experiments are conducted. The results show that the proposed method improves control accuracy by more than 50% and control stability by more than 60% compared to traditional methods, and the advantages are more obvious under low signal to noise ratio conditions, more suitable for practical engineering application scenarios.

keywords:BP PID speed control fruit fly optimization algorithm noise robust Kalman filter

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