Purpose

To improve the robustness of missile control system and reduce the error, a missile attitude adaptive control method based on active disturbance rejection control technology (ADRC) and BP neural network is innovatively proposed.

Design/methodology/approach

ADRC improves the performance of the missile control system by estimating and eliminating the total disturbance of the system. BP neural network adjusts the parameters of ADRC controller according to the state of the system to realize adaptive control. Based on the control system and missile dynamics model, the convergence analysis of the extended state observer and the stability analysis of the closed-loop system after embedding BP neural network are given.

Findings

The simulation results show that the adaptive control method can adjust the coefficient of error feedback rate according to the system input, output and error change rate, which accelerates the response speed of missile attitude angle and reduces the attitude angle error.

Practical implications

BP–ADRC further improves the robustness and environmental adaptability of the missile control system. The BP–ADRC control method proposed in this paper is proved feasible.

Originality/value

Different from the traditional ADRC, the BP–ADRC feedback signal proposed in this paper uses the output signal and its rate of the closed-loop system instead of the system state quantity estimated by extended state observer (ESO). This innovative method combined with BP neural network can make the system output meet the requirements when ESO has errors in the estimation of missile dynamics model.

中文翻译：

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基于BP-ADRC的导弹姿态自适应控制

目的

为了提高导弹控制系统的鲁棒性，减少误差，提出了一种基于主动抗扰控制技术和BP神经网络的导弹姿态自适应控制方法。

设计/方法/方法

ADRC通过估计和消除系统的总干扰来改善导弹控制系统的性能。BP神经网络根据系统状态调整ADRC控制器的参数，以实现自适应控制。基于控制系统和导弹动力学模型，给出了扩展状态观测器的收敛性分析和嵌入BP神经网络后的闭环系统的稳定性分析。

发现

仿真结果表明，自适应控制方法可以根据系统的输入，输出和误差变化率来调整误差反馈率的系数，从而加快了导弹姿态角的响应速度，减小了姿态角误差。

实际影响

BP–ADRC进一步提高了导弹控制系统的鲁棒性和环境适应性。本文提出的BP-ADRC控制方法是可行的。

创意/价值

与传统的ADRC不同，本文提出的BP-ADRC反馈信号使用闭环系统的输出信号及其速率，而不是使用扩展状态观测器（ESO）估算的系统状态量。这种创新的方法与BP神经网络相结合，可以在ESO导弹动力学模型估计出错时使系统输出满足要求。