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兵工学报 ›› 2023, Vol. 44 ›› Issue (7): 2184-2196.doi: 10.12382/bgxb.2022.0229

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面向输出约束基于神经网络观测器的发射平台输出反馈控制

宋秋雨1, 胡健1,2,*(), 姚建勇1, 白艳春1, 杨正银1   

  1. 1 南京理工大学 机械工程学院, 江苏 南京 210094
    2 中南大学 高性能复杂制造国家重点实验室, 湖南 长沙 410083

Output Feedback Control for Launch Platform Using Neural Network Observer and Output Constraint

SONG Qiuyu1, HU Jian1,2,*(), YAO Jianyong1, BAI Yanchun1, YANG Zhengyin1   

  1. 1 School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
    2 State Key Laboratory of High-Performance Complex Manufacturing, Central South University, Changsha 410083, Hunan, China
  • Received:2022-04-03 Online:2023-07-30

摘要:

一种由两台永磁同步电机驱动的发射平台用于发射动能载荷,但该平台在实际运行中经常面临比较强的参数不确定性和气流冲击等未知的外部干扰,大大降低其跟踪精度。针对这一问题,提出一种用于发射平台高精度运动控制的考虑输出约束的基于自适应神经网络观测器输出反馈控制器。该控制器采用扩展状态观测器估计系统中的参数不确定性,同时利用其观测的系统速度值设计相关控制量,从而达到输出反馈控制的目的;另外设计一种改进的样条小脑模型关节控制器神经网络(Spline CMAC)对系统中的未知扰动进行估计,由此利用前馈补偿技术对参数不确定性和时变扰动进行补偿。考虑到发射平台在实际情况中遇到的输出约束问题,采用障碍Lyapunov函数分析法设计控制率并证明了系统的稳定性。仿真和实验结果表明:在考虑输出约束的条件下,新的复合控制器能够实现系统的一致最终有界稳定,且跟踪性能很好,并具有很强的抗干扰能力,相比于传统的控制方法有很大的提升。

关键词: 发射平台, 输出反馈控制, 神经网络, 输出约束, 障碍Lyapunov函数

Abstract:

A launch platform, driven by two permanent magnet synchronous motors, is used to launch kinetic energy loads. However, the platform often faces strong parameter uncertainties and unknown external disturbances, such as air impact, during practical operation. These factors can greatly reduce the platform’s tracking accuracy. To solve this problem, an output feedback controller based on adaptive neural network observer with output constraints is proposed for high-precision motion control of the launch platform. The controller uses ESO to estimate system parameter uncertainties in the system, and uses the velocity value of the system to design relevant control parameters to achieve the purpose of output feedback control. An improved Spline CMAC neural network is designed to estimate the unknown disturbances in the system. Feedforward compensation technique is used to compensate parameter uncertainty and time-varying disturbance. Considering the output constraints of the launch platform in practice, the control rate is designed by using the obstacle Lyapunov function analysis method. The stability of the system is also proved. The simulation and experimental results demonstrate that the proposed composite controller can achieve uniform final bounded stability of the system with good tracking performance and strong anti-interference capability while considering the output constraints. The proposed approach represents a great improvement over traditional control methods.

Key words: launch platform, output feedback control, cerebellar model articulation controller neural network, output constraint, barrier lyapunov function