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

doi:10.12382/bgxb.2022.0229

摘要/Abstract

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

宋秋雨, 胡健, 姚建勇, 白艳春, 杨正银. 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[J]. 兵工学报, 2023, 44(7): 2184-2196.

SONG Qiuyu, HU Jian, YAO Jianyong, BAI Yanchun, YANG Zhengyin. Output Feedback Control for Launch Platform Using Neural Network Observer and Output Constraint[J]. Acta Armamentarii, 2023, 44(7): 2184-2196.

图/表 16

图1 发射平台系统框图

Fig.1 Block diagram of launch platform system

图2 一个三层二输入的Spline CMAC神经网络:只显示由输入索引的激活基函数

Fig.2 A spline CMAC neural network with three layers and two inputs-only the activated basis functions indexed by the input are illustrated

图3 假设预测是正确的(索引从左向右移动):在时间1,索引基函数预测下一个索引单元格将在右边(图3(a));在时间2,原始的基函数仍然被激活(图3(b)),它与时间2索引层上的新基函数重叠(图3(c))

Fig.3 Assuming that the prediction is correct (the index moves from left to right): the index base function predicts that the next index cell will be on the right at time 1 (Fig. 3(a)); the original base function is still active at time 2 (Fig. 3(b)), which overlaps with the new base function on the index layer at time 2 (Fig. 3(c))

图4 控制策略图

Fig.4 Control strategy diagram

表1 发射平台机电作动系统的参数值

Table 1 Parameter values of the electromechanical actuation system of the launch platform

系统参数	数值
电机端转动惯量J/(kg·m²)	0.031
电机力矩系数k_u/(N·m·V^-1)	1.04
黏性摩擦系数B/(N·m·s·rad^-1)	0.035
减速比	190

图5 指令信号输出曲线

Fig.5 Output curve of the command signals

图6 OF_ESO_ Spline CMAC的位置曲线

Fig.6 Position curve of OF_ESO_ Spline CMAC

图7 OF_ESO_ Spline CMAC的跟踪误差

Fig.7 Tracking error of OF_ESO_ Spline CMAC

图8 3种神经网络估计值与误差对比

Fig.8 Comparison of three neural network estimates

表2 仿真跟踪误差指标

Table 2 Simulation tracking error index (°)

控制器	最大值	平均值	标准方差
OF_ESO	0.01077	0.00299	0.005498
OF_ESO_RBF	0.006459	0.001493	0.003516
OF_ESO_CMAC	0.005864	0.001278	0.003234
OF_ESO_ Spline CMAC	0.003762	0.0003677	0.002393

图9 4种控制器的跟踪误差对比

Fig.9 Comparison of tracking errors of four controllers

图10 发射平台以及基于DSP的控制电路板(上为发射平台,下为控制电路板)

Fig.10 Launch platform and control circuit board based on DSP (the top is the launch platform, and the bottom is the control circuit board)

表3 发射平台实验装置机电作动系统的系统参数

Table 3 System parameter values ofthe electromechanical actuation system of the launching platform

系统参数	数值
电机端转动惯量J/(kg·m²)	0.031
电机力矩系数k_u/(N·m·V^-1)	1.04
黏性摩擦系数B/(N·m·s·rad^-1)	0.035
减速比	190
定子电阻R/Ω	1.32
绕组电感L/H	0.0311
额定电流I/A	3.1

图11 工况Ⅰ下5种控制器跟踪误差对比

Fig.11 Comparison of tracking errors of five controllers under the working condition Ⅰ

图12 工况Ⅱ下5种控制器跟踪误差对比

Fig.12 Comparison of tracking errors of five controllers under the working condition Ⅱ

图13 工况Ⅲ下5种控制器跟踪误差对比

Fig.13 Comparison of tracking errors of five controllers under the working condition Ⅲ

参考文献 23

[1]	李自勇, 戴田国, 马大为, 等. 新型火箭发射平台动力学仿真与优化研究[J]. 火炮发射与控制学报, 2008(1):39-41.
	LI Z Y, DAI T G, MA D W, et al. Dynamics simulation and optimization of new rocket launch platform[J]. Journal of Artillery Launch and Control, 2008(1):39-41. (in Chinese)
[2]	侯孝涵, 杨兴华, 杨喜军, 等. 基于新型趋近律的PMSM反馈线性化滑模控制[J]. 微电机, 2019, 52(12):45-48.
	HOU X H, YANG X H, YANG X J, et al. PMSM feedback linearized sliding mode control based on new reaching law[J]. Micromotors, 2019, 52(12):45-48. (in Chinese)
[3]	张海刚, 胡添添, 王步来, 等. 一种改进的PMSM滑模变结构控制策略研究[J]. 电气传动, 2019, 49(10):13-15.
	ZHANG H G, HU T T, WANG B L, et al. An improved sliding mode variable structure control strategy for PMSM[J]. Electric Drive, 2019, 49(10):13-15. (in Chinese)
[4]	HU C X, YAO B, WANG Q F. Adaptive robust precision motion control of systems with unknown input dead-zones: a case study with comparative experiments[J]. IEEE Transactions on Industrial Electronics, 2015, 58(6):2454-2464. doi: 10.1109/TIE.2010.2066535 URL
[5]	韩京清. 自抗扰控制技术——估计补偿不确定因素的控制技术[M]. 北京: 国防工业出版社, 2008.
	HAN J Q. Active disturbance rejection control technology-control technology for estimating and compensating uncertain factors[M]. Beijing: National Defense Industry Press, 2008. (in Chinese)
[6]	LI S H, LIU Z G. Adaptive speed control for permanent-magnet syn chronous motor system with variations of load inertia[J]. IEEE Transactions on Industrial Electronics, 2009, 56(8):3050-3059. doi: 10.1109/TIE.2009.2024655 URL
[7]	UNDERWOOD S J, HUSAIN I. Online parameter estimation and adaptive control of permanent-magnet synchronous machines[J]. IEEE Transactions on Industrial Electronics, 2010, 57(7):2435-2443. doi: 10.1109/TIE.2009.2036029 URL
[8]	ALYAQOUT S F, ALYAQOUT P Y, ULSOY A. G. Combined robust design and robust control of an electric DC motor[J]. IEEE-ASME Transactions on Mechatronics, 2011, 16(3):574-582. doi: 10.1109/TMECH.2010.2047652 URL
[9]	ERROUISSI R, OUHRIUCHE M, CHEN W H, et al. Robust nonlinear predictive controller for permanent-magnet synchronous motors with an optimized cost function[J]. IEEE Transactions on Industrial Electronics, 2012, 59(7):2849-2858. doi: 10.1109/TIE.2011.2157276 URL
[10]	朱玉川, 马大为, 李志刚, 等. 带积分项的火箭炮最优滑模伺服控制[J]. 兵工学报, 2007, 28(10):1272-1275.
	ZHU Y C, MA D W, LI Z G, et al. Optimal sliding mode servo control for rocket launcher with integral term[J]. Acta Armamentarii, 2007, 28 (10):1272-1275. (in Chinese)
[11]	柴华伟, 马大为, 李志刚, 等. 交流伺服系统最优内模滑模控制器设计与应用[J]. 南京航空航天大学学报, 2007, 39(4):510-513.
	CHAI H W, MA D W, LI Z G, et al. Design and application of optimal internal mode sliding mode controller for AC servo system[J]. Journal of Nanjing University of Aeronautics and Astronautics, 2007, 39(4):510-513. (in Chinese)
[12]	SUN W C, GAO H J, KAYNAK O. Adaptive backstepping control for active suspension systems with hard constraints[J]. IEEE/ASME Transactions on Mechatronics, 2013, 18(3):1072-1079. doi: 10.1109/TMECH.2012.2204765 URL
[13]	HAN J Q. From PID to active disturbance rejection control[J]. IEEE Transactions on Industrial Electronics, 2009, 56(3):900-906. doi: 10.1109/TIE.2008.2011621 URL
[14]	GAO Z Q, HUANG Y, HAN J Q. An alternative paradigm for control system design[C]// Proceedings of IEEE Confrences Decision Control on Orlando. FL, US:IEEE, 2001:4578-4585.
[15]	郑颖, 马大为, 姚建勇, 等. 火箭炮两轴耦合位置伺服系统线性自抗扰控制[J]. 兵工学报, 2015, 36(6):987-993. doi: 10.3969/j.issn.1000-1093.2015.06.004
	ZHENG Y, MA D W, YAO J Y, et al. Linear active disturbance rejection control of two-axis coupled position servo system for rocket launcher[J]. Acta Armamentarii, 2015, 36(6):987-993. (in Chinese) doi: 10.3969/j.issn.1000-1093.2015.06.004
[16]	LIU X, YANG C G, CHEN Z G, et al. Neuro-adaptive observer based control of flexible joint robot[J]. Neurocomputing, 2018, 275:73-82. doi: 10.1016/j.neucom.2017.05.011 URL
[17]	YAO J Y, JAO Z X, MA D W. Output feedback robust control of direct current motors with nonlinear friction compensation and disturbance rejection[J]. Journal of Dynamic Systems,Measurement,and Control, 2015(4):1-8.
[18]	侯捷, 陈谋, 刘楠. 基于径向基函数神经网络与扩张状态观测器的无人直升机控制[J]. 控制理论与应用, 2021, 38(9): 1361-1371.
	HOU J, CHEN M, LIU N. Control of unmanned helicopter based on radial basis function neural network and extended state observer[J]. Control Theory & Applications, 2021, 38(9): 1361-1371. (in Chinese)
[19]	李彬, 徐怡杭, 罗杰. 采用残差神经网络的无人机遥控信号识别监测算法[J]. 西安交通大学学报, 2021, 55(12):146-154.
	LI B, XU Y H, LUO J. Recognition and monitoring algorithm of uav remote control signal using residual neural network[J]. Journal of Xi’an Jiaotong University, 2021, 55(12):146-154. (in Chinese)
[20]	ALBUS J. Data storage in the cerebellar model articulation controller(CMAC)[J]. Journal of Dynamic Systems, Measurement, and Control, 1975, 97:228-233. doi: 10.1115/1.3426923 URL
[21]	ZHAO Z N, WANG Z P, ZHANG C B, et al. Simulation and experimental research of digital valve control servo system based on CMAC-PID control method[J]. High Technology Letters, 2017, 23(3):306-314.
[22]	MACNAB C. Preventing bursting in approximate-adaptive control when using local basis functions[J]. Fuzzy Sets And Systems, 2009, 160:439-462. doi: 10.1016/j.fss.2008.03.021 URL
[23]	陈强, 丁科新, 南余荣. 带有输出约束的柔性关节机械臂预设性能自适应控制[J]. 控制与决策, 2021, 36(2):387-394.
	CHEN Q, DING K X, NAN Y R. Adaptive control of preset performance of flexible joint manipulator with output constraints[J]. Control and Decision, 2021, 36(2):387-394. (in Chinese)

编辑推荐 0

Metrics

阅读次数

全文

128

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	2	26	0	100

来源	本网站	其他网站

次数	70	58
比例	55%	45%

摘要

255

最新录用	在线预览	正式出版

29	0	226

来源	本网站	其他网站

次数	55	200
比例	22%	78%