武器站伺服传动装置在线辨识与自适应控制方法

doi:10.12382/bgxb.2023.0342

摘要/Abstract

摘要：

针对武器站俯仰轴伺服传动装置在武器载荷变化时控制精度和安全性下降的问题,提出基于多参数在线辨识的自适应复合控制方法。基于符号函数的线性化方法,构建包含传动间隙、电机和负载摩擦非线性的伺服传动装置待辨识模型框架,推导出待辨识参数的显式迭代方程,通过带遗忘因子的递推增广最小二乘算法实现参数在线辨识。在此基础上,提出自适应比例积分控制器与自适应状态扩张卡尔曼滤波器相结合的复合控制方法。实验结果表明:参数辨识方法可实现伺服传动装置9个关键动力学参数的精确在线辨识,稳态辨识误差不超过10%。自适应复合控制方法将系统速度跟随残差均方根降低了28.24%,有效提升了俯仰轴在武器载荷变化时的控制精度和稳定裕度。

关键词: 武器站, 伺服传动装置, 在线辨识, 自适应控制

Abstract:

The control accuracy and safety of pitch axis servo transmission device in weapon stations may be reduced when the weapon load changes. An adaptive composite control method based on multi-parameter online identification is proposed for the issues above. A model framework for identifying the servo transmission devices with nonlinear transmission clearance, motor, and load friction is constructed based on the linearization method of symbolic functions. The explicit iterative equations for the identified parameters are derived, and the online parameter identification is achieved using a recursive augmented least squares algorithm. On this basis, a composite control method combining an adaptive proportional integral controller and an adaptive state-augmented Kalman filter is proposed. The experimental results show that the parameter identification method can achieve the accurate online identification of 9 key dynamic parameters of servo transmission devices, with steady-state identification errors not exceeding 10%. The adaptive composite control method reduces the root mean square of the system speed following residual by 28.24%, effectively improving the control accuracy and stability margin of the pitch axis when the weapon load changes.

Key words: weapon station, servo transmission device, online identification, adaptive control

中图分类号:

TH132

谢馨, 郑杰基, 李宝宇, 于滨, 范大鹏. 武器站伺服传动装置在线辨识与自适应控制方法[J]. 兵工学报, 2024, 45(6): 1761-1775.

XIE Xin, ZHENG Jieji, LI Baoyu, YU Bin, FAN Dapeng. Online Identification and Adaptive Control Method for Servo Transmission Device in Weapon Station[J]. Acta Armamentarii, 2024, 45(6): 1761-1775.

图/表 17

图1 典型武器站

Fig.1 Typical weapon station

图2 参数在线辨识模型框架

Fig.2 Framework of parameters online identification model

图3 自适应复合控制方法框架

Fig.3 Framework of adaptive composite control method

图4 传动间隙反转运动测量法原理

Fig.4 Principle of measurement method for transmission backlash reverse motion

图5 自适应复合控制器框架

Fig.5 Framework of adaptive composite controller

图6 API框架

Fig.6 Framework of adaptive proportional integral controller

图7 伺服传动装置测试平台

Fig.7 Servo transmission device testing platform

表1 伺服传动装置测试平台的部件型号与参数值

Table 1 Component models and parameter values of servo transmission device testing platform

部件名称	型号	参数	数值
		额定功率/W	750
		额定转矩/(N·m)	2.39
永磁同步电机	ASM80B1007-30M	额定转速/(r·min^-1)	3000
		转矩常数/ (N·m·A^-1)	0.48
		转动惯量/(kg·m²)	1.14×10^-4
驱动器	YD8000	转换系数/(A·V^-1)	0.49
		额定扭矩/(N·m)	150
联轴器	GSG-82×68	容许转速/(r·min^-1)	4000
		扭转刚度/ (N·m·rad^-1)	168000
		转动惯量/(kg·m²)	2.7×10^-4
		减速比	161
		最大输出转矩/(N·m)	450
精密减速器	ZKRV-20E-161-B	最大输出转速/ (r·min^-1)	75
		传动间隙/arcmin	≤1
		传动刚度/ (N·m·rad^-1)	5×10⁵
绝对编码器	BCE112K50	角度分辨率/bit	19
惯量盘		转动惯量/(kg·m²)	0.22
砝码		质量/kg	5
		个数	12

表2 仿真模型待辨识参数设定值

Table 2 Set values of parameters to be identified in the simulation model

参数/单位	空载	带载
负载转动惯量J_l/(kg·m²)	0.22	0.45
传动刚度K_s/(N·m·rad^-1)	1×10⁶	1×10⁶
电机黏滞阻尼系数B_m/(N·m·rad^-1·s)	0.005	0.005
负载黏滞阻尼系数B_l/(N·m·rad^-1·s)	20	20
电机正库伦摩擦力矩 $T f c m +$ /(N·m)	0.1	0.1
电机负库伦摩擦力矩 $T f c m -$ /(N·m)	-0.1	-0.1
负载正库伦摩擦力矩 $T f c l +$ /(N·m)	5	5
负载负库伦摩擦力矩 $T f c l -$ /(N·m)	-5	-5
传动间隙2θ_b/arcmin	1	1

表2 仿真模型待辨识参数设定值

Table 2 Set values of parameters to be identified in the simulation model

参数/单位	空载	带载
负载转动惯量J_l/(kg·m²)	0.22	0.45
传动刚度K_s/(N·m·rad^-1)	1×10⁶	1×10⁶
电机黏滞阻尼系数B_m/(N·m·rad^-1·s)	0.005	0.005
负载黏滞阻尼系数B_l/(N·m·rad^-1·s)	20	20
电机正库伦摩擦力矩 $T f c m +$ /(N·m)	0.1	0.1
电机负库伦摩擦力矩 $T f c m -$ /(N·m)	-0.1	-0.1
负载正库伦摩擦力矩 $T f c l +$ /(N·m)	5	5
负载负库伦摩擦力矩 $T f c l -$ /(N·m)	-5	-5
传动间隙2θ_b/arcmin	1	1

图8 模型参数在线辨识收敛过程

Fig.8 Convergence process of online identification of model parameters

表3 模型参数在线辨识收敛过程

Table 3 Steady state convergence results of online identification of model parameters

参数	空载		带载
参数	辨识值	辨识误差/%	辨识值	辨识误差/%
负载转动惯量J_l/(kg·m²)	0.22	0	0.451	0.22
传动刚度K_s/(N·m·rad^-1)	1×10⁶	0	1×10⁶	0
电机黏滞阻尼系数B_m/(N·m·rad^-1·s)	0.0046	6.6	0.0047	6
负载黏滞阻尼系数B_l/(N·m·rad^-1·s)	20.54	2.7	20.55	2.75
电机正库伦摩擦力矩 $T f c m +$ /(N·m)	0.0904	9.6	0.091	9
电机负库伦摩擦力矩 $T f c m -$ /(N·m)	-0.1034	0.34	-0.103	0.3
负载正库伦摩擦力矩 $T f c l +$ /(N·m)	4.612	7.76	4.607	7.86
负载负库伦摩擦力矩 $T f c l -$ /(N·m)	-4.68	6.4	-4.675	6.5
传动间隙2θ_b/arcmin	1~1.1	10	1.1	10

表3 模型参数在线辨识收敛过程

Table 3 Steady state convergence results of online identification of model parameters

参数	空载		带载
参数	辨识值	辨识误差/%	辨识值	辨识误差/%
负载转动惯量J_l/(kg·m²)	0.22	0	0.451	0.22
传动刚度K_s/(N·m·rad^-1)	1×10⁶	0	1×10⁶	0
电机黏滞阻尼系数B_m/(N·m·rad^-1·s)	0.0046	6.6	0.0047	6
负载黏滞阻尼系数B_l/(N·m·rad^-1·s)	20.54	2.7	20.55	2.75
电机正库伦摩擦力矩 $T f c m +$ /(N·m)	0.0904	9.6	0.091	9
电机负库伦摩擦力矩 $T f c m -$ /(N·m)	-0.1034	0.34	-0.103	0.3
负载正库伦摩擦力矩 $T f c l +$ /(N·m)	4.612	7.76	4.607	7.86
负载负库伦摩擦力矩 $T f c l -$ /(N·m)	-4.68	6.4	-4.675	6.5
传动间隙2θ_b/arcmin	1~1.1	10	1.1	10

图9 伺服传动装置参数在线辨识收敛过程

Fig.9 Convergence process of online identification of servo transmission device parameters

表4 伺服传动装置参数在线辨识稳态收敛结果

Table 4 Steady state convergence results of online identification of servo transmission device parameters

参数	空载	带载
负载转动惯量J_l/(kg·m²)	0.246	0.453
传动刚度K_s/(N·m·rad^-1)	9.7×10⁵	8.9×10⁵
电机黏滞阻尼系数B_m/(N·m·rad^-1·s)	2.4×10^-3	2.4×10^-3
负载黏滞阻尼系数B_l/(N·m·rad^-1·s)	23.17	25.23
电机正库伦摩擦力矩 $T f c m +$ /(N·m)	0.1	0.11
电机负库伦摩擦力矩 $T f c m -$ /(N·m)	-0.095	-0.12
负载正库伦摩擦力矩 $T f c l +$ /(N·m)	1.9	2.89
负载负库伦摩擦力矩 $T f c l -$ /(N·m)	-1.86	-2.01
传动间隙2θ_b/arcmin	0.82	1.74

表4 伺服传动装置参数在线辨识稳态收敛结果

Table 4 Steady state convergence results of online identification of servo transmission device parameters

参数	空载	带载
负载转动惯量J_l/(kg·m²)	0.246	0.453
传动刚度K_s/(N·m·rad^-1)	9.7×10⁵	8.9×10⁵
电机黏滞阻尼系数B_m/(N·m·rad^-1·s)	2.4×10^-3	2.4×10^-3
负载黏滞阻尼系数B_l/(N·m·rad^-1·s)	23.17	25.23
电机正库伦摩擦力矩 $T f c m +$ /(N·m)	0.1	0.11
电机负库伦摩擦力矩 $T f c m -$ /(N·m)	-0.095	-0.12
负载正库伦摩擦力矩 $T f c l +$ /(N·m)	1.9	2.89
负载负库伦摩擦力矩 $T f c l -$ /(N·m)	-1.86	-2.01
传动间隙2θ_b/arcmin	0.82	1.74

图10 不同模型对实际系统的拟合程度对比结果

Fig.10 Comparison results of fitting degrees among different models and actual systems

表5 不同模型对实际系统的拟合误差均方根

Table 5 Root mean squares of fitting errors among different models and actual systems

工况	线性模型			本文模型
工况	参数固定不变	参数在线更新	降低/ %	参数固定不变	降低/ %	参数在线更新	降低/ %
空载	5.23	5.16	1.34	2.12	59.46	2.11	59.66
带载	6.83	4.36	36.16	3.52	48.46	2.37	65.30

图11 自适应复合控制方法验证测试结果

Fig.11 Validation test results of adaptive composite control method

表6 不同控制方法的速度跟随残差均方根

Table 6 Root mean squares of speed following residuals for different control methods

工况	定值复合控制方法	自适应复合控制方法	降低/%
空载	5.92	5.87	0.84
带载	10.34	7.42	28.24

参考文献 20

[1]	向学辅, 刘启辉, 陈浩, 等. 独立跟瞄式遥控武器站控制系统稳定性分析[J]. 兵器装备工程学报, 2021, 42(2): 168-173.
	XIANG X F, LIU Q H, CHEN H, et al. Analysis on stability of control system of remote control weapon station with independent tracking and aiming device[J] Journal of Weapon Equipment Engineering, 2021, 42(2): 168-173. (in Chinese)
[2]	刘浩, 徐宏斌, 李正宇, 等. 无人武器站的轻量化设计[J]. 弹箭与制导学报, 2021, 41(2): 64-67.
	LIU H, XU H B, LI Z Y, et al. Lightweight design of unmanned weapon station[J] Journal of Missile and Guidance, 2021, 41(2): 64-67. (in Chinese)
[3]	WU Y C, ZHANG Z Y, XIANG X F. Research on intelligent decision of fire distribution in remote control weapon station[J]. Journal of Physics Conference Series, 2020, 1507: 072011.
[4]	MAO B Q, WANG Z Q, CHANG L, et al. Research on muzzle dynamic analysis of an overhead weapon station with the viscoelastic elastomer damper[J]. Journal of Physics: Conference Series, 2020, 1507(10): 102042 (19pp).
[5]	HARANDI M R J, KHALILPOUR S A, TAGHIRAD H D, et al. Adaptive control of parallel robots with uncertain kinematics and dynamics[J]. Mechanical Systems and Signal Processing, 2021, 157: 107693.
[6]	程勇, 孙汉旭, 叶平, 等. 未知负载转矩下机械臂关节参数在线辨识及控制参数在线研究[J]. 机械设计与制造, 2016(11): 30-33,37.
	CHENG Y, SUN H X, YE P, et al. Research on parameter identification and control parameters optimization of robot joints with unknown load torque[J] Machinery Design & Manufacture, 2016(11): 30-33,37. (in Chinese)
[7]	程善美, 张益. 基于协同粒子群算法的PMSM在线参数辨识[J]. 电气传动, 2012, 42(11).
	CHENG S M, ZHANG Y. Collaborative particle swarm optimization based online parameter identification applied to PMSM[J] Electric Drive, 2012, 42(11). (in Chinese)
[8]	熊琰, 李叶松. 基于粒子群优化的伺服谐振系统的参数辨识[J]. 华中科技大学学报(自然科学版), 2014, 42(12): 111-115.
	XIONG Y, LI Y S. Parameters identification of servo system with resonance based on particle swarm optimization[J] Journal of Huazhong University of Science and Technology (Natural Science Edition), 2014, 42(12): 111-115. (in Chinese)
[9]	BENOSMAN M. Model-based vs data-driven adaptive control: an overview[J]. International Journal of Adaptive Control and Signal Processing, 2018, 32(5): 753-776.
[10]	ZHENG S Q, TANG X Q, SONG B, et al. Stable adaptive PI control for permanent magnet synchronous motor drive based on improved JITL technique[J]. ISA Transactions, 2013, 52(4): 539-549. doi: 10.1016/j.isatra.2013.03.002 pmid: 23659836
[11]	MOFID O, MOBAYEN S. Adaptive sliding mode control for finite-time stability of quad-rotor UAVs with parametric uncertainties[J]. ISA Transactions, 2018, 72: 1-14. doi: S0019-0578(17)30617-1 pmid: 29224853
[12]	XIE W, CABECINHAS D, CUNHA R, et al. Adaptive backstepping control of a quadcopter with uncertain vehicle mass, moment of inertia, and disturbances[J]. IEEE Transactions on Industrial Electronics, 2022, 69(1): 549-559.
[13]	WANG Z F, YANG G L, WANG X Y, et al. Adaptive robust control for triple avoidance-striking-arrival performance of uncertain tank mechanical systems[J]. Defence Technology, 2022, 18(8): 1483-1497.
[14]	ALVARO-MENDOZA E, DE LEÓN-MORALES J, SALAS-PEÑA O. State and parameter estimation for a class of nonlinear systems based on sliding mode approach[J]. ISA Transactions, 2021, 112: 99-107.
[15]	ZHANG H Z, ZHANG W, ZHAO Y N, et al. Adaptive state observers for incrementally quadratic nonlinear systems with application to chaos synchronization[J]. Circuits, Systems, and Signal Processing, 2020, 39(3): 1290-1306.
[16]	ZHANG R. An accurate and fast filtering algorithm based on adaptive compensation observer[C]∥Proceedings of the 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). Chengdu, China:IEEE,2019: 2324-2328.
[17]	VILLWOCK S, PACAS M. Time-domain identification method for detecting mechanical backlash in electrical drives[J]. IEEE Transactions on Industrial Electronics, 2009, 56(2): 568-573.
[18]	祁超, 范世珣, 谢馨, 等. 光电稳定平台伺服机构低速及稳定性能控制方法研究[J]. 兵工学报, 2018, 39(10): 1873-1882. doi: 10.3969/j.issn.1000-1093.2018.10.001
	QI C, FAN S X, XIE X, et al. Research on control method for improving low speed performance and stable precision of electro-optic servo system[J]. Acta Armamentarii, 2018, 39(10): 1873-1882. (in Chinese) doi: 10.3969/j.issn.1000-1093.2018.10.001
[19]	ERKORKMAZ K, ALTINTAS Y. High speed CNC system design. Part Ⅱ: modeling and identification of feed drives[J]. International Journal of Machine Tools and Manufacture, 2001, 41(10): 1487-1509.
[20]	YANG M, LIU Z R, LONG J, et al. An algorithm for online inertia identification and load torque observation via adaptive Kalman observer-recursive least squares[J]. Energies, 2018, 11(4): 778.