Research on Multi-parameter identification of underwater robot joint motor
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摘要: 随着水下无人系统的快速发展, 水下关节电机作为水下机器人、水下机械臂等水下装备的核心驱动装置发挥着重要的作用。文中针对不同工作环境影响下导致的水下关节电机参数改变, 从而引起的电机控制的精确性和稳定性变差的问题, 开展电机多参数在线辨识研究。采用增加稳态工作点方法实现多参数的满秩辨识。同时, 为提高辨识方法的精度和鲁棒性, 研究了扩展卡尔曼滤波(EKF)方法和H∞方法在电机参数辨识方面的可行性, 进而提出了一种基于EKF和H∞的联合估计方法。通过仿真对比发现, 在参数辨识时, 所提出的联合估计方法相较于自适应EKF方法稳态标准差最大减少了84.7%, 相较于自适应H∞方法精确度最大提升了91.7%。验证联合估计方法优秀的鲁棒性和准确性, 可为水下关节电机的稳定高效运行提供理论和技术支撑。Abstract: With the rapid development of underwater unmanned systems, underwater joint motor plays an important role as the core driving device of underwater robot, underwater robot arm and other underwater equipment. In this paper, the multi-parameter on-line identification of underwater articulated motor is studied to solve the problem that the precision and stability of motor control are deteriorated due to the change of motor parameters under the influence of different working environments. Specifically, the method of increasing steady-state operating points is used to realize multi-parameter full rank identification. At the same time, in order to improve the accuracy and robustness of the identification method, the feasibility of Extended Kalman filter(EKF) and H-infinity filter (HIF) in the identification of motor parameters was studied. Then a new method based on EKF and H infinite joint estimation is proposed. Through simulation comparison, it is found that in parameter identification, the steady-state standard deviation of the proposed joint estimation method is reduced by 84.7% compared with the adaptive EKF method, and the accuracy is increased by 91.7% compared with the adaptive HIF method. The robustness and accuracy of the proposed method based on EKF and H infinite joint estimation are verified, which provides theoretical and technical support for the stable and efficient operation of the underwater joint motor.
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图 1 于EKF和H∞的联合估计算法流程
Figure 1. Process based on EKF and H infinite joint estimation algorithm
图 2 基于多稳态工作点的满秩参数辨识策略
Figure 2. Full rank parameter identification based on multi-stable operating points
图 3 不同工况下不同算法辨识结果对比
Figure 3. Comparison of identification results of different working conditions
图 4 额定工况下不同方法辨识结果曲线
Figure 4. Identification results of different identification methods
图 5 额定工况下的定子电感辨识性能对比
Figure 5. Comparison of stator inductance identification performance
图 6 额定工况下的定子电阻辨识性能对比
Figure 6. Comparison of stator resistance identification performance
图 7 额定工况下的永磁体磁链辨识性能对比
Figure 7. Comparison of flux identification performance of permanent magnet
表 1 仿真系统中的PMSM参数
参数 数值 参数 数值 额定电压/V 24 d轴电感/µH 340 额定电流/A 2.5 q轴电感/µH 340 额定扭矩/(N·m) 0.3 定子电阻/mΩ 650 额定转速/(r/m) 1 200 转子磁链/Wb 0.003 3 转动惯量/(kg·m2) 1.8×10−5 极对数 14 
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表 2 额定工况下的不同辨识方法性能对比
Table 2. Comparison of the performance of three identification methods
参数 方法 均值 相对误差 标准差 均方根误差 ${L_{\text{s}}}$/uH AEKF 339.040 0.28% 4.264 4.370 0 AHIF 342.840 0.84% 0.900 2.980 0 AEKF+AHIF 339.260 0.22% 2.232 2.350 0 ${R_{\text{s}}}$/mΩ AEKF 651.400 0.22% 1.510 2.040 0 AHIF 694.520 6.85% 0.970 54.220 0 AEKF+AHIF 653.700 0.57% 1.030 3.850 0 ${\psi _{\text{f}}}$/(m·Wb) AEKF 3.293 1.11% 0.438 0.438 3 AHIF 3.194 4.08% 0.028 0.109 5 AEKF+AHIF 3.294 1.08% 0.067 0.067 7
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