ESO-Based Robust Model Predictive Control for Undersea Vehicle Manipulator System
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摘要: 考虑到海洋环境的复杂性、不确定性及水下机器人机械臂系统(UVMS)的强非线性、强耦合性等特点, 提出一种基于扩张状态观测器(ESO)的鲁棒模型预测控制(RMPC)方法。首先基于UVMS的动力学特性, 建立其动力学模型, 并忽略不确定项和干扰给出其名义模型系统。然后, 基于名义系统设计了RMPC算法。将原系统的不确定项、干扰以及建模误差等影响因素集总为扩张状态, 设计了ESO对其进行估计, 并在名义模型的RMPC基础上进行了补偿, 以得到应用于UVMS系统的RMPC方法。最后通过仿真实验证明, 基于ESO的RMPC具有很好的轨迹跟踪性能和抗扰动能力。
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关键词:
- 水下机器人机械臂系统 /
- 鲁棒模型预测控制 /
- 扩张状态观测器 /
- 轨迹跟踪
Abstract: In view of the complexity and uncertainty of the marine environment and the strong nonlinearity and coupling of the undersea vehicle manipulator system(UVMS), this paper proposed a robust model predictive control(RMPC) method based on extended state observer(ESO). First, a dynamics model was established based on the dynamics characteristics of UVMS, and a nominal model system was defined by ignoring uncertainties and disturbances. Then, a UVMS algorithm was designed for the nominal system. The uncertainties, disturbances, modeling errors, and other influencing factors of the original system were summarized into extended states, and an ESO was designed to estimate these factors. Furthermore, the factors were compensated based on the RMPC of the nominal model, so as to obtain the RMPC method applied to the UVMS system. Finally, it is demonstrated through simulation experiments that the ESO-based RMPC has good trajectory tracking performance and anti-disturbance capability. -
表 1 工况1下各控制器的跟踪误差RMS指标
Table 1. The tracking error of each controller in case 1
RMS ${x_e}$/m ${y_e}$/m ${z_e}$/m ${\psi _e}$/rad ${\theta _{e1}}$/rad ${\theta _{e2}}$/rad RMPCESO 0.001 1 0.002 2 0.000 2 0.008 1 0.004 8 0.009 1 NTSMC 0.001 2 0.010 9 0.001 3 0.060 6 0.033 9 0.015 9 PID 0.062 1 0.029 9 0.080 4 0.040 8 0.021 7 0.049 2 
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表 2 工况2下各控制器跟踪误差RMS指标
Table 2. The tracking error of each controller in case 2
RMS ${x_e}$/m ${y_e}$/m ${z_e}$/m ${\psi _e}$/rad ${\theta _{e1}}$/rad ${\theta _{e2}}$/rad RMPCESO 0.000 3 0.000 3 0.000 1 0.002 3 0.004 9 0.000 5 NTSMC 0.008 3 0.111 7 0.036 1 0.040 3 0.004 2 0.730 1 PID 0.030 2 0.145 2 0.044 8 0.011 4 0.013 3 0.073 2
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