| 姜金叶,冯浩,常潇丹,殷晨波,曹东辉,李春彪,谢家学.改进LuGre模型的挖掘机器人摩擦补偿控制[J].电子测量与仪器学报,2024,38(2):139-147 |
| 改进LuGre模型的挖掘机器人摩擦补偿控制 |
| Improved LuGre model for friction compensation control of robotic excavators |
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| DOI： |
| 中文关键词: 电液伺服系统 摩擦辨识 粒子群优化算法 摩擦补偿 LuGre模型 |
| 英文关键词:electro-hydraulic servo system friction identification particle swarm optimization algorithm friction compensation LuGre model |
| 基金项目:国家自然科学基金（52105064）、江苏省自然科学基金（BK20221342）、国家重点研发计划（2021YFB2011904）、江苏省研究生实践创新计划项目（SJCX23_0401）资助 |
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| Author | Institution |
| Jiang Jinye | School of Computer Science, Nanjing University of Information Science & Technology, Nanjing 210044, China |
| Feng Hao | 2.School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China;
3.Xiongyu Heavy Industry Group Co., Ltd., Wuxi 214100, China |
| Chang Xiaodan | School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China |
| Yin Chenbo | United Institute of Excavator Key Technology,
Nanjing Tech University, Nanjing 211816, China |
| Cao Donghui | SANY Group Co., Ltd., Kunshan 215300, China |
| Li Chunbiao | School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China |
| Xie Jiaxue | Xiongyu Heavy Industry Group Co., Ltd., Wuxi 214100, China |
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| 中文摘要: |
| 非线性摩擦会降低挖掘机器人电液伺服系统的动静态性能，引起轨迹爬行、平峰和稳态误差等现象。经典LuGre摩擦模型仅与速度有关，内部鬃毛状态变量无法准确测量，无法全面描述复杂的挖掘机器人电液伺服系统摩擦特性。本文综合考虑电液伺服系统位置、速度和方向等信息，设计了一种改进的LuGre摩擦模型，同时引入速度阈值解决了弹性鬃毛平均变形状态观测器不稳定问题。其次，为了解决传统优化算法陷入局部最优解、收敛速度慢等问题，通过引入惯性权重、异步变化和精英突变操作改进基本粒子群优化算法，以精准快速辨识出改进LuGre摩擦模型中的6个未知参数。最后，结合辨识出的摩擦模型，基于结构不变性原理设计前馈摩擦补偿控制器，并在23吨挖掘机器人进行了正弦和三角波不同工况下的轨迹跟踪实验。实验结果表明，传统的比例积分微分控制器跟踪误差最大，三角轨迹最大跟踪误差达到了29.68 mm，基于改进LuGre模型设计的前馈摩擦补偿控制器仅为9.70 mm，误差减小了67.31%，基于改进LuGre模型设计的前馈摩擦补偿控制器可以有效提升挖掘机器人的轨迹跟踪精度。 |
| 英文摘要: |
| Nonlinear friction negatively impacts the dynamic and static performance of hydraulic servo systems in robotic excavators, leading to issues such as trajectory creep, flat peaks, and steady-state errors. The traditional LuGre friction model, which relies solely on velocity and internal bristle state variables that cannot be accurately measured, fails to comprehensively describe the complex friction characteristics of excavator hydraulic servo systems. Considering the position, velocity, and direction of the excavator hydraulic servo system, we propose an enhanced LuGre friction model and introduce a velocity threshold to address the instability issue of the elastic bristle average deformation state observer in the friction model. Secondly, to address the issues of traditional optimization algorithms getting stuck in local optimal solutions and having slow convergence speeds, the basic particle swarm optimization algorithm has been enhanced. This enhancement involves the introduction of inertia weight, asynchronous change, and elite mutation operations to accurately and rapidly identify the six unknown parameters in the improved LuGre friction model. Subsequently, using the identified friction model, a friction compensation controller based on the principle of structural invariance is designed. Three different operating condition trajectory tracking experiments were conducted on a 23-ton excavator. The conventional proportional-integral-differential controller exhibits the highest tracking error, with the maximum tracking error for the triangular trajectory reaching 29.68 mm. In contrast, the feedforward friction compensation controller, which is based on the enhanced LuGre model, achieves a significantly lower error of 9.70 mm, representing a 67.31% reduction in error. The experimental results demonstrate that the proposed friction compensation controller significantly enhances the trajectory tracking accuracy of the excavator. |
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