| 彭达,赖惠鸽,余坼操,熊垒垒,杨明,毛坤.Scara机器人关节摩擦模型参数辨识及补偿[J].电子测量与仪器学报,2026,40(2):197-208 |
| Scara机器人关节摩擦模型参数辨识及补偿 |
| Parameter identification and compensation of joint friction model of Scara robot |
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| DOI： |
| 中文关键词: 改进人工萤火虫算法 参数辨识 摩擦模型 模糊PID Scara机器人 |
| 英文关键词:improved glowworm swarm optimization parameter identification friction model fuzzy PID Scara robot |
| 基金项目:国家自然科学基金（51765056）项目资助 |
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| Author | Institution |
| Peng Da | School of Mechanical Engineering, Ningxia University, Yinchuan 750021, China |
| Lai Huige | School of Mechanical Engineering, Ningxia University, Yinchuan 750021, China |
| Yu Checao | School of Mechanical Engineering, Ningxia University, Yinchuan 750021, China |
| Xiong Leilei | School of Mechanical Engineering, Ningxia University, Yinchuan 750021, China |
| Yang Ming | School of Mechanical Engineering, Ningxia University, Yinchuan 750021, China |
| Mao Kun | School of Mechanical Engineering, Ningxia University, Yinchuan 750021, China |
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| 摘要点击次数: 376 |
| 全文下载次数: 226 |
| 中文摘要: |
| 为解决Scara机器人关节摩擦导致定位精度下降的问题，提出了一种改进的人工萤火虫算法来识别摩擦模型参数，在传统算法的基础上进行了两方面优化，结合Levy飞行策略和惯性因子，利用非高斯随机游走和自适应惯性权重对陷入局部最优的萤火虫进行随机初始化，提升算法的全局搜索能力；引入模拟退火算法对潜在最优解进行局部退火操作，提高了算法的局部寻优能力。通过测试函数性能分析以及参数辨识实验，表明改进的人工萤火虫算法相较于其他优化算法具有更好的寻优性能。最后，为了进一步验证通过算法辨识所得摩擦模型的有效性，设计了基于摩擦补偿的模糊PID控制器进行机器人轨迹跟踪控制实验。实验结果表明，识别的摩擦模型精度较高，并且提出的控制方法相较于仅用模糊PID控制方法能够有效抑制关节摩擦对Scara机器人轨迹跟踪控制的不利影响，机器人两关节的位置跟踪误差分别减少了76.1%和81.9%，进一步提高了机器人的定位精度。 |
| 英文摘要: |
| To address the issue of reduced positioning accuracy caused by joint friction in Scara robots, an improved glowworm swarm optimization is proposed to identify the parameters of the friction model. Two optimizations are made on the basis of the traditional algorithm: by combining the Levy flight strategy and inertia factor, non-Gaussian random walks and adaptive inertia weights are utilized to randomly initialize fireflies trapped in local optima, enhancing the algorithm’s global search capability; the simulated annealing algorithm is introduced to perform local annealing operations on potential optimal solutions, improving the algorithm’s local optimization ability. Through performance analysis of test functions and parameter identification experiments, the results show that the improved artificial firefly algorithm has better optimization performance compared to other optimization algorithms. Finally, to further verify the effectiveness of the friction model identified through the algorithm, a fuzzy PID controller based on friction compensation is designed for the robot trajectory tracking control experiment. The experimental results indicate that the identified friction model has high accuracy, and the proposed control method can effectively suppress the adverse effects of joint friction on the trajectory tracking control of Scara robots compared to using only the fuzzy PID control method. The position tracking errors of the two joints of the robot are reduced by 76.1% and 81.9% respectively, further improving the positioning accuracy of the robot. |
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