Exploring Speed-forward-looking Particle Swarm-taboo Search Optimization Algorithm by Introducing Path Angle
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摘要: 对于数控系统中路径为连续微线段的情况, 本文提出一种采用路径夹角进行优化的多轨迹速度前瞻算法。首先基于前后路径的约束关系初算衔接速度; 其次, 利用路径夹角对衔接速度和最大加加速度进行矢量函数映射; 最后, 将所得的加减速参数带入S加减速模型, 得到非对称S型加减速数据。并且采用粒子群-禁忌搜索混合算法对路径夹角的调整系数进行优化, 通过熵权法计算适应度函数合成指标的权重, 得到全局最优解。结果表明, 相较于传统S型加减速, 本文采用的算法显著提高了加工效率, 具有较高的柔性, 极大的提升了加工精度。
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关键词:
- 粒子群-禁忌搜索混合算法 /
- 熵权法 /
- S型加减速 /
- 前瞻规划 /
- 路径夹角
Abstract: This paper proposes a multi-track and speed-forward-looking algorithm that uses path angles to optimize the path in a CNC system that has continuous micro-line segments. Firstly, it calculates the connection speed based on the constraint relationship between front and back paths. Secondly, it uses the path angle to map the connection speed and the maximum jerk vector function. Finally, it introduces the obtained acceleration and deceleration parameters into the S-shaped acceleration and deceleration model to obtain the asymmetric S-shaped acceleration and deceleration data. The particle swarm-taboo search hybrid algorithm is used to optimize the adjustment coefficient of the introduced path angle. The weight of the fitness function synthesis index is calculated with the entropy weight method, and the global optimal solution is obtained. The results show that compared with the traditional S-type acceleration and deceleration model, the algorithm proposed in this paper significantly improves the processing efficiency, has higher flexibility and greatly improves the processing accuracy. -
表 1 3种算法性能比较
指标 普通S型 普通S型+前瞻+夹角 优化S型+前瞻+夹角 速度突变指数 数值/(mm·s-2) 5.091 4.117 3.385 提升比例/% 33.5 17.8 - 运动时间 数值/s 12.171 7.720 6.671 提升比例/% 45.2 13.6 - 路径误差 数值/mm 4.25×10-10 2.05×10-11 4.79×10-12 提升比例/% 98.9 76.6 - 
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