摘要
为了提高复杂的食品原料仓库中全向搬运机器人的跟踪能力,提出一种改进路径跟踪控制方法。首先依据机器人状态空间模型,以跟踪误差信息和电机电压为输入、输出设计控制器,并且设计论域放缩因子优化模糊控制器,通过试验测定初步确定了参数k p、k i、k d的取值范围。然后通过加入反向学习策略初始化麻雀种群,设计权重因子控制靠近发现者的跟随者比例防止早熟。结果显示:改进麻雀算法比原算法在测试函数下寻优精度和收敛速度有所提升,提出的控制方法跟踪误差快速降零、跟踪过程较为平顺,到达期望路径速度较快。该方法提高了全向搬运机器人的工作状况。
Based on state of robot,an improved tracking control method which takes the tracking error as an input and the drive voltage of the motor as an output is proposed.A theoretical domain deflation factor is added to automatically adjust the fuzzy controller,and the range of parameters was determined preliminarily by experimental measurement.Then the sparrow population was initialized by adding a reverse learning strategy,and a weight factor was designed to control the proportion of followers close to the finder to prevent precocity.The results show that the improved sparrow algorithm performs better than the original algorithm in terms of seeking accuracy and convergence speed under the test function.The method in this paper has a rapid reduction of the tracking error to zero,a smoother tracking process,and a faster arrival speed to the desired path.The method improves the working condition of the omnidirectional robot.
作者
张品
李长勇
ZHANG Pin;LI Chang-yong(School of Mechanical Engineering,Xinjiang University,Urumqi,Xinjiang 830049,China)
出处
《食品与机械》
北大核心
2021年第6期114-119,190,共7页
Food and Machinery
基金
新疆维吾尔自治区自然科学基金(编号:2021D01C052)。
