摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-研究人员详细介绍了机器人学的新数据。根据NewsRx编辑在摩洛哥马拉喀什的新闻报道，研究国家D，"近年来，移动机器人的轨迹跟踪一直是特定文献中最主要解决的问题之一，因为移动机器人必须有跟踪轨迹的能力。本文提出了一种基于超扭曲滑模的IEHO-STSM控制器，用于移动机器人的路径跟踪。基于EHO(Elephant Herding Optimization,大象放牧优化)元启发式算法,改进了EHO的收敛速度、探索能力和开发能力,然后基于移动机器人动力学模型,设计了超扭曲滑模(STSM)控制器,将机器人引导到期望的轨迹。将改进的D IEHO算法应用于超扭滑模(STSM)控制器的参数整定,并将其与EHO、PSO(微粒群优化)和GWO(G Rey Wolf Optimizer)算法进行了比较,分析了该算法在超扭滑模(STSM)控制器参数整定中的应用。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Researchers detail new data in Robotics. Accordin g to news reporting out of Marrakech, Morocco, by NewsRx editors, research state d, “In recent years, trajectory tracking of a mobile robot has been one of the m ost addressed problems in the specilized literature, as a mobile robot must have the ability to follow a trajectory, while also compensating various external an d internal disturbances. This paper proposes an IEHO-STSM controller based on th e super-twisting sliding mode for the path tracking of a mobile robot.”Our news journalists obtained a quote from the research from Cadi Ayyad Universi ty, “First, a new improved IEHO algorithm has been developed and introduced, bas ed on the EHO (Elephant Herding Optimization) metaheuristic algorithm. The devel oped algorithm consisted in improving the performance of the basic EHO such as c onvergence speed, exploration and exploitation capabilities. Then, based on a dy namic model of the mobile robot, a super-twisting sliding mode (STSM) controller was designed to guide the robot to the desired trajectory. Finally, the improve d IEHO algorithm was applied for adjusting the parameters of the super-twisting sliding mode (STSM) controller. The analysis of the proposed IEHO algorithm has been done by comparing it with EHO, PSO (Particle Swarm Optimization) and GWO (G rey Wolf Optimizer) algorithms, by employing it in tuning the STSM.”
NETL
NSTL