基于改进蜣螂优化的自适应滑模神经网络控制
Adaptive Sliding Mode Neural Network Control Based on Improved Dung Beetle Optimization
高赫佳 1王祥 1荆哲 2孙长银3
作者信息
- 1. 安徽大学人工智能学院,合肥 230601
- 2. 北京理工大学自动化学院,北京 100081
- 3. 安徽大学人工智能学院,合肥 230601;东南大学自动化学院,南京 210096
- 折叠
摘要
针对移动机器人跟踪系统中滑模控制器存在的抖振和参数自适应性较低的问题以及不确定扰动的挑战,提出了一种基于改进蜣螂优化的自适应滑模神经网络控制方法.采用一种基于组合趋近律和自适应径向基函数(RBF)神经网络的滑模控制器解决系统的抖振和不确定扰动的问题,较好地解决了系统的抖振问题以及外界干扰对系统的影响,实现移动机器人对期望轨迹的精确跟踪;基于此控制器,引入改进的蜣螂优化算法对控制器参数进行优化,在增强控制器参数自适应性的同时提高了系统的稳定性.仿真结果表明:所提方法不仅可以使移动机器人对期望轨迹进行快速精确的跟踪,还能有效克服不确定干扰对系统造成的影响.研究结果可以应用于移动机器人的运动控制和优化等领域.
Abstract
In order to solve the problems of jitter and low parameter adaptability and the challenge of uncertain dis-turbance in the sliding mode controller in the mobile robot tracking system,an adaptive sliding mode neural network control method based on improved dung beetle optimization is proposed.A sliding mode controller based on combinatorial approach law and adaptive radial basis function(RBF)neural network is used to solve the problems of jitter and uncertain disturbance of the system,which solves the jitter problem of the system and the influence of external interference on the system,and re-alizes the accurate tracking of the desired trajectory of the mobile robot.Based on this controller,an improved dung beetle optimization algorithm is introduced to optimize the controller parameters,which enhances the adaptability of the controller parameters and improves the stability of the system.Based on the simulation results,the proposed method can not only ena-ble the mobile robot to track the expected trajectory quickly and accurately,but also effectively overcome the influence of uncertain interference on the system.The research results can be applied to the fields of motion control and optimization of mobile robots.
关键词
移动机器人/滑模控制/RBF神经网络/改进蜣螂优化算法/轨迹跟踪Key words
mobile robots/sliding mode control/radial basis function(RBF)neural network/improved dung bee-tle optimization algorithm/trajectory tracking引用本文复制引用
出版年
2024
NETL
NSTL
万方数据