智能水滴算法结合神经网络的路径跟踪研究

Research on Path Tracking of Intelligent Water Droplet Algorithms Combined with Neural Network

路径跟踪的精确度问题是机器人研究的最后重要课题之一,文章基于小脑神经网络模型与PID复合控制器的处理路径跟踪问题。鉴于该复合控制器学习算法的不足,其寻优过程中易陷入局部极值以及算法效率等问题,论文采用智能水滴算法来优化学习机制,该算法多峰寻优效果好,算法简洁;融入智能水滴算法的CMAC-PID复合控制器学习性能明显提升,并且针对线性路径跟踪和圆弧路径跟踪效果表明改进后算法具有很好的鲁棒性。最后对跟踪算法运用实验平台进行对比验证算法的可行性,对比改进后的算法与原始算法的效果,结果表明改进后的算法跟踪效果明显提高,进一步说明该算法具有较好的应用效果。

The accuracy of path tracking is one of the last important topics in robot research.The article deals with path tracking based on Cerebellar Model Articulation Controller and PID compound controller.In view of the shortcomings of the learning algorithm of the composite controller,the local extremum and the efficiency of the algorithm are easily trapped in the process of optimization.In the article,the intelligent water drop algorithm is used to optimize the learning mechanism.The algorithm is good at multi-peak optimization effect and concise algorithm.The learning performance of CMAC-PID composite controller incorporating the intelligent water drop algorithm is obviously improved,and linear path tracking and arc path tracking are tested.The results show that the improved algorithm has good robustness.Finally,the feasibility of the tracking algorithm is verified by comparing the improved algorithm with the original algorithm.The results show that the tracking effect of the improved algorithm is significantly improved,which further proves that the algorithm has better application effect.