通过分析生物节律运动机理,提出基于中枢模式发生器模型的步态控制方法。利用改进的Van der pol方程构造非线性振子作为单个CPG的数学模型。采用KB法得到振荡神经元一阶近似周期解,在此基础上设计具有线性反馈项的环状CPG网络模型,并给...通过分析生物节律运动机理,提出基于中枢模式发生器模型的步态控制方法。利用改进的Van der pol方程构造非线性振子作为单个CPG的数学模型。采用KB法得到振荡神经元一阶近似周期解,在此基础上设计具有线性反馈项的环状CPG网络模型,并给出了控制机器人步态协调运动方法。仿真结果验证了基于CPG模型的控制方法可以有效生成节律运动的常规步态并实现步态切换。展开更多
This paper develops a fast filtering algorithm based on vibration systems theory and neural information exchange approach. The characters, including the derivation process and parameter analysis, are discussed and the...This paper develops a fast filtering algorithm based on vibration systems theory and neural information exchange approach. The characters, including the derivation process and parameter analysis, are discussed and the feasibility and the effectiveness are testified by the filtering performance compared with various filtering methods, such as the fast wavelet transform algorithm, the particle filtering method and our previously developed single degree of freedom vibration system filtering algorithm, according to simulation and practical approaches. Meanwhile, the comparisons indicate that a significant advantage of the proposed fast filtering algorithm is its extremely fast filtering speed with good filtering perfi^rmance. Further, the developed fast filtering algorithm is applied to the navigation and positioning system of the micro motion robot, which is a high real-time requirement for the signals preprocessing. Then, the preprocessing data is used to estimate the heading angle error and the attitude angle error of the micro motion robot. The estimation experiments illustrate the high practicality of the proposed fast filtering algorithm.展开更多
文摘通过分析生物节律运动机理,提出基于中枢模式发生器模型的步态控制方法。利用改进的Van der pol方程构造非线性振子作为单个CPG的数学模型。采用KB法得到振荡神经元一阶近似周期解,在此基础上设计具有线性反馈项的环状CPG网络模型,并给出了控制机器人步态协调运动方法。仿真结果验证了基于CPG模型的控制方法可以有效生成节律运动的常规步态并实现步态切换。
基金Project supported by the National Natural Science Foundation of China(Grant Nos.60901074,51075092,61005076,and 61175107)the National High Technology Research and Development Program of China(Grant No.2007AA042105)the Natural Science Foundation of Heilongjiang Province,China(Grant No.E200903)
文摘This paper develops a fast filtering algorithm based on vibration systems theory and neural information exchange approach. The characters, including the derivation process and parameter analysis, are discussed and the feasibility and the effectiveness are testified by the filtering performance compared with various filtering methods, such as the fast wavelet transform algorithm, the particle filtering method and our previously developed single degree of freedom vibration system filtering algorithm, according to simulation and practical approaches. Meanwhile, the comparisons indicate that a significant advantage of the proposed fast filtering algorithm is its extremely fast filtering speed with good filtering perfi^rmance. Further, the developed fast filtering algorithm is applied to the navigation and positioning system of the micro motion robot, which is a high real-time requirement for the signals preprocessing. Then, the preprocessing data is used to estimate the heading angle error and the attitude angle error of the micro motion robot. The estimation experiments illustrate the high practicality of the proposed fast filtering algorithm.
