基于T-S模糊再励学习的稳定双足步态生成算法  

Stable Biped Gait Generation Algorithm Based on T-S Fuzzy Reinforcement Learning Method

作　　者：胡凌云[1] 孙增圻[1] 

机构地区：[1]清华大学计算机科学与技术系,北京100084

出　　处：《机器人》2004年第5期461-466,共6页Robot

基　　金：国家 973计划资助项目 (G2 0 0 2 31 2 2 0 5) ;国家自然科学基金资助项目 (60 1 741 8;60 350 0 8;90 2 0 50 0 8) .

摘　　要：提出了一种基于T S模糊再励学习的稳定双足步态生成算法 .将再励学习引入T S模糊神经网学习增益参数 ,从而采用较少的模糊规则充分逼近了由ZMP曲线到髋关节轨迹的非线性变化关系 ,并将连续空间的多变量变化转换为一维独立动作增益的并行搜索 .仿真结果和双足机器人Luna的实验数据都验证了算法的可行性 .A stable gait generation algorithm based on T-S type fuzzy learning net method is proposed in this paper. Reinforcement learning method is introduced into fuzzy network to learn the gain parameters. Few fuzzy rules are needed to formulate the nonlinear relation between the ZMP(zero moment point) curve and hip trajectory. The problem of multi-variables in continuous space is also simplified to search the independent action gains simultaneously. Simulation experiments on Luna biped robot prove its feasibility.

关 键 词：双足机器人 T-S模糊再励学习网络 稳定 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]