在两足动物走期间,检测并且由反馈控制调整机器人姿势在从一些不可避免的不明确的因素产生的多来源随机骚乱中维持它的正常状态是批评的。激进的基础函数( RBF )一个五连接的两足动物机器人的神经网络模型被建立,并且二某些骚乱和随机不明确的骚乱然后在网络模型与最佳的转矩被混合由几个评估索引和特定的 Poincar 学习两足动物机器人的性能?????禮?(
During bipedal walking,it is critical to detect and adjust the robot postures by feedback control to maintain its normal state amidst multi-source random disturbances arising from some unavoidable uncertain factors.The radical basis function(RBF)neural network model of a five-link biped robot is established,and two certain disturbances and a randomly uncertain disturbance are then mixed with the optimal torques in the network model to study the performance of the biped robot by several evaluation indices and a specific Poincar′e map.In contrast with the simulations,the response varies as desired under optimal inputting while the output is fluctuating in the situation of disturbance driving.Simulation results from noise inputting also show that the dynamics of the robot is less sensitive to the disturbance of knee joint input of the swing leg than those of the other three joints,the response errors of the biped will be increasing with higher disturbance levels,and especially there are larger output fluctuations in the knee and hip joints of the swing leg.