Dynamical analysis and performance evaluation of a biped robot under multi-source random disturbances

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, 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.