Robust model predictive control of biped robots with adaptive on-line gait generation

Abstract

In this paper, an on-line gait control scheme is proposed for the biped robots for walking up and down the stairs. In the proposed strategy, the nonlinear model predictive control approach is used for the trajectory planning and as well as for the control of the robot. The motion of the robot is expressed in the form of a cost function and some constraints that are related to the stable walking of the robot. The main feature of this method is that it does not need any off-line trajectory planning and the walking gait is formulated such that the environmental and stability constraints of the robot are satisfied. This on-line trajectory planning gives the important ability to the robot to adjust its gait lengths. In this way, the robot is able to ascend and descend the stairs without knowing the height and depth of the stairs in advance. In the control algorithm, the Radial-Basis Function (RBF) neural network with on-line training method is used to model the behavior of the robot over the prediction horizon. The stability analysis of the closed-loop system is performed using the Lyapunov method as well as the Poincare map. The proposed method is applied to a 5-DOF biped robot in the sagittal plane. The simulation results show effectiveness of the proposed method.