Standing Posture Control of Bipedal Robots with Adaptive Compliance Under Unknown Payload Variations and External Disturbances

Abstract

When executing tasks, robots are required to demonstrate compliance to unexpected external disturbances or human–robot interactions, and return to the demanded posture when the disturbances or contacts are removed. Traditional Virtual Model Control (VMC) requires precise gravitational compensation to accurately control the posture of a robot. Hence, load variations or other uncertain unmodeled factors in the robot will result in offsets to its balance posture, which makes the robot deviate from the demanded posture when it is in a static state. To reject this offset without sacrificing the compliance of the robot, an adaptive controller is proposed in this paper to implement adaptive compliance on the robot, which makes the robot robust to the variations in gravitational loads in the double leg support phase. The adaptive controller is a combination of the VMC controller and an online gravitational loads estimator, in which the estimator is derived in the double leg support phase to estimate the values of these parameters and obtains an online updating law based on a Kalman optimal estimator. Then, a Lyapunov function is designed to modify and combine the controller and the online gravitational loads estimator. The experiments are conducted on a 4 DoF bipedal robot in the sagittal plane to validate the effectiveness of the controller and show that, by estimating the gravitational loads of the robot, the effects of load variations on balance posture are rejected without sacrificing compliance.