Time-projection control to recover inter-sample disturbances, application to bipedal walking control
This addresses robust walking control for bipedal robots, offering an incremental improvement in disturbance recovery.
The paper tackles the problem of recovering from inter-sample disturbances in bipedal walking control by proposing a time-projection controller based on a 3LP model, which reacts immediately to pushes and provides superior performance compared to discrete LQR, covering most of the maximal viable set of states while being computationally faster than MPC.
We present a new walking controller based on 3LP, a 3D model of bipedal walking that is composed of three pendulums to simulate falling, swing and torso dynamics. Taking advantage of linear equations and closed-form solutions of 3LP, the proposed controller projects intermediate states of the biped back to the beginning of the phase for which a discrete LQR controller is designed. After the projection, a proper control policy is generated by this LQR controller and used at the intermediate time. The projection controller reacts to disturbances immediately and compared to the discrete LQR controller, it provides superior performance in recovering intermittent external pushes. Further analysis of closed-loop eigenvalues and disturbance rejection strength show strong stabilization properties for this architecture. An analysis of viable regions also show that the proposed controller covers most of the maximal viable set of states. It is computationally much faster than Model Predictive Controllers (MPC) and yet optimal over an infinite horizon.