Discrete time model predictive control for humanoid walking with step adjustment

TL;DR

This approach differs from existing MPC methods for walking pattern generation by not relying on a predefined foot-plan or a reference center of pressure trajectory, and generates stable walking and prevents fall against push disturbances.

Abstract

This paper presents a Discrete-Time Model Predictive Controller (MPC) for humanoid walking with online footstep adjustment. The proposed controller utilizes a hierarchical control approach. The high-level controller uses a low-dimensional Linear Inverted Pendulum Model (LIPM) to determine desired foot placement and Center of Mass (CoM) motion, to prevent falls while maintaining the desired velocity. A Task Space Controller (TSC) then tracks the desired motion obtained from the high-level controller, exploiting the whole-body dynamics of the humanoid. Our approach differs from existing MPC methods for walking pattern generation by not relying on a predefined foot-plan or a reference center of pressure (CoP) trajectory. The overall approach is tested in simulation on a torque-controlled Humanoid Robot. Results show that proposed control approach generates stable walking and prevents fall against push disturbances.

Figures (17)

• Figure 1: Humanoid robot used for simulation
• Figure 2: Humanoid Control Block Diagram
• Figure 3: Humanoid Walking Gait
• Figure 4: Foot Placement
• Figure 5: CoM x-position and velocity