# Nonlinear Model Predictive Control for Robust Bipedal Locomotion: Exploring Angular Momentum and CoM Height Changes

## TL;DR

This paper introduces a nonlinear model predictive control framework for bipedal walking that integrates step adjustment, CoM height variation, and angular momentum adaptation to enhance robustness against disturbances.

## Contribution

It presents a novel NMPC approach that combines multiple balance strategies within a unified control framework using a nonlinear inverted pendulum model.

## Key findings

- Robust walking patterns are generated using the proposed NMPC.
- The framework effectively exploits reactive stepping, body inclination, and CoM height changes.
- Simulation results demonstrate adaptability in various scenarios.

## Abstract

Human beings can utilize multiple balance strategies, e.g. step location adjustment and angular momentum adaptation, to maintain balance when walking under dynamic disturbances. In this work, we propose a novel Nonlinear Model Predictive Control (NMPC) framework for robust locomotion, with the capabilities of step location adjustment, Center of Mass (CoM) height variation, and angular momentum adaptation. These features are realized by constraining the Zero Moment Point within the support polygon. By using the nonlinear inverted pendulum plus flywheel model, the effects of upper-body rotation and vertical height motion are considered. As a result, the NMPC is formulated as a quadratically constrained quadratic program problem, which is solved fast by sequential quadratic programming. Using this unified framework, robust walking patterns that exploit reactive stepping, body inclination, and CoM height variation are generated based on the state estimation. The adaptability for bipedal walking in multiple scenarios has been demonstrated through simulation studies.