Real-Time Model Predictive Control of Nonlinear Coupled Joints Using MPPI: Application to Humanoid Ankle Joints

Abstract

Modern robotic systems increasingly employ nonlinear coupled joints, which present significant challenges in control. Unlike traditional serial chain configurations, where simplicity was the primary concern, parallel mechanisms such as those found in humanoid ankle joints add another layer of complexity. In this work, we propose an actuation controller for nonlinear coupled joints based on Model Predictive Path Integral (MPPI) control framework: a sampling-based model predictive control framework that incorporates nonlinearity and coupling effect simultaneously. Highly nonlinear Actuator-Joint mapping, expressed through lightweight neural network, enables intuitive controller design by exposing the actuator space control to the joint space command. Also, our method enables posing joint limit constraints, enabling safe operation on a real-robot platform. To experimentally validate our method, joint position control of a humanoid ankle joint with 2-DOF has been conducted, where accurate, real-time control and constraint-respecting behavior has been demonstrated.