Optimal Control for Quadruped Locomotion using LTV MPC

TL;DR

This paper introduces a linear time-varying model predictive control approach for quadruped robots, enabling robust gait execution and trajectory tracking at high speeds using a simplified SRB model.

Contribution

It proposes a novel LTV MPC framework for quadruped locomotion based on SRB dynamics, demonstrating real-time implementation and high-speed gait capabilities.

Findings

Successfully executes trot and crawl gaits

Achieves up to 1 m/s top speed

Maintains trajectory tracking under disturbances

Abstract

This paper presents a state-of-the-art optimal controller for quadruped locomotion. The robot dynamics is represented using a single rigid body (SRB) model. A linear time-varying model predictive controller (LTV MPC) is proposed by using linearization schemes. Simulation results show that the LTV MPC can execute various gaits, such as trot and crawl, and is capable of tracking desired reference trajectories even under unknown external disturbances. The LTV MPC is implemented as a quadratic program using qpOASES through the CasADi interface at 50 Hz. The proposed MPC can reach up to 1 m/s top speed with an acceleration of 0.5 m/s2 executing a trot gait. The implementation is available at https:// github.com/AndrewZheng-1011/Quad_ConvexMPC