Available at: https://digitalcommons.calpoly.edu/theses/3163
Date of Award
8-2025
Degree Name
MS in Mechanical Engineering
Department/Program
Mechanical Engineering
College
College of Engineering
Advisor
Siyuan Xing
Advisor Department
Mechanical Engineering
Advisor College
College of Engineering
Abstract
Quadrupedal robots offer a versatile locomotion option that can extend the operating space of a robot into uneven terrains. However, controlling these systems presents significant challenges due to nonlinearities introduced by various factors.
In this thesis, model-predictive control (MPC) is applied to an 8-DOF legged robot developed by Cal Poly’s Legged Robotics group. The MPC framework employs a lumped rigid-body model that treats the robot as a single rigid body with forces applied directly at the foot contact points. The controller is developed within the ROS2 environment, with integration of state estimation and gait-pattern generation, to provide maximum modularity and flexibility for various locomotion scenarios.
The efficacy of the controller is demonstrated in the simulation environment through generating various stable symmetric (and even asymmetric) gaits at speeds exceeding 1 m/s. Notably, the control system relies exclusively on proprioceptive sensing, operating without exteroceptive feedback from the surrounding environment. This research provides a foundation for implementation on real hardware.