Robust walking based on MPC with viability-based feasibility guarantees

10/09/2020
by   Mohammad Hasan Yeganegi, et al.
0

Model predictive control (MPC) has shown great success for controlling complex systems such as legged robots. However, when closing the loop, the performance and feasibility of the finite horizon optimal control problem solved at each control cycle is not guaranteed anymore. This is due to model discrepancies, the effect of low-level controllers, uncertainties and sensor noise. To address these issues, we propose a modified version of a standard MPC approach used in legged locomotion with viability (weak forward invariance) guarantees that ensures the feasibility of the optimal control problem. Moreover, we use past experimental data to find the best cost weights, which measure a combination of performance, constraint satisfaction robustness, or stability (invariance). These interpretable costs measure the trade off between robustness and performance. For this purpose, we use Bayesian optimization (BO) to systematically design experiments that help efficiently collect data to learn a cost function leading to robust performance. Our simulation results with different realistic disturbances (i.e. external pushes, unmodeled actuator dynamics and computational delay) show the effectiveness of our approach to create robust controllers for humanoid robots.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 3

10/01/2021

RLO-MPC: Robust Learning-Based Output Feedback MPC for Improving the Performance of Uncertain Systems in Iterative Tasks

In this work we address the problem of performing a repetitive task when...
05/15/2020

Stochastic and Robust MPC for Bipedal Locomotion: A Comparative Study on Robustness and Performance

Linear Model Predictive Control (MPC) has been successfully used for gen...
02/02/2019

Transparent Concurrency Control: Decoupling Concurrency Control from DBMS

For performance reasons, conventional DBMSes adopt monolithic architectu...
07/08/2021

Full-Body Torque-Level Non-linear Model Predictive Control for Aerial Manipulation

Non-linear model predictive control (nMPC) is a powerful approach to con...
10/27/2017

Declarative vs Rule-based Control for Flocking Dynamics

The popularity of rule-based flocking models, such as Reynolds' classic ...
02/27/2019

Learning a Family of Optimal State Feedback Controllers

Solving optimal control problems is well known to be very computationall...
06/09/2019

Trajectory Optimization for Robust Humanoid Locomotion with Sample-Efficient Learning

Trajectory optimization (TO) is one of the most powerful tools for gener...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.