1,203 research outputs found
Feedback MPC for Torque-Controlled Legged Robots
The computational power of mobile robots is currently insufficient to achieve
torque level whole-body Model Predictive Control (MPC) at the update rates
required for complex dynamic systems such as legged robots. This problem is
commonly circumvented by using a fast tracking controller to compensate for
model errors between updates. In this work, we show that the feedback policy
from a Differential Dynamic Programming (DDP) based MPC algorithm is a viable
alternative to bridge the gap between the low MPC update rate and the actuation
command rate. We propose to augment the DDP approach with a relaxed barrier
function to address inequality constraints arising from the friction cone. A
frequency-dependent cost function is used to reduce the sensitivity to
high-frequency model errors and actuator bandwidth limits. We demonstrate that
our approach can find stable locomotion policies for the torque-controlled
quadruped, ANYmal, both in simulation and on hardware.Comment: Paper accepted to IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2019
Online learning with stability guarantees: A memory-based real-time model predictive controller
We propose and analyze a real-time model predictive control (MPC) scheme that
utilizes stored data to improve its performance by learning the value function
online with stability guarantees. For linear and nonlinear systems, a learning
method is presented that makes use of basic analytic properties of the cost
function and is proven to learn the MPC control law and the value function on
the limit set of the closed-loop state trajectory. The main idea is to generate
a smart warm start based on historical data that improves future data points
and thus future warm starts. We show that these warm starts are asymptotically
exact and converge to the solution of the MPC optimization problem. Thereby,
the suboptimality of the applied control input resulting from the real-time
requirements vanishes over time. Simulative examples show that existing
real-time MPC schemes can be improved by storing data and the proposed learning
scheme.Comment: This article is an extended version of the paper "Online learning
with stability guarantees: A memory-based warm starting for real-time MPC"
published in Automatica, Volume 122, 109247, 2020, including all proofs, an
application example, and a detailed description of the used algorith
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