Perceptive locomotion through nonlinear model-predictive control
Dynamic locomotion in rough terrain requires accurate foot placement, collision avoidance,
and planning of the underactuated dynamics of the system. Reliably optimizing for such …
and planning of the underactuated dynamics of the system. Reliably optimizing for such …
HPIPM: a high-performance quadratic programming framework for model predictive control
This paper introduces HPIPM, a high-performance framework for quadratic programming
(QP), designed to provide building blocks to efficiently and reliably solve model predictive …
(QP), designed to provide building blocks to efficiently and reliably solve model predictive …
Model predictive control of legged and humanoid robots: models and algorithms
Model predictive control (MPC) of legged and humanoid robotic systems has been an active
research topic in the past decade. While MPC for robotic systems has a long history, its …
research topic in the past decade. While MPC for robotic systems has a long history, its …
Koopman NMPC: Koopman-based learning and nonlinear model predictive control of control-affine systems
C Folkestad, JW Burdick - 2021 IEEE International Conference …, 2021 - ieeexplore.ieee.org
Koopman-based learning methods can potentially be practical and powerful tools for
dynamical robotic systems. However, common methods to construct Koopman …
dynamical robotic systems. However, common methods to construct Koopman …
A unified ship manoeuvring model with a nonlinear model predictive controller for path following in regular waves
In this paper, a nonlinear model predictive controller for path following of a surface vessel in
the presence of regular waves is studied. A model predictive controller is developed for a …
the presence of regular waves is studied. A model predictive controller is developed for a …
Deep reinforcement learning control for non-stationary building energy management
Developing an optimal supervisory control policy for building energy management is a
complex problem because the system exhibits non-stationary behaviors, and the target …
complex problem because the system exhibits non-stationary behaviors, and the target …
Koopnet: Joint learning of koopman bilinear models and function dictionaries with application to quadrotor trajectory tracking
C Folkestad, SX Wei, JW Burdick - … International Conference on …, 2022 - ieeexplore.ieee.org
Nonlinear dynamical effects are crucial to the operation of many agile robotic systems.
Koopman-based model learning methods can capture these nonlinear dynamical system …
Koopman-based model learning methods can capture these nonlinear dynamical system …
Industrial, large-scale model predictive control with structured neural networks
The design of neural networks (NNs) is presented for treating large, linear model predictive
control (MPC) applications that are out of reach with available quadratic programming (QP) …
control (MPC) applications that are out of reach with available quadratic programming (QP) …
Nonlinear model predictive control of robotic systems with control lyapunov functions
The theoretical unification of Nonlinear Model Predictive Control (NMPC) with Control
Lyapunov Functions (CLFs) provides a framework for achieving optimal control performance …
Lyapunov Functions (CLFs) provides a framework for achieving optimal control performance …
Continuous control set nonlinear model predictive control of reluctance synchronous machines
In this article, we describe the design and implementation of a current controller for a
reluctance synchronous machine (RSM) based on continuous control set nonlinear model …
reluctance synchronous machine (RSM) based on continuous control set nonlinear model …