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Found 21 papers, 11 papers with code
Progressive Smoothing for Motion Planning in Real-Time NMPC
Nonlinear model predictive control (NMPC) is a popular strategy for solving motion planning problems, including obstacle avoidance constraints, in autonomous driving applications.
Collision-free Motion Planning for Mobile Robots by Zero-order Robust Optimization-based MPC
This paper presents an implementation of robust model predictive control (MPC) for collision-free reference trajectory tracking for mobile robots.
Imitation Learning from Nonlinear MPC via the Exact Q-Loss and its Gauss-Newton Approximation
This work presents a novel loss function for learning nonlinear Model Predictive Control policies via Imitation Learning.
Frenet-Cartesian Model Representations for Automotive Obstacle Avoidance within Nonlinear MPC
We specify costs and collision avoidance constraints in the more advantageous coordinate frame, derive appropriate formulations and compare different obstacle constraints.
Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots
The development of fast and safe approximate NMPC holds the potential to accelerate the adoption of flexible robots in industry.
Vertical Airborne Wind Energy Farms with High Power Density per Ground Area based on Multi-Aircraft Systems
This paper proposes and simulates vertical airborne wind energy (AWE) farms based on multi-aircraft systems with high power density (PD) per ground area.
Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control
Model-based control requires an accurate model of the system dynamics for precisely and safely controlling the robot in complex and dynamic environments.
An Inverse Optimal Control Approach for Trajectory Prediction of Autonomous Race Cars
The algorithm therefore learns to predict the observed vehicle trajectory in a least-squares relation to measurement data and to the presumed structure of the predicting NLP.
Convergence Analysis of Homotopy-SGD for non-convex optimization
In this work, we present a first-order stochastic algorithm based on a combination of homotopy methods and SGD, called Homotopy-Stochastic Gradient Descent (H-SGD), which finds interesting connections with some proposed heuristics in the literature, e. g. optimization by Gaussian continuation, training by diffusion, mollifying networks.
An Efficient Real-Time NMPC for Quadrotor Position Control under Communication Time-Delay
The advances in computer processor technology have enabled the application of nonlinear model predictive control (NMPC) to agile systems, such as quadrotors.
Robotics Systems and Control Systems and Control Optimization and Control
Kernel Distributionally Robust Optimization
We prove a theorem that generalizes the classical duality in the mathematical problem of moments.
On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs
This enables researchers to further study and improve this promising optimization technique and hopefully reconsider stochastic second-order methods as competitive optimization techniques for training DNNs; we also hope that the promise of SGN may lead to forward automatic differentiation being added to Tensorflow or Pytorch.
Transferring Optimality Across Data Distributions via Homotopy Methods
Homotopy methods, also known as continuation methods, are a powerful mathematical tool to efficiently solve various problems in numerical analysis, including complex non-convex optimization problems where no or only little prior knowledge regarding the localization of the solutions is available.
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem
In order to anticipate rare and impactful events, we propose to quantify the worst-case risk under distributional ambiguity using a recent development in kernel methods -- the kernel mean embedding.
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 control problems.
Optimization and Control Systems and Control Systems and Control
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control
We apply kernel mean embedding methods to sample-based stochastic optimization and control.
A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming
This work presents the concept of kernel mean embedding and kernel probabilistic programming in the context of stochastic systems.
acados: a modular open-source framework for fast embedded optimal control
The acados software package is a collection of solvers for fast embedded optimization, intended for fast embedded applications.
Optimization and Control
The BLAS API of BLASFEO: optimizing performance for small matrices
This BLAS API has lower performance than the BLASFEO API, but it nonetheless outperforms optimized BLAS and especially LAPACK libraries for matrices fitting in cache.
Mathematical Software
A Family of Iterative Gauss-Newton Shooting Methods for Nonlinear Optimal Control
This paper introduces a family of iterative algorithms for unconstrained nonlinear optimal control.
Systems and Control Robotics Optimization and Control
BLASFEO: basic linear algebra subroutines for embedded optimization
BLASFEO is a dense linear algebra library providing high-performance implementations of BLAS- and LAPACK-like routines for use in embedded optimization.
Mathematical Software
