Results 1 to 10 of about 565 (144)

Koopman NMPC: Koopman-based Learning and Nonlinear Model Predictive Control of Control-affine Systems [PDF]

2021 IEEE International Conference on Robotics and Automation (ICRA), 2021
Koopman-based learning methods can potentially be practical and powerful tools for dynamical robotic systems. However, common methods to construct Koopman representations seek to learn lifted linear models that cannot capture nonlinear actuation effects inherent in many robotic systems.
Folkestad, Carl, Burdick, Joel W.
openaire   +5 more sources

HCDRNN-NMPC: A New Approach to Design Nonlinear Model Predictive Control (NMPC) Based on the Hyper Chaotic Diagonal Recurrent Neural Network (HCDRNN)

Complexity, 2022
In industrial applications, Stewart platform control is especially important. Because of the Stewart platform’s inherent delays and high nonlinear behavior, a novel nonlinear model predictive controller (NMPC) and new chaotic neural network model (CNNM ...
Samira Johari, Mahdi Yaghoobi, Hamid R. Kobravi   +2 more
doaj   +2 more sources

LVD-NMPC: A learning-based vision dynamics approach to nonlinear model predictive control for autonomous vehicles [PDF]

International Journal of Advanced Robotic Systems, 2021
In this article, we introduce a learning-based vision dynamics approach to nonlinear model predictive control (NMPC) for autonomous vehicles, coined learning-based vision dynamics (LVD) NMPC.
Sorin Grigorescu, Cosmin Ginerica, Mihai Zaha, Gigel Macesanu, Bogdan Trasnea   +4 more
doaj   +3 more sources

Nonlinear model predictive control (NMPC) of the solvent-based post-combustion CO2 capture process [PDF]

Energy, 2020
The flexible operation capability of solvent-based post-combustion capture (PCC) process is vital to efficiently meet the load variation requirement in the integrated upstream power plant. This can be achieved through the deployment of an appropriate control strategy.
Akinola, Toluleke E., Oko, Eni, Wu, Xiao, Ma, Keming, Wang, Meihong   +4 more
openaire   +5 more sources

Learning Nonlinear Dynamics of Flexible Structures for Predictive Control Using Gaussian Process NARX Models [PDF]

Biomimetics
Biological systems regulate motion and suppress unwanted vibrations through learning, adaptation, and predictive control under uncertainty. Inspired by these principles, Bayesian system identification has emerged as a powerful framework for modeling and ...
Nasser Ayidh Alqahtani
doaj   +2 more sources

Intelligent Multi-Objective Nonlinear Model Predictive Control (iMO-NMPC): Towards the ‘on-line’ optimization of highly complex control problems

Expert Systems with Applications, 2012
The benefits of using the Nonlinear Model Predictive Control (NMPC) for the response optimization of highly complex controlled plants are well known. Nevertheless the complexity and associated high computational cost make its implementation and reliability the focus of the discussion.
Valera, Juan José, Gómez, Vicente, Irigoyen, Eloy, Artaza, Fernando, Larrea, Mikel   +4 more
openaire   +4 more sources

Autonomous berthing path tracking of a 4-DOF ship under nonlinear model predictive control [PDF]

Scientific Reports
To address the path tracking and control difficulties faced by unmanned surface ship in severe maritime environments, this research introduces a nonlinear model predictive control (NMPC) based approach to attain intelligent and accurate berthing.
Chunyu Song, Xiaomin Guo, Jianghua Sui   +2 more
doaj   +2 more sources

PSO-NMPC control strategy based path tracking control of mining LHD (scraper) [PDF]

Scientific Reports
The automation of underground articulated vehicles is a critical step in advancing digital and smart mining. Current nonlinear model predictive control (NMPC) controllers face challenges such as delays in turning on large curvature paths and correction ...
Ya Liu, Ping-an Peng, Li-guan Wang, Jia-xi Wu, Ming-yu Lei, Chao-wei Zhang, Ru Lei   +6 more
doaj   +2 more sources

Probably Approximately Correct Nonlinear Model Predictive Control (PAC-NMPC)

IEEE Robotics and Automation Letters, 2023
2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component ...
Adam Polevoy, Marin Kobilarov, Joseph L. Moore   +2 more
openaire   +2 more sources

Nonlinear Model Predictive Control with Terminal Cost for Autonomous Vehicles Trajectory Follow

Applied Sciences, 2022
This paper presents a nonlinear model predictive control with terminal cost (NMPC–WTC) algorithm and its open/closed-loop system analysis and simulation validation for accurate and stable path tracking of autonomous vehicles.
Jinrui Nan, Xucheng Ye, Wanke Cao
doaj   +1 more source