Results 61 to 70 of about 7,719 (160)

Neural Network Predictive Controller Design

Chemical Engineering Transactions, 2017
This paper aims at showing the application of neural network predictive control (NNPC) to counter-current heat exchangers (HEs) in series for water savings. The controlled process unit is composed of five counter-current shell-and-tube heat exchangers in
A. Vasickaninova, M. Bakosova, J. Oravec, A. Meszaros   +3 more
doaj   +1 more source

High-Resolution Extended Finite Control Set Model Predictive Control for Three-Phase Two-Level Grid-Connected Converters

IEEE Access
Finite control set model predictive control is an advanced control strategy for power converters. However, when applied to the control of grid-connected converters, it still faces several practical problems such as high current distortion, variable ...
Yonglei Zhang, Wei Jiang, Xibo Yuan, Sergio Busquets-Monge, Yan Li, Ruijie Zhu   +5 more
doaj   +1 more source

A Finite-Time Extended State Observer with Prediction Error Compensation for PMSM Control

Computation
This paper proposes a finite-time extended state observer (FTESO) integrated with model predictive control (MPC) for high-performance control of permanent magnet synchronous motors (PMSMs).
Lihua Gao, Guangming Zhang, Xiaodong Lv, Yan Wang, Zhihan Shi   +4 more
doaj   +1 more source

Continuous-Control-Set Model Predictive Control Strategy for MMC-UPQC Under Non-Ideal Conditions

Energies
In the MMC-based unified power quality conditioner (MMC-UPQC), the computational burden of finite-control-set model predictive control (FCS-MPC) increases rapidly with the number of MMC submodules.
Lianghua Chen, Jianping Zhou, Jiayu Zhai, Lisheng Yang, Xudong Qian, Zhiyong Tao   +5 more
doaj   +1 more source

Model predictive control (MPC) with integral action; Reducing the control horizon and model free MPC

, 2015
Model Predictive Control (MPC) is the most widely used strategy in process industries due to remarkable features. It has the capability to control the non-minimum phase, unstable processes and handle the constraints in a systematic way. MPC with integral action is an effective method to achieve the offset free control which can remove the unknown ...
openaire   +1 more source

An accurate trajectory tracking method for low-speed unmanned vehicles based on model predictive control

Scientific Reports
Trajectory tracking on a low-speed vehicle using the model predictive control (MPC) algorithm usually assumes a simple road terrain. This assumption does not correspond to the actual road situation, leading to low tracking accuracy.
Lifen Wang, Sizhong Chen, Hongbin Ren
doaj   +1 more source

Residual MPC: Blending Reinforcement Learning with GPU-Parallelized Model Predictive Control

CoRR
Model Predictive Control (MPC) provides interpretable, tunable locomotion controllers grounded in physical models, but its robustness depends on frequent replanning and is limited by model mismatch and real-time computational constraints. Reinforcement Learning (RL), by contrast, can produce highly robust behaviors through stochastic training but often
Se Hwan Jeon, Ho Jae Lee, Seungwoo Hong, Sangbae Kim   +3 more
openaire   +2 more sources

Path-Following Control of Unmanned Vehicles Based on Optimal Preview Time Model Predictive Control

World Electric Vehicle Journal
In order to reduce the lateral error of path-following control of unmanned vehicles under variable curvature paths, we propose a path-following control strategy for unmanned vehicles based on optimal preview time model predictive control (OP-MPC).
Xinyu Wang, Xiao Ye, Yipeng Zhou, Cong Li   +3 more
doaj   +1 more source

IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control

2024 IEEE International Conference on Robotics and Automation (ICRA)
Model-based reinforcement learning (RL) has shown great promise due to its sample efficiency, but still struggles with long-horizon sparse-reward tasks, especially in offline settings where the agent learns from a fixed dataset. We hypothesize that model-based RL agents struggle in these environments due to a lack of long-term planning capabilities ...
Rohan Chitnis, Yingchen Xu, Bobak Hashemi, Lucas Lehnert, Ürün Dogan, Zheqing Zhu, Olivier Delalleau   +6 more
openaire   +2 more sources

Accelerator Control Using Gaussian Process-Model Predictive Control (GP-MPC)

GP-MPC (Gaussian Process - Model Predictive Control) is a sophisticated control strategy to make informed decisions in control software systems. GPs are employed to model the dynamic behavior of the controlled system, monitoring not only expectedresponses but also uncertainties associated with the system reactions.
Aye, Su Lei, Kain, Verena, Schenk, Michael   +2 more
openaire   +2 more sources