Results 81 to 90 of about 509,541 (208)

Sequential Model Predictive Torque Control for Six-Phase Machines Without Weighting Factors

IEEE Open Journal of Power Electronics
Model predictive control has become a powerful and versatile control strategy, particularly effective in controlling multiphase machines. One of its key advantages is the ability to address multiple control objectives by incorporating constraints ...
Jorge Rodas, Osvaldo Gonzalez, Paola Maidana, Christian Medina, Jesus Doval-Gandoy, Margarita Norambuena, Magno Ayala, Jose Rodriguez   +7 more
doaj   +1 more source

Observation of charge ordering signal in monovalent doped Nd0.75Na0.25-xKxMn1O3 (0 ≤ x ≤ 0.10) manganites [PDF]

, 2018
K doping in the compound of Nd0.75Na0.25-xKxMn1O3 (x = 0, 0.05 and 0.10) manganites have been investigated to study its effect on crystalline phase and surface morphology as well as electrical transport and magnetic properties.
Ibrahim, Norazilah, Noh, Mohamad Zaky, Razali, Sufia Aqilah, Shamsuddin, Suhadir   +3 more
core  

Time-Optimal Path Tracking via Reachability Analysis

, 2018
Given a geometric path, the Time-Optimal Path Tracking problem consists in finding the control strategy to traverse the path time-optimally while regulating tracking errors.
Pham, Hung, Pham, Quang-Cuong
core   +1 more source

An improved finite set model predictive control of SRM drive based on a voltage vectors strategy for low torque ripple

Heliyon
The robust rotor structure and fault-tolerance characteristics of the Switched Reluctance Motors (SRMs) are the best choice for next-generation Electric Vehicle (EV) applications. This machine has few restraints like high torque and flux ripples. However,
Deepak Mohanraj, Devakirubakaran S, Praveen Kumar Balachandran   +2 more
doaj   +1 more source

Model Predictive Torque Control for Velocity Tracking of a Four-Wheeled Climbing Robot. [PDF]

Sensors (Basel), 2020
Santos HB, Teixeira MAS, Dalmedico N, de Oliveira AS, Neves-Jr F, Ramos JE, de Arruda LVR.   +6 more
europepmc   +1 more source

Learning a Structured Neural Network Policy for a Hopping Task

, 2018
In this work we present a method for learning a reactive policy for a simple dynamic locomotion task involving hard impact and switching contacts where we assume the contact location and contact timing to be unknown.
Herzog, Alexander, Kozolinsky, Jules, Righetti, Ludovic, Viereck, Julian   +3 more
core   +1 more source

Model-free predictive flux vector control for N ∗ 3-Phase PMSM drives considering parameters mismatch

International Journal of Electrical Power & Energy Systems
This paper develops a novel model free-predictive flux vector control method (MF-PFVC) for high-performance stator flux and torque monitoring of the multi-module three-phase PMSM (N ∗ 3-Phase PMSM) drive with parameters mismatch.
Gongping Wu, Xiangyuan Chen, Qiuwei Wu, Zhuo Long, Sheng Huang, Changfan Zhang   +5 more
doaj   +1 more source

Direct model predictive torque control for permanent magnet synchronous motor with voltage vector parallel optimisation algorithm

IET Electric Power Applications
The multi‐vector‐based direct model predictive torque control (DMPTC) inherits the high control precise of vector control and the fast dynamic response of model predictive control.
Shejuan Qiao, Jin Huang, Yuhao Xu, Ke Song   +3 more
doaj   +1 more source

Weightless Model Predictive Control for Permanent Magnet Synchronous Motors with Extended State Observer

Applied Sciences
Traditional model predictive torque control (MPTC) predicts the torque and flux values for the next time step and selects the voltage vector that minimizes the cost function as the optimal vector to apply to the inverter.
Quanfu Geng, Quanhui Liu, Weiguang Zheng
doaj   +1 more source

Fault tolerant model predictive control of three-phase permanent magnet synchronous motors [PDF]

, 2013
A new fault tolerant model predictive control (FTMPC) strategy is proposed for three-phase magnetically isotropic permanent magnet synchronous motor (PMSM) with complete loss of one phase (LOP) or loss of one leg (LOL) of the inverter.
Bai, J, Sun, Y, Teng, Q, Zhu, J
core