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Global Automotive Adaptive Cruise Control Market Size To

The Global Automotive Adaptive Cruise Control Market Size was Valued at USD 15.63 Billion in 2023 and the Worldwide Automotive Adaptive Cruise Control.

Mexico
Germany
South-africa
Australia
Saudi-arabia
New-york
United-states
Canada
Argentina
France
Brazil
South-korea

Researchers develop new control method that optimizes autonomous ship navigation

Researchers develop new control method that optimizes autonomous ship navigation
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South-korea
Korea
Daejeong-kim
Yiming-zhang
Division-of-navigation-convergence
National-korea-maritime-ocean-university
Korea-maritime-ocean-university
Korea-maritime
Ocean-university
Maritime-autonomous-surface-ships
Model-predictive-control
Assistant-professor-daejeong-kim

"Real-time integrated path tracking control using input dimensionality-" by Guodong Wang, Li Liu et al.

The integrated path tracking control (PTC) of steering and braking is crucial for enhancing the stability of autonomous vehicles under extreme conditions. In the present study, a control input dimensionality-reducing method and an asynchronous sampling method are presented to address the problem of the high computational cost of the steering and braking integrated path tracking controller (PTCer) based on model predictive control (MPC). First, based on tire friction limit and tire force utilization, the control input dimensionality-reducing method is designed and a vehicle model with reduced dimensionality of control input is derived. Second, the rolling iteration mechanism of MPC is analyzed and a variable-scale asynchronous sampling method between the control loop and prediction horizon is designed with the control horizon as the boundary. Finally, the integrated MPC-PTCer based on control input dimensionality-reducing and asynchronous sampling is designed. The real-time performance

Autonomous-vehicle
Ntegrated-path-tracking-control
Model-predictive-control
Onlinear-vehicle-dynamics
Real-time-performance

"Improved Model Predictive Torque Control with Reduced Active Predictio" by Nazmul Islam Nahin, Shuvra Prokash Biswas et al.

High-performance control, precise torque regulation, and minimal stator current total harmonic distortion (THD) of induction motor drives (IMDs) have always been considered an industrial concern. The conventional finite control set model predictive control (FCS-MPC) strategy suffers from high computational complication, increased torque ripple, and stator current THD, which is employed to drive the voltage source inverter (VSI) based IMD. This paper proposes an optimized method of selecting prediction vectors to minimize the computational cost of the traditional FCS-MPC for a two-level VSI-based IMD. By minimizing the number of prediction vectors from six to three utilizing the proposed strategy, the cost function is assessed for only four vectors. The proposed improved model predictive control (MPC) is based on finite control set predictive torque control (FCS-PTC). The proposed improved MPC strategy also prioritizes the selection of the zero vector by avoiding the phase arm that carr

Cost-function
Induction-motor-drive
Model-predictive-control
Voltage-source-inverter

Making waves with MPC

Making waves with MPC
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Malaysia
Egypt
United-states
China
Denmark
Aalborg-portland
Rockwell-automation
Model-predictive-control
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