Real-time control for the high order, wide field DRAGON AO test bench

2014 ◽  
Author(s):  
Alastair Basden ◽  
Nazim A. Bharmal ◽  
Urban Bitenc ◽  
Nigel Dipper ◽  
Tim Morris ◽  
...  
Keyword(s):  
Real Time ◽  
Test Bench ◽  
High Order ◽  
Wide Field ◽  
Real Time Control ◽  
Time Control

Real time control via a high order sliding mode controller of a multi-cellular converter

2009 ◽  
Vol 42 (17) ◽  
pp. 346-351
Author(s):  
M. DJEMAI ◽  
K. BUSAWON ◽  
K. BENMANSOUR ◽  
A. MAROUF
Keyword(s):  
Real Time ◽  
Sliding Mode ◽  
High Order ◽  
Sliding Mode Controller ◽  
Real Time Control ◽  
Time Control

Dynamic Test and Real-time Control Platform of Anode Recirculation for PEM Fuel Cell Systems

2006 ◽  
Vol 3 (3) ◽  
pp. 333-345 ◽  
Author(s):  
Cheng Bao ◽  
Kexun Zhang ◽  
Minggao Ouyang ◽  
Baolian Yi ◽  
Pingwen Ming
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Real Time ◽  
Test Bench ◽  
Future Application ◽  
Real Time Control ◽  
Cell Systems ◽  
Time Control ◽  
Injection Pump ◽  
Wide Range

Anode recirculation is essential to the pure-hydrogen proton exchange membrane fuel cell system. Keeping the pressure difference between the anode and the cathode is also important to the membrane health. In this paper, a dynamic platform was designed for the recirculation test of injection pump and real-time control of the anode pressure tracking. The test bench can work in a wide range of conditions for high- and low-pressure application. Based on the MATLAB/xPC Target environment, some S functions were written to drive the PC board for the hardware-in-loop application. Then an analytical full-order and a reduced-order model were built with good accuracy. By linearization of the nonlinear dynamic model, a linear quadratic Gaussian algorithm based on state feedback was used for set-point tracking. Moreover, an adaptive fuzzy neural network with an on-line neural network identifier was also designed to improve the control robustness. The foundation of the test bench and realization of the real-time control algorithms are meaningful to the future application in fuel cell systems.

Real-Time Control Based On High Order Neural Networks Using Stochastic Estimation

2016 ◽  
Author(s):  
Carlos E. Castañeda ◽  
Fidencio C. Hermosillo ◽  
P. Esquivel ◽  
Francisco Jurado
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
High Order ◽  
Stochastic Estimation ◽  
Real Time Control ◽  
Time Control

Evaluation of the Xeon phi processor as a technology for the acceleration of real-time control in high-order adaptive optics systems

2014 ◽  
Author(s):  
David Barr ◽  
Alastair Basden ◽  
Nigel Dipper ◽  
Noah Schwartz ◽  
Andy Vick ◽  
...  
Keyword(s):  
Real Time ◽  
Adaptive Optics ◽  
High Order ◽  
Xeon Phi ◽  
Real Time Control ◽  
Time Control

A Real Time Control Architecture for Continuously Managing Patients in a Care Unit

1995 ◽  
Vol 34 (05) ◽  
pp. 475-488
Author(s):  
B. Seroussi ◽  
J. F. Boisvieux ◽  
V. Morice
Keyword(s):  
Real Time ◽  
Linear Time ◽  
Treatment Plan ◽  
Control Architecture ◽  
Real Time Control ◽  
Time Representation ◽  
Time Control ◽  
Continuous Support ◽  
Definition Of ◽  
Computerized System

Abstract:The monitoring and treatment of patients in a care unit is a complex task in which even the most experienced clinicians can make errors. A hemato-oncology department in which patients undergo chemotherapy asked for a computerized system able to provide intelligent and continuous support in this task. One issue in building such a system is the definition of a control architecture able to manage, in real time, a treatment plan containing prescriptions and protocols in which temporal constraints are expressed in various ways, that is, which supervises the treatment, including controlling the timely execution of prescriptions and suggesting modifications to the plan according to the patient’s evolving condition. The system to solve these issues, called SEPIA, has to manage the dynamic, processes involved in patient care. Its role is to generate, in real time, commands for the patient’s care (execution of tests, administration of drugs) from a plan, and to monitor the patient’s state so that it may propose actions updating the plan. The necessity of an explicit time representation is shown. We propose using a linear time structure towards the past, with precise and absolute dates, open towards the future, and with imprecise and relative dates. Temporal relative scales are introduced to facilitate knowledge representation and access.

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