DYNAMICS OF THERMAL-HYDRAULIC NETWORK CONTROL STRATEGIES

A dynamic thermal-hydraulic analysis of strategies used for the control of hydronic piping and heat exchanger networks is carried out. Three common temperature control methods are analysed and compared using a mathematical model. For this purpose a general thermal network with a primary loop, a secondary loop and a bypass that has the three control systems as special cases is proposed. The primary loop includes a chiller, while the secondary has a water-air cooling coil which serves as a thermal load. Integral controllers are used to vary the valve settings to control the air temperature leaving the cooling coil. The system responses for each of the three control schemes as a function of the thermal load are then compared to assess their relative merits using the temperature drop in the chiller as a criterion.

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Comparison of thermal–hydraulic network control strategies

CrossRef Listing of Deleted DOIs ◽

10.1243/095965103321196677 ◽

2003 ◽

Vol 217 (1) ◽

pp. 35-47 ◽

Cited By ~ 2

Author(s):

W Franco ◽

M Sen ◽

K T Yang ◽

R L McClain

Keyword(s):

Control Strategies ◽

Network Control ◽

Hydraulic Network

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Research of Intelligent Control Policy on Main Steam Temperature of Thermal Power Plant

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.143-144.307 ◽

2011 ◽

Vol 143-144 ◽

pp. 307-311 ◽

Cited By ~ 4

Author(s):

Yu Feng Luo ◽

Lu Lu Liu

Keyword(s):

Neural Network ◽

Fuzzy Neural Network ◽

Control Strategies ◽

Thermal Power ◽

Network Control ◽

Neural Network Control ◽

Steam Temperature ◽

Boiler Load ◽

Fuzzy Neural ◽

Main Steam

In view of the nonlinear, time-variable, long delay, large inertia character of main steam temperature system, the difficult point of control is summarized. The status quo of application study on main steam temperature by fuzzy control, neural network and fuzzy neural network control, genetic algorithms is introduced. And taking "the 600 MW concurrent boiler load in 100%" as an example, carry on the main steam temperature control simulation by means of Matlab/Simulink software. In this simulation, four control strategies are taking for the simulation experiment, and comparing with the traditional PID control algorithm. The simulation results prove that fuzzy neural network control strategies have good robustness, fast response, short setting time, and great potential for the control of main steam temperature.

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The Neural Network Control Algorithm Research of Single Crystal Furnace Temperature System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.765-767.789 ◽

2013 ◽

Vol 765-767 ◽

pp. 789-792

Author(s):

Xin Song ◽

Ming Yu Li ◽

Li Li ◽

Chao Yang

Keyword(s):

Neural Network ◽

Single Crystal ◽

Control Strategies ◽

Time Lag ◽

Adaptive Method ◽

Network Control ◽

Temperature Control System ◽

Neural Network Modeling ◽

Neural Network Control ◽

Long Time

Straight pull single crystal furnaces temperature control system has problem of the long time lag and nonlinearity, so the precise mathematic mode that is hard to build. Advanced control strategies show strong advantages for resolving these problems. This paper use artificial neural network modeling approach to establish single crystal furnace temperatures neural network control BP structure model, use adaptive method to control the temperature of the single crystal furnace.

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Neural Network Trajectory Tracking Control of Compliant Parallel Robot

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.799-800.1069 ◽

2015 ◽

Vol 799-800 ◽

pp. 1069-1073

Author(s):

Hao Tian ◽

Yue Qing Yu

Keyword(s):

Neural Network ◽

Trajectory Tracking ◽

Tracking Control ◽

Control Method ◽

Rbf Neural Network ◽

Control Strategies ◽

Parallel Robot ◽

Network Control ◽

Trajectory Tracking Control ◽

Compliant Joint

Trajectory tracking control of compliant parallel robot is presented. According to the characteristics of compliant joint, the system model is derived and the dynamic equation is obtained based on the Lagrange method. Radial Basis Function (RBF) neural network control is designed to globally approximate the model uncertainties. Further, an itemized approximate RBF control method is proposed for higher identify precision. The trajectory tracking abilities of two control strategies are compared through simulation.

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Neural network control strategies for low specification servo actuators

International Journal of Machine Tools and Manufacture ◽

10.1016/s0890-6955(97)00067-9 ◽

1998 ◽

Vol 38 (9) ◽

pp. 1109-1124 ◽

Cited By ~ 1

Author(s):

C.F. Wong ◽

J. Shippen ◽

B. Jones

Keyword(s):

Neural Network ◽

Control Strategies ◽

Network Control ◽

Neural Network Control

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Fundamental Issues and Prospective Directions in Networked Multirate Control Systems

Mathematical Problems in Engineering ◽

10.1155/2014/513506 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Wang Zhiwen ◽

Guo Ge

Keyword(s):

Control Systems ◽

Sampling Method ◽

Control Strategies ◽

Network Control ◽

Pattern Selection ◽

Multirate Sampling ◽

Network Control Systems ◽

Multirate Control ◽

Natural Characteristics ◽

Sampling Pattern

In view of the synchronized uniform equidistant sampling method that is widely used in network control systems (NCSs), multirate sampling control for NCSs is presented by considering the natural characteristics of NCSs. Two kinds of typical structures of network multirate control systems (NMCSs) are presented and the multirate sampling mechanisms are formulated. Then, fundamental issues and prospective directions in NMCSs are preliminarily discussed. The contents involve modeling, sampling pattern selection, control strategies, scheduling algorithms, and codesign of NMCSs. The purpose of this paper is to arouse more attention of researchers in related subjects, so that a complete theory for NCSs can be established.

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