Network Optimization-Based MPC for Distributed Control Systems

2012 ◽  
Vol 482-484 ◽  
pp. 2485-2488
Author(s):  
Yan Zhang
Keyword(s):  
Control Systems ◽  
Distributed Control ◽  
Network Optimization ◽  
Small Scale ◽  
Distributed Control Systems ◽  
Fuel Feed ◽  
Optimization And Control ◽  
On Line ◽  
And Control ◽  
Walking Beam Reheating Furnace

In this paper, a novel network optimization-based MPC scheme is proposed for on-line optimization and control of a class of distributed control systems, in which the on-line optimization of the whole system is decomposed into that of several small-scale sub-systems in distributed structures. Under network environment, the connectivity of the communication network is assumed to be sufficient for each sub-system to exchange information with other sub-systems. An iterative algorithm for networked MPC with ideal information model is developed for DCS. Finally, the simulation study of the fuel feed flow control for the walking beam reheating furnace is provided to test the effectiveness and practicality of the proposed networked MPC algorithm.

scholarly journals Fault detection Automation in Distributed Control Systems using Data-driven methods: SVM and KNN

2021 ◽  
Author(s):  
Seyed Hossein Ahmadi
Keyword(s):  
Control Systems ◽  
Distributed Control ◽  
Power Distribution ◽  
Gas Turbines ◽  
Distribution Systems ◽  
Diagnostic Methods ◽  
Power Distribution Systems ◽  
Distributed Control Systems ◽  
Using Data ◽  
And Control

<p>Fault diagnostic methods with fuzzy logic methods, SVM, KNN and artificial intelligence systems have been used in complex systems such as wind turbines, gas turbines, power distribution systems, power transformers and rotary machines, but in the specific field of distributed control systems, the vacancy of this topic is strongly felt. Due to the need of the industry to detect faults quickly and in a timely manner in all modes of sensors, actuators, outputs and control logics to maintain expensive, valuable resources, important and complex equipment, it is very necessary to enter this topic. In this paper, a suitable theoretical and practical basis for diagnosing various types of faults in the DCS of a gas refinery is done. The fact that the operator quickly identifies the area and the cause of the fault can avoid huge losses in terms of downtime. Automation of fault diagnosis in DCS has not been explicitly mentioned in any article or book, and here the plan is presented for the first time. In this design, we connect MATLAB classification apps to the industrial system like DCS, then data are analyzed by SVM and KNN methods to detect faults. The results show that faults can be detected with a probability of more than 85% accuracy without the need for on-site expert force and with much less time.</p>

scholarly journals Fault detection Automation in Distributed Control Systems using Data-driven methods: SVM and KNN

2021 ◽  
Author(s):  
Seyed Hossein Ahmadi ◽  
Mohammad Javad Khosrowjerdi
Keyword(s):  
Control Systems ◽  
Distributed Control ◽  
Power Distribution ◽  
Gas Turbines ◽  
Distribution Systems ◽  
Diagnostic Methods ◽  
Power Distribution Systems ◽  
Distributed Control Systems ◽  
Using Data ◽  
And Control

<p>Fault diagnostic methods with fuzzy logic methods, SVM, KNN and artificial intelligence systems have been used in complex systems such as wind turbines, gas turbines, power distribution systems, power transformers and rotary machines, but in the specific field of distributed control systems, the vacancy of this topic is strongly felt. Due to the need of the industry to detect faults quickly and in a timely manner in all modes of sensors, actuators, outputs and control logics to maintain expensive, valuable resources, important and complex equipment, it is very necessary to enter this topic. In this paper, a suitable theoretical and practical basis for diagnosing various types of faults in the DCS of a gas refinery is done. The fact that the operator quickly identifies the area and the cause of the fault can avoid huge losses in terms of downtime. Automation of fault diagnosis in DCS has not been explicitly mentioned in any article or book, and here the plan is presented for the first time. In this design, we connect MATLAB classification apps to the industrial system like DCS, then data are analyzed by SVM and KNN methods to detect faults. The results show that faults can be detected with a probability of more than 85% accuracy without the need for on-site expert force and with much less time.</p>

scholarly journals Fault detection Automation in Distributed Control Systems using Data-driven methods: SVM and KNN

2021 ◽  
Author(s):  
Seyed Hossein Ahmadi
Keyword(s):  
Control Systems ◽  
Distributed Control ◽  
Power Distribution ◽  
Gas Turbines ◽  
Distribution Systems ◽  
Diagnostic Methods ◽  
Power Distribution Systems ◽  
Distributed Control Systems ◽  
Using Data ◽  
And Control

<p>Fault diagnostic methods with fuzzy logic methods, SVM, KNN and artificial intelligence systems have been used in complex systems such as wind turbines, gas turbines, power distribution systems, power transformers and rotary machines, but in the specific field of distributed control systems, the vacancy of this topic is strongly felt. Due to the need of the industry to detect faults quickly and in a timely manner in all modes of sensors, actuators, outputs and control logics to maintain expensive, valuable resources, important and complex equipment, it is very necessary to enter this topic. In this paper, a suitable theoretical and practical basis for diagnosing various types of faults in the DCS of a gas refinery is done. The fact that the operator quickly identifies the area and the cause of the fault can avoid huge losses in terms of downtime. Automation of fault diagnosis in DCS has not been explicitly mentioned in any article or book, and here the plan is presented for the first time. In this design, we connect MATLAB classification apps to the industrial system like DCS, then data are analyzed by SVM and KNN methods to detect faults. The results show that faults can be detected with a probability of more than 85% accuracy without the need for on-site expert force and with much less time.</p>

scholarly journals Private Weighted Sum Aggregation for Distributed Control Systems

2020 ◽  
Vol 53 (2) ◽  
pp. 11081-11088
Author(s):  
Andreea B. Alexandru ◽  
George J. Pappas
Keyword(s):  
Control Systems ◽  
Distributed Control ◽  
Weighted Sum ◽  
Distributed Control Systems
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