scholarly journals Fuzzy State Space Model of Multivariable Control Systems

2009 ◽  
Vol 2 (2) ◽  
Author(s):  
R. Ismail ◽  
K. Jusoff ◽  
T. Ahmad ◽  
S. Ahmad ◽  
R.S. Ahmad
Keyword(s):  
State Space ◽  
Control Systems ◽  
State Space Model ◽  
Multivariable Control ◽  
Space Model ◽  
Multivariable Control Systems ◽  
Fuzzy State

scholarly journals Fuzzy state space model for a pressurizer in a nuclear power plant

2015 ◽  
Vol 11 (2) ◽  
Author(s):  
A. Ashaari ◽  
T. Ahmad ◽  
Mustaffa Shamsuddin ◽  
S. Zenian
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
State Space ◽  
Nuclear Power ◽  
State Space Model ◽  
Published Data ◽  
State Space Approach ◽  
Space Model ◽  
Fuzzy State ◽  
Space Approach

In this paper, Fuzzy State Space Model (FSSM) for a nuclear power plant is proposed. Pressurizer is used to control pressure and temperature in a nuclear power plant. In order to maintain the pressure and the temperature of the system, the effectiveness of the system needs to be monitored frequently. Hence, fuzzy state space approach is used to model the pressurizer. The influence of input to output of the pressurizer is established and presented in this paper. The result from the model is then verified against published data.

scholarly journals Interaction Assessment and Stability Analysis of the MMC-Based VSC-HVDC Link

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2075 ◽  
Author(s):  
Saman Dadjo Tavakoli ◽  
Eduardo Prieto-Araujo ◽  
Enric Sánchez-Sánchez ◽  
Oriol Gomis-Bellmunt
Keyword(s):  
State Space ◽  
Control Systems ◽  
System Analysis ◽  
Dynamic Performance ◽  
State Space Model ◽  
Phase Margin ◽  
Space Model ◽  
Relative Gain Array ◽  
Multi Level ◽  
Level Converter

This paper investigates the dynamic behavior of a modular multi-level converter (MMC)-based HVDC link. An overall state-space model is developed to identify the system critical modes, considering the dynamics of the master MMC and slave MMC, their control systems, and the HVDC cable. Complementary to the state-space model, an impedance-based model is also derived to obtain the minimum phase margin (PM) of the system. In addition, a relative gain array (RGA) analysis is conducted to quantify the level of interactions among the control systems of master and slave MMCs and their impacts on stability. Finally, with the help of the results obtained from the system analysis (eigenvalue, phase margin, sensitivity, and RGA), the system dynamic performance is improved.

DECENTRALIZED STABILIZATION OF COMPLEX SYSTEMS BY COMBINATION OF CONVENTIONAL AND FUZZY CONTROLS

1999 ◽  
Vol 07 (04) ◽  
pp. 423-427
Author(s):  
QUANG VINH THAI ◽  
MANH DAO HA ◽  
SI BANG HO
Keyword(s):  
Complex System ◽  
Robust Control ◽  
State Space ◽  
Control Method ◽  
State Space Model ◽  
Linear Interaction ◽  
Decentralized Stabilization ◽  
Space Model ◽  
Decentralized Robust Control ◽  
Fuzzy State

In this paper, we deal with the problem of how to achieve stability for a complex system in which the subsystems are stable, but the non-linear interaction between the subsystems may cause instability. We assume that the uncertainties with which we know the parameters of the system are bounded, but that these bounds are not known. For such systems, we propose a new control method: a decentralized robust control with fuzzy estimation of the bounds of uncertainties, implemented as a combination of conventional and fuzzy controllers. The use of fuzzy control is motivated by the equivalent dynamic fuzzy state-space model of the system consisting of interconnected uncertain subsystems.

scholarly journals Representation of multi-connected system of Fuzzy State Space Modeling (FSSM) in potential method based on a network context

2017 ◽  
Vol 13 (4) ◽  
pp. 711-716 ◽  
Author(s):  
Jibril Aminu ◽  
Tahir Ahmad ◽  
Surajo Sulaiman
Keyword(s):  
State Space ◽  
State Space Model ◽  
Type A ◽  
Potential Method ◽  
Network System ◽  
Space Model ◽  
Space Modeling ◽  
State Space Modeling ◽  
Fuzzy State

The complexity of a system of Fuzzy State Space Modeling (FSSM) is the reason that leads to the main objective of this research. A multi-connected system of Fuzzy State Space Model is made up of several components, each of which performs a function. These components are interconnected in some manner and determine how the overall system operates. In this study, we study the concept of graph, network system and network projections which are the requisite knowledge to potential method. Finally, the multi-connected system of FSSM of type A namely feeder, common feeder and greatest common feeder are transformed into potential method using various method of transformation.

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