Design of fuzzy state feedback controller for robust stabilization of uncertain fractional-order chaotic systems

2014 ◽  
Vol 351 (12) ◽  
pp. 5480-5493 ◽  
Author(s):  
Xia Huang ◽  
Zhen Wang ◽  
Yuxia Li ◽  
Junwei Lu
Keyword(s):  
Fractional Order ◽  
State Feedback ◽  
Chaotic Systems ◽  
Robust Stabilization ◽  
Feedback Controller ◽  
State Feedback Controller ◽  
Fuzzy State

scholarly journals State variable-fuzzy prediction control strategy for superheated steam temperature of thermal power units

2021 ◽  
Vol 25 (6 Part A) ◽  
pp. 4083-4090
Author(s):  
Xuan Tu ◽  
Jiakui Shi ◽  
Kun Yao ◽  
Jie Wan ◽  
Fei Qiao
Keyword(s):  
State Feedback ◽  
Superheated Steam ◽  
Thermal Power ◽  
The State ◽  
Feedback Controller ◽  
Steam Temperature ◽  
State Feedback Controller ◽  
State Variable ◽  
Superheated Steam Temperature ◽  
Fuzzy State

With the large-scale grid connection of new energy power, the random fluctuation existing in the power system is intensified, which leads to frequent fluctuation of load instructions of thermal power units. It is of great significance to improve the variable load performance of the coal-fired units. It is more difficult to control the superheated steam temperature (SST). In order to improve the control performance of SST, a state variable fuzzy predictive control method is proposed in this paper. Firstly, Takagi-Sugeno fuzzy state observer is used to approximate the non-linear plant of the SST. At the same time, based on the state observer, a fuzzy state feedback controller is designed to improve its dynamic characteristics. Thirdly, based on the extended predictive model of the state feedback controller, a model predictive controller is designed to realize the SST tracking control. Dynamic simulation shows the effectiveness of the strategy.

scholarly journals Adaptive Inverse Optimal Control of a Novel Fractional-Order Four-Wing Hyperchaotic System with Uncertain Parameter and Circuitry Implementation

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Chaojun Wu ◽  
Gangquan Si ◽  
Yanbin Zhang ◽  
Ningning Yang
Keyword(s):  
Optimal Control ◽  
Fractional Order ◽  
State Feedback ◽  
Feedback Controller ◽  
Uncertain Parameter ◽  
Hyperchaotic System ◽  
Inverse Optimal Control ◽  
State Feedback Controller ◽  
Oscillation Circuit ◽  
Order Four

An efficient approach of inverse optimal control and adaptive control is developed for global asymptotic stabilization of a novel fractional-order four-wing hyperchaotic system with uncertain parameter. Based on the inverse optimal control methodology and fractional-order stability theory, a control Lyapunov function (CLF) is constructed and an adaptive state feedback controller is designed to achieve inverse optimal control of a novel fractional-order hyperchaotic system with four-wing attractor. Then, an electronic oscillation circuit is designed to implement the dynamical behaviors of the fractional-order four-wing hyperchaotic system and verify the satisfactory performance of the controller. Comparing with other fractional-order chaos control methods which may have more than one nonlinear state feedback controller, the inverse optimal controller has the advantages of simple structure, high reliability, and less control effort that is required and can be implemented by electronic oscillation circuit.

Control of PMSG Wind Energy Conversion System with TS Fuzzy State-Feedback Controller

2013 ◽  
Vol 446-447 ◽  
pp. 728-732 ◽  
Author(s):  
Nikorn Kaewpraek ◽  
Wudhichai Assawinchaichote
Keyword(s):  
Energy Conversion ◽  
State Feedback ◽  
Feedback Controller ◽  
Lmi Approach ◽  
Wind Energy Conversion System ◽  
Wind Energy Conversion ◽  
State Feedback Controller ◽  
Conversion System ◽  
Energy Conversion System ◽  
Fuzzy State

This paper presents a Takagi-Sugeno (TS) fuzzy state-feedback controller based on a linear matrix inequality (LMI) approach for the permanent magnet synchronous generator of wind energy conversion system (PMSG-WECS). A dc/dc converter is considered to regulate the maximum power output of the system. To show its effectiveness, the dynamic model is replaced by the TS fuzzy model, which the proposed controller can be applied to the PMSG-WECS, while the controller gains can be obtained by solving set of a LMI approach. The proposed controller guarantees the stability of the system. Therefore, the performance of the proposed TS fuzzy state-feedback controller is assessed through the computer simulation.

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