A new control method based on type-2 fuzzy neural PI controller to improve dynamic performance of a half-bridge DC–DC converter

2016 ◽  
Vol 214 ◽  
pp. 718-728 ◽  
Author(s):  
Amir Sharifian ◽  
Samaneh Fathi Sasansara ◽  
Alireza Agah Balgori
Keyword(s):  
Control Method ◽  
Dynamic Performance ◽  
Pi Controller ◽  
Fuzzy Neural

scholarly journals A Novel Fuzzy PI Control Method for Variable Frequency Brushless Synchronous Generators

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7950
Author(s):  
Yongjie Wang ◽  
Huizhen Wang ◽  
Weifeng Liu ◽  
Qin Wang
Keyword(s):  
Fuzzy Logic Controller ◽  
Control Method ◽  
Dynamic Performance ◽  
State Space Model ◽  
Synchronous Generator ◽  
Stage Structure ◽  
Pi Controller ◽  
Operating Conditions ◽  
Response Characteristics ◽  
Fuzzy Pi Controller

With the application of more electric aircraft (MEA) technology, variable frequencies and high power ratings become import features of aero-generators. The brushless synchronous generator, which has a three-stage structure, is the most commonly used type of aero-generator. Due to the variation of operating conditions, the implementation of generator controllers becomes more and more difficult. In this paper, a state space model of a generator is derived and the influence of different operating conditions on the frequency response characteristics of the generator is revealed. Based on a fuzzy PI controller, an additional fuzzy logic controller is applied to modify the PI parameters of the voltage loop by introducing the generator speed to cope with the speed variation. Finally, the results of the simulations and experiments demonstrate that the dual fuzzy PI controller can improve both the steady-state and dynamic performance of the brushless synchronous generator, verifying the previous theoretical study.

Study on Application of Interval Type 2 Fuzzy Logic Control for Gap Width Controller Used in EDM Machine

2013 ◽  
Vol 365-366 ◽  
pp. 863-869 ◽  
Author(s):  
Kotler Ter Pey Tee ◽  
Reza Hosseinnezhad ◽  
Milan Brandt ◽  
John Mo
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Control Method ◽  
Pi Controller ◽  
Logic Control ◽  
Highly Nonlinear ◽  
Gap Control ◽  
Interval Type ◽  
Gap Width

This paper presents a new method for controlling the gap distance in an EDM machine. Existing gap width control method using PI controller does not perform efficiently in a highly nonlinear and time-variant process such as EDM. In this method, constant tuning of the PI controller is required to achieve a stable and efficient EDM process. The new gap control method present in this paper uses an artificial intelligent type 2 fuzzy logic control to control the gap distance between the electrode and the workpiece. The main advantage of this proposed method is its robustness that would rid the practitioners from the tedious tuning job and would provide the industry with better accuracy and confidence in the control performance.

Fuzzy Neural Network Applied in Sensorless BLDC Motor Control Based on MC56F8013

2011 ◽  
Vol 63-64 ◽  
pp. 841-845
Author(s):  
Li Huo Wang ◽  
Wei Yao ◽  
Ke Wei Hu ◽  
Wei Zhang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Dynamic Performance ◽  
Fast Response ◽  
Current Response ◽  
Bldc Motor ◽  
Stator Core ◽  
Rotor Position ◽  
Fuzzy Neural

This paper describes Freescale company’s DSP device MC56F8013 implementation of a control method for sensorless BLDC motor, which is according to fuzzy neural network. The inductance of stator core varies according to the variation of rotor position, and the rotor position can be estimated with the variation of current response caused by six direction voltage vectors. Then use fuzzy neural network PI control method to optimize the dynamic performance, accelerate the rotor to a certain speed, and switch to EMF mode. The experiment results show that the proposed method can have fast response, strong robustness and superior performances.

scholarly journals An interval type-2 fuzzy logic controller design method for hydraulic actuators of a human-like robot by using improved drone squadron optimization

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141989155 ◽  
Author(s):  
Haozhen Dong ◽  
Liang Gao ◽  
Pi Shen ◽  
Xinyu Li ◽  
Yan Lu ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
High Performance ◽  
Fuzzy Logic Controller ◽  
Heuristic Algorithms ◽  
Control Method ◽  
Dynamic Performance ◽  
System Control ◽  
Hydraulic Actuator ◽  
Interval Type

Hydraulic actuator becomes an increasingly concerned driver for human-like robots. However, its dynamic performance under the control should be still further improved because hydraulic system is a typical nonlinearity system. Interval type-2 fuzzy logic controller is an advanced control method featured with high performance to deal with uncertain and nonlinear dynamics, so designing an interval type-2 fuzzy logic controller for the control of hydraulic is a feasible method. In this article, an improved drone squadron optimization-based approach is proposed to optimize interval type-2 fuzzy logic controller parameters. To verify the feasibility and priority of improved drone squadron optimization, a comparison on three different typical plants including proportional-derivative (PD) system, proportional-integral (PI) system, and PI nonlinear system between improved drone squadron optimization and other meta-heuristic algorithms is carried out. Simulation results demonstrate that improved drone squadron optimization not only gets an appropriate interval type-2 fuzzy logic controller for system control but also outperforms other popular algorithms in accuracy of performance.

Distributed adaptive dynamic surface formation control for uncertain multiple quadrotor systems with interval type-2 fuzzy neural networks

2018 ◽  
Vol 41 (7) ◽  
pp. 1861-1879 ◽  
Author(s):  
Teh-Lu Liao ◽  
Wei-Shou Chan ◽  
Jun-Juh Yan
Keyword(s):  
Neural Networks ◽  
Formation Control ◽  
Control Method ◽  
Fuzzy Neural Networks ◽  
Consensus Algorithm ◽  
Dynamic Surface ◽  
Adaptive Dynamic ◽  
Fuzzy Neural ◽  
Interval Type

This paper presents a distributed adaptive formation control method for uncertain multiple quadrotor systems under a directed graph that characterizes the interaction among the leader and followers. The proposed approach is based on an adaptive dynamic surface control, consensus algorithm and graph theory, where the system uncertainties are approximately modelled by interval type-2 fuzzy neural networks. The adaptive laws of interval type-2 fuzzy neural network parameters are derived from the stability analysis. In this study, the robust stability of the closed-loop system is guaranteed by the Lyapunov theorem, and the leader-follower formation goal can be asymptotically achieved. The developed control scheme is applied to the followers of quadrotor systems for performance evaluations. Simulation results are also provided to compare with the existing methods and reveal the superiority of the proposed adaptive formation controller.

scholarly journals Dynamic performance improvement of an ultra-lift Luo DC–DC converter by using a type-2 fuzzy neural controller

2018 ◽  
Vol 69 ◽  
pp. 171-182 ◽  
Author(s):  
Amir Sharifian ◽  
Samaneh Fathi Sasansara ◽  
M. Jabbari Ghadi ◽  
Sahand Ghavidel ◽  
Li Li ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Dynamic Performance ◽  
Neural Controller ◽  
Fuzzy Neural

Influence of different fuzzy operators on analytical structure and variable gains of typical interval type-2 fuzzy PI controller

2020 ◽  
Vol 39 (3) ◽  
pp. 4319-4329
Author(s):  
Haibo Zhou ◽  
Chaolong Zhang ◽  
Shuaixia Tan ◽  
Yu Dai ◽  
Ji’an Duan ◽  
...  
Keyword(s):  
Pi Controller ◽  
Real Time Control ◽  
Fuzzy Operators ◽  
Fuzzy Operator ◽  
Analytical Structure ◽  
Product Operator ◽  
Pi Controllers ◽  
Fuzzy Pi Controller ◽  
Interval Type

The fuzzy operator is one of the most important elements affecting the control performance of interval type-2 (IT2) fuzzy proportional-integral (PI) controllers. At present, the most popular fuzzy operators are product fuzzy operator and min() operator. However, the influence of these two different types of fuzzy operators on the IT2 fuzzy PI controllers is not clear. In this research, by studying the derived analytical structure of an IT2 fuzzy PI controller using typical configurations, it is proved mathematically that the variable gains, i.e., proportional and integral gains of typical IT2 fuzzy PI controllers using the min() operator are smaller than those using the product operator. Moreover, the study highlights that unlike the controllers based on the product operator, the controllers based on the min() operator have a simple analytical structure but provide more control laws. Real-time control experiments on a linear motor validate the theoretical results.

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