Improvement of the Stability Performance of a Quad-Copter Helicopter by a Neuro-Fuzzy Controller

In many practical systems, stochastic behaviors usually occur and need to be considered in the controller design. To ensure the system performance under the effect of stochastic behaviors, the controller may become bigger even beyond the capacity of practical applications. Therefore, the actuator saturation problem also must be considered in the controller design. The type-2 Takagi-Sugeno (T-S) fuzzy model can describe the parameter uncertainties more completely than the type-1 T-S fuzzy model for a class of nonlinear systems. A fuzzy controller design method is proposed in this paper based on the Interval Type-2 (IT2) T-S fuzzy model for stochastic nonlinear systems subject to actuator saturation. The stability analysis and some corresponding sufficient conditions for the IT2 T-S fuzzy model are developed using Lyapunov theory. Via transferring the stability and control problem into Linear Matrix Inequality (LMI) problem, the proposed fuzzy control problem can be solved by the convex optimization algorithm. Finally, a nonlinear ship steering system is considered in the simulations to verify the feasibility and efficiency of the proposed fuzzy controller design method.

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An Adaptive Neuro-Fuzzy Control of Pneumatic Mechanical Ventilator

Actuators ◽

10.3390/act10030051 ◽

2021 ◽

Vol 10 (3) ◽

pp. 51

Author(s):

Jozef Živčák ◽

Michal Kelemen ◽

Ivan Virgala ◽

Peter Marcinko ◽

Peter Tuleja ◽

...

Keyword(s):

Control System ◽

Control Algorithm ◽

Fuzzy Inference ◽

Fuzzy Controller ◽

Mechanical Design ◽

Mechanical Ventilator ◽

Mechanical Ventilators ◽

Inference System ◽

Control Approach ◽

Neuro Fuzzy

COVID-19 was first identified in December 2019 in Wuhan, China. It mainly affects the respiratory system and can lead to the death of the patient. The motivation for this study was the current pandemic situation and general deficiency of emergency mechanical ventilators. The paper presents the development of a mechanical ventilator and its control algorithm. The main feature of the developed mechanical ventilator is AmbuBag compressed by a pneumatic actuator. The control algorithm is based on an adaptive neuro-fuzzy inference system (ANFIS), which integrates both neural networks and fuzzy logic principles. Mechanical design and hardware design are presented in the paper. Subsequently, there is a description of the process of data collecting and training of the fuzzy controller. The paper also presents a simulation model for verification of the designed control approach. The experimental results provide the verification of the designed control system. The novelty of the paper is, on the one hand, an implementation of the ANFIS controller for AmbuBag pressure control, with a description of training process. On other hand, the paper presents a novel design of a mechanical ventilator, with a detailed description of the hardware and control system. The last contribution of the paper lies in the mathematical and experimental description of AmbuBag for ventilation purposes.

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Reinforcement Hybrid Evolutionary Learning for TSK-type Neuro-Fuzzy Controller Design

IFAC Proceedings Volumes ◽

10.3182/20080706-5-kr-1001.01101 ◽

2008 ◽

Vol 41 (2) ◽

pp. 6530-6535 ◽

Cited By ~ 1

Author(s):

Yung-Chi Hsu ◽

Sheng-Fuu Lin

Keyword(s):

Fuzzy Controller ◽

Controller Design ◽

Evolutionary Learning ◽

Neuro Fuzzy

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Adaptive Neuro - Fuzzy Controller for Stabilizing Autonomous Bicycle

2006 IEEE International Conference on Robotics and Biomimetics ◽

10.1109/robio.2006.340214 ◽

2006 ◽

Cited By ~ 6

Author(s):

N Umashankar ◽

Himanshu Dutt Sharma

Keyword(s):

Fuzzy Controller ◽

Neuro Fuzzy

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Neuro Fuzzy Controller for DTC of Induction Motor Using Multilevel Inverter with SVM

Journal of Circuits System and Computers ◽

10.1142/s0218126621502509 ◽

2021 ◽

pp. 2150250

Author(s):

B. Kiran Kumar ◽

Y. V. Siva Reddy ◽

M. Vijaya Kumar

Keyword(s):

Induction Motor ◽

Fuzzy Logic Controller ◽

Fuzzy Controller ◽

Reference Voltage ◽

Training Dataset ◽

Testing Time ◽

Hybrid Technique ◽

Nonlinear Loads ◽

Neuro Fuzzy ◽

Voltage Signal

In this paper, an effective neuro-fuzzy controller (NFC) technique has been proposed to control the induction motor torque and flux. The NFC hybrid technique is the grouping of the neural network (NN) and fuzzy logic controller (FLC), which generated the target voltages with the corresponding input flux and torque. The novelty of the proposed hybrid technique is highly flexible in nonlinear loads, convenient user interface and logical intellectual and permitting for integrated controlling schemes. Here, the FLC generates the training dataset of the NN technique based on the logical rules. The generated dataset contains the information about the flux and torque deviation parameters and the corresponding reference voltage parameters. The NN has been trained based on training dataset and the testing time which produces the optimal reference voltage parameters depends on the variation of the torque and flux parameters. By using the output of the NFC technique, the space vector modulation (SVM) develops the appropriate control pulses to the five-level inverter and the inverter generates the output voltage signal to the induction motor. The proposed method is designed in the MATLAB/Simulink platform and the outputs are verified through the comparison analysis with the existing techniques.

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