scholarly journals Design an intelligent hybrid position/force control for above knee prosthesis based on adaptive neuro-fuzzy inference system

2021 ◽  
Vol 23 (2) ◽  
pp. 675
Author(s):  
Mithaq N. Raheema ◽  
Dhirgaam A. Kadhim ◽  
Jabbar S. Hussein
Keyword(s):  
Control System ◽  
Knee Joint ◽  
Force Control ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Knee Prosthesis ◽  
Current Control ◽  
Inference System ◽  
Control Approach ◽  
Neuro Fuzzy

<div>This paper reviews the position/force control approach for governs an efficient knee joint in an active lower limb prosthesis, and the inter facing current control algorithm with human gate parameter is inserted. Two techniques are used to collect gait cycle data of leg: first, the foot ground force is obtained by the force platform device based on its position (x, y), then data of knee joint angles is recorded by using a video-camera device.The collected information is sent and used in the proposed intelligent controller. This intelligent control system used an adaptive neuro-fuzzy inference system (ANFIS) circuit in addition to the proportional integral derivative (PID) controller. This hybrid ANFIS-PID control system simulates and provides the ground force values. The experimental results show anexcellent response and lower root mean square error (RMSE) compared with each of PID and ANFIS controller that implemented for a similar purpose. In summary, the results showed acceptably stable performance of the proposedposition/force controller based on hybrid ANFIS-PID system. It can be concluded that the finest performance of the controlled force, as quantified by the RMSE criteria, is perceived by the proposed hybrid scheme depending on the controller intelligent decision circuit.</div>

scholarly journals An Adaptive Neuro-Fuzzy Control of Pneumatic Mechanical Ventilator

Actuators ◽  
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.

Developing Adaptive Neuro-Fuzzy Inference System for Controlling the Intelligent Universal Transformers in ADA

2012 ◽  
Vol 433-440 ◽  
pp. 3969-3973
Author(s):  
Maryam Sadeghi ◽  
Majid Gholami
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Voltage Regulation ◽  
Current Control ◽  
Distribution Automation ◽  
Membership Functions ◽  
Back Propagation Algorithm ◽  
Input Output ◽  
Inference System ◽  
Neuro Fuzzy

This approach is carry out for developing the Adaptive Neuro-Fuzzy Inference System (ANFIS) for controlling the forthcoming Intelligent Universal Transformer (IUT) in regard of voltages and current control in both input and output stages which is optimized by particle swarm optimization. Current or voltages errors and their time derivative have been considered as the inputs of Nero Fuzzy controller for elaborating the firing angles of converters in IUT basic construction. ANFIS constructed from a fuzzy inference system (FIS) in which the membership function parameters are tuned according to the back propagation algorithm or in conjunction to the least squares method. A neural network maps inputs through input membership functions and associated parameters, and output membership functions and associated parameters to outputs which interprets the input-output map. The associated parameters of membership functions change through the learning algorithm by a gradient vector modeling the input output data in case of given parameters. Optimization method will be investigated to adjust the parameters according to error reduction computed by sum of the squared variation from actual outputs to the desired ones. Advanced Distribution Automation (ADA) is the state of art introducing for tomorrows distribution automation with the new invention in management and control. ADA is equipping by the Intelligent Equipment Devices (IED) in which IUT is a key point introducing as an intelligent transformer subjecting for tomorrows distribution automation in the near future. The proposed ANFIS is a control scheme develop for controlling the IUT by bringing the major advantages like harmonic Filtering, voltage regulation, automatic sag correction, energy storage, 48V DC option, three phase outputs in term of one phase input, reliable divers power as 240V 400HZ for communication utilization and two other 240V 60 HZ outputs, dynamic system monitoring and robustness in major disturbances occurred in terms of input and load variation.

Neuro-Fuzzy-Based Control for Parallel Cascade Control

2013 ◽  
Vol 8 (1) ◽  
pp. 15-25 ◽  
Author(s):  
R. Karthikeyan ◽  
K. Manickavasagam ◽  
Shikha Tripathi ◽  
K.V.V. Murthy
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Internal Model Control ◽  
Cascade Control ◽  
Regulatory Control ◽  
Inference System ◽  
Control Approach ◽  
Neuro Fuzzy ◽  
Model Control

Abstract This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptation. A combination of neural networks and fuzzy logic offers the possibility of solving tuning problems and design difficulties of fuzzy logic. Due to their complementary advantages, these two models are integrated together to form more robust learning systems, referred to as adaptive neuro-fuzzy inference system (ANFIS). The secondary controller is designed using the internal model control approach. The performance of the proposed ANFIS-based control is evaluated using different case studies and the simulated results reveal that the ANFIS control approach gives improved servo and regulatory control performances compared to the conventional proportional integral derivative controller.

An Wavelet Based Handoff Algorithm for Cognitive Wireless Network

2012 ◽  
Vol 433-440 ◽  
pp. 5087-5091
Author(s):  
Meng Jia Li ◽  
Jing Yao Wang ◽  
Mei Song ◽  
Xiao Jun Wang ◽  
Ning Ning Liu
Keyword(s):  
Control System ◽  
Wavelet Analysis ◽  
Fuzzy Control ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Second Use

This paper proposed a novel handoff algorithm for cognitive network based on wavelet analysis and fuzzy control system. It makes the system cognitive and adaptive to the changes of the environment by two steps: first, make wavelet analysis to the received signal to get the basic signal which is without noise. Second, use adaptive neuro-fuzzy inference system (ANFIS) to make diligent handoff decision. The simulation shows that it improves the performance of the whole system when the channel is in low signal-to-noise ratio.

scholarly journals Basement Flood Control with Adaptive Neuro Fuzzy Inference System Using Ultrasonic Sensor

2020 ◽  
Vol 5 (2) ◽  
pp. 11
Author(s):  
Raden Muhamad Yuda Pradana Kusumah ◽  
Maman Abdurohman ◽  
Aji Gautama Putrada
Keyword(s):  
Control System ◽  
Water Level ◽  
Fuzzy Inference System ◽  
Flood Control ◽  
Fuzzy Inference ◽  
Flood Management ◽  
Ultrasonic Sensor ◽  
Training Data ◽  
Inference System ◽  
Neuro Fuzzy

This paper proposes a basement flood management system based on Adaptive Neuro Fuzzy Inference System (ANFIS). Basement is one of the main parts of a building that has a high potential for flooding. Therefore, the existence of a flood control system in the basement can be a solution to this threat. Water level control is the key to solving the problem. Fuzzy Inference System (FIS) has proven to be a reliable method in the control system but this method has limitations, that is, it needs to have a basis or a reference when determining the fuzzy set. When there is no basis or reference, Adaptive Neuro FIS (ANFIS) can be a solution. The Neuron aspect in ANFIS determines fuzzy sets through training data. In terms of the Internet of Things (IoT), this system uses an ultrasonic sensor, Node Red IoT platform, and Matlab Server.  Then the water pump will turn on to control the water level when there is rainfall. By undergoing a comparative test with the FIS method, ANFIS provides a lower Root Mean Square Error (RMSE) and is recommended for use in basement flood management systems.

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