Design of fuzzy logic controller for a non-linear system

Amuthameena, S.; S.Monisa,

doi:10.1109/iceice.2017.8191844

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…It has been a robot leg along an essential position covering the three joints: hip, knee, and lower leg. In [20] Authors planned of Fuzzy Logic Controller for a traffic signal system. Best sign settings can be fixed while passages of the excursion & system stream areunder equilibrium.…”

Section: Literature Reviewmentioning

confidence: 99%

Distinct Architectures of Feed Forward Neural Network for Implementing A Human Swing Leg System

2023

IJCCCE

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The artificial intelligence techniques such as neural networks and fuzzy systems play an important role to disconnect flexion & expansion of the swing leg, the earth response force of the other foot has been redesigned. Underthat paper, we think the fuzzy controller plan issue for yield following flawed genuine investigation of nonlinear systems. For examination, an essential fuzzy control plot has been bristly developed dependent on a current methodology delegate under the ﬁeld.In this paper, the Feedforward Neural Network has been implemented with integer, fixed point and floating point data representations. Additionally, The Fuzzy Logic Controllers in both analog and digital forms has been implemented in hardware. Both designs use less hardware resources and operate with reasonable speed compared to other existing designs. The digital implementation of Fuzzy Logic Controller has been tested for a simulated first order liquid level process and the performance results have been compared with those of the Matlab version of Fuzzy Logic Controller. Here, Fuzzy Logic Controller is used as the controller and is trained adaptively for the changes in process parameters using recursive k-means clustering algorithm for updating the centers of the hidden layer and Recursive Least Square algorithm for updating the weights of the output layer, the result of the settling time about 20ms and takes 20 iterations, and the squared error reaches zero at approximately 20 µs. Index Terms— Fuzzy Controller, leg interaction force, Prosthetic legs, Human walking, GyroAccelerate,Fuzzy Logic System.

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Section: Literature Reviewmentioning

confidence: 99%

Distinct Architectures of Feed Forward Neural Network for Implementing A Human Swing Leg System

2023

IJCCCE

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“…However, the optimized PI controller has some disadvantages when it deals with non-linear behavioural problems such as high starting overshoot, sensitivity to controller gains and steady-state error. According to the complicated dynamic nature of human behaviour, FLC, which has a higher capability in dealing with non-linear dynamic responses, was, therefore, adopted [18][19]. A comparison of overall HRI qualitative performance of the control schemes was investigated.…”

Section: Implementation Of Pi and Flc Schemes To Implicit Force Controlmentioning

confidence: 99%

Implicit force control approach for Safe physical Robot-to-Human object handover

Neranon

2020

IJEECS

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This research focuses on the development of the conceptual frameworks of human-human interaction applied for a robotic behaviour-based approach for safe physical human-robot interaction. The control has been constructed based on understanding the dynamic and kinematic behavioural characteristics of how two humans pass an object to each other. This has enabled a KR-16-KUKA robot to naturally interact with a human so as to facilitate the dexterous transfer of an object in an effective manner. Implicit force control based on Proportional Integral and Fuzzy Logic Control which allows the robot end effector's trajectory to be moderated based on the applied force in real-time was adopted. The experimental results have confirmed that the quantitative performance of the force-controlled robot is close to that of the human and can be considered acceptable for human-robot interaction. Furthermore, the control based Fuzzy Logic Control was shown to be slightly superior performance compared to Proportional Integral control.

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“…In the inference process, the output is determined by the linguistic values (fuzzified output).The process of converting the fuzzified output into a crisp value is called Defuzzification [10]. In this architecture all the rules are evaluated to produce the final fuzzy value which is converted into a crisp set by defuzzification process.…”

Section: Fuzzy Logic Controller[12]mentioning

confidence: 99%

Draft Fan Control using Fuzzy Logic in Thermal Power Plant

Sakthi¹,

Yuvarani²,

Kiruthika³

2019

IJEAT

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In a thermal power plant combustion process in the boilers plays a vital role and it has to be controlled effectively. At present, this is controlled using draft fans employed with DCS. This paper deals with the controlling process using Fuzzy logic. The goal of this task is to manage the Forced draft fan, Secondary air fan, Grid refining fan and induced draft fan in the boiler. Every one of these fans assume a significant job in controlling the temperature of the boiler during the burning (combustion) procedure. Utilizing DCS to control the temperature is mind-boggling expense and furthermore it needs a different server, a processor and a workstation to complete the procedure. In the case of software based Fuzzy logic controller, the controlling process is the cheapest and the easiest method to maintain and control

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Design of fuzzy logic controller for a non-linear system

Cited by 9 publications

References 17 publications

Distinct Architectures of Feed Forward Neural Network for Implementing A Human Swing Leg System

Distinct Architectures of Feed Forward Neural Network for Implementing A Human Swing Leg System

Implicit force control approach for Safe physical Robot-to-Human object handover

Draft Fan Control using Fuzzy Logic in Thermal Power Plant

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