ANFIS modeling and Direct ANFIS Inverse control of an Electro-Hydraulic Actuator system

Ling, T. G.; Rahmat, M. F.; Husain, Abdul Rashid

doi:10.1109/iciea.2013.6566397

Cited by 9 publications

(8 citation statements)

References 22 publications

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“…Where x1 and x2 are the crisp inputs, A1, B1 are linguistic variables shown in Figure 10. ANFIS architecture which consist of five layers can be explained as follows [15]:…”

Section: Anfis Controller Modelingmentioning

confidence: 99%

Comparison of FLC and ANFIS Methods to Keep Constant Power Based on Zeta Converter

Mufa’ary¹,

Sudiharto²,

Murdianto

2021

Intek

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The rapid development of technology encourages humans to always create various types of renewable innovations, which are useful for facilitating work and fulfill user’s order as desired. Especially in household appliances that use renewable energy sources in the form of solar cell, this implementation produces a fluctuating output power according to the properties of solar cell. So, it needs to be stabilized by zeta converter with the help of technology in the engineering sector, it is carried out by means of an interface as a liaison between the software and the controlled hardware. Therefore, a fuzzy set theory emerged to solve the problem in control design. However, there are other controls that can improve fuzzy deficiencies, called ANFIS. ANFIS has advantages in the learning process from the plant and the rules that will be made by the Neural Network have the main ability in terms of learning and adaptation, then decision making is done by FLC. This paper aims to compare the performance of the FLC and ANFIS as a control to keep stability of the output power of the zeta converter, where the converter work like a buck-boost converter that can increase or decrease the output power to be consumed in order to stabilize. The use of these two controllers can also compare the time at steady state and the constant power before learning occurs and after learning process. The simulation results show that the accuracy of ANFIS is 99.82% higher than accuracy of FLC which is 98.08%.

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“…Where x1 and x2 are the crisp inputs, A1, B1 are linguistic variables shown in Figure 10. ANFIS architecture which consist of five layers can be explained as follows [15]:…”

Section: Anfis Controller Modelingmentioning

confidence: 99%

Comparison of FLC and ANFIS Methods to Keep Constant Power Based on Zeta Converter

Mufa’ary¹,

Sudiharto²,

Murdianto

2021

Intek

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“…Within the scope of this paper, ANFIS is used to obtain the nonlinear inverse model. In Ling et al (2013), the rule definitions for open- and closed-loop ANFIS inverse control are made and applied to the nonlinear sample systems.…”

Section: Neuro-fuzzy Inverse System Identificationmentioning

confidence: 99%

Neuro-fuzzy iterative learning control for 4-poster test rig

Dursun

Cansever

Üstoğlu

2020

Transactions of the Institute of Measurement and Control

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In this paper, a new control method is presented for the 4-poster test systems. The primary aim of the paper is to improve the convergence speed and decrease the error rate for model-based iterative learning control (ILC), a widely used method as a tracking control. First, the dynamic equations of the system are generated, and the control problem is formulated. Then, an inverse model of the system is established directly through the adaptive neuro-fuzzy inference system (ANFIS) with auxiliary parameter (piston position) as a serial combination of two sub-models. In order to construct a neuro-fuzzy ILC (NFILC) structure, these sub-models are integrated into the neuro-fuzzy inverse controller (NFIC). Because of this new structure, the modified ILC rule has two layers. In the first layer, the controlled parameter, namely, the acceleration is iterated, whereas, in the second layer, the auxiliary parameter is iterated. The outcomes of the proposed control method are scrutinized by testing through a numerical simulation. Finally, it is demonstrated that the modified ILC rule dramatically increase the convergence speed and reduce the final error rate.

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“…It assumes a parameterized fuzzy structure firstly and uses the data to train the Fuzzy Inference System (FIS) model. Then, according to a selected error criterion, it corrects the member function parameters to make the FIS harmonize the training data [12][13]. ANFIS can get rid of the condition where the membership functions are designed by relying on the human mind in the process of the traditional fuzzy logic reasoning.…”

Section: Bayesian Anfis Icing Risk Evaluation Modelmentioning

confidence: 99%

Intelligent Risk Evaluation of Transmission Line Icing Based on Bayesian ANFIS

Huang¹,

Ma²,

Wu³

et al. 2016

Proceedings of the 2016 5th International Conference on Energy and Environmental Protection (ICEEP 2016)

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Transmission line icing is a serious threat to the security of power system. Risk evaluation for icing is the foundation of building ice disaster defense system and ensuring the safety of power system. In this paper, the index system of transmission line icing risk assessment was developed in consideration of the influence factors. By applying Bayesian inference to ANFIS, the Bayesian ANFIS icing risk evaluation model was established and was applied into practice. Experiments showed that, the ANFIS system can optimize the parameters of the network by applying Bayesian inference and has good learning abilities and adaptabilities. The method is effective and feasible in the risk evaluation of transmission line icing.

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ANFIS modeling and Direct ANFIS Inverse control of an Electro-Hydraulic Actuator system

Cited by 9 publications

References 22 publications

Comparison of FLC and ANFIS Methods to Keep Constant Power Based on Zeta Converter

Comparison of FLC and ANFIS Methods to Keep Constant Power Based on Zeta Converter

Neuro-fuzzy iterative learning control for 4-poster test rig

Intelligent Risk Evaluation of Transmission Line Icing Based on Bayesian ANFIS

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