Adaptive Neuro-fuzzy Control System Research Articles

Enhanced ship engine vibration reduction using magnetorheological dampers and Adaptive Neuro-Fuzzy Control System

ABSTRACT In this paper, an innovative approach is proposed to mitigate ship engine vibrations by integrating a semi-active control system with magnetorheological (MR) dampers and an Adaptive Neuro-Fuzzy Control System (ANFIS). The ANFIS controller adjusts MR damper damping force to effectively adapt to engine vibrations. A comprehensive mathematical model simulates the complex dynamics of the ship engine vibration system, validated with real-world data. The model incorporates engine vibration measurements and MR damper properties to train the ANFIS controller. Compared to passive control systems, the proposed semi-active system reduces torsional vibrations by 22.606% and longitudinal vibrations by 29.840%. The ANFIS controller dynamically adjusts damping force in response to engine conditions, ensuring optimal performance. This system effectively mitigates ship engine vibrations, improving overall ship performance and passenger comfort. Consistency between modeling and experimental results confirms the system's viability and efficacy.

Design of Fast Real Time Controller for the SSSC Based on Takagi-Sugeno (TS) Adaptive Neuro-Fuzzy Control System

FACTS (Flexible AC Transmission Systems) technology is one aspect of the power electronics revolution that is taking place in all areas of electric energy. The power transfer capability of a transmission line can be enhanced by using power electronics based system like FACTS that offer high speed and reliable control capability. The Static Synchronous Series Compensator (SSSC) is the second generation of the FACTS controller, which is used to control the power flow in the transmission line. In this paper, a new scheme to control SSSC using adaptive neuro fuzzy logic is proposed, in this controller, Takagi-Sugeno fuzzy rules are trained using off-line neuro fuzzy system, also, instantaneous power theory is used to calculate the power system parameters due to its high accuracy and less computation. The simulation and practical results show that real time application of the proposed controller is possible and robust compared to conventional controllers previously investigated. The experiment results obtained using the dSPACE data acquisition system and Matlab real time toolbox.

Analysis of Grid-tied Solar Photovoltaic Energy Generation under Uncertain Atmospheric Conditions Using Adaptive Neuro-fuzzy Control System

The grid-tied photovoltaic (PV) power system has remained the most practical and sustainable configuration among renewable energy generation systems. Although uncertainties persist in solar irradiance and temperature, the grid-tied system faces transient instability issues during maximum power point tracking, adversely affecting power quality and resulting in substantial costs. To overcome this issue, we proposed analyzing the grid-tied system under uncertain atmospheric conditions based on an adaptive neuro-fuzzy control system (ANCS). This control scheme incorporates a hybrid learning algorithm and undergoes evaluation across various operating conditions. The obtained results demonstrate the effectiveness of the learning algorithm in maintaining a fast convergence speed. Consequently, this capability ensures the consistent preservation of sufficient power quality in the power system without any discernible transient impact. Furthermore, the investigation reveals the significant impact of solar radiation and temperature on the performance of the solar grid-tied PV system. Specifically, temperature alone contributes to over 15% power reduction when reaching 45 °C. As the temperature decreases to 5 °C at 1000 W/m2 irradiance, the ANCS influences an increase in the system's power generation from 100.72 kW at 25 °C to 103.01 kW.

Temperature field analysis and adaptive neuro-fuzzy inference system for mgo single crystal production

The temperature field in MgO single crystal furnace is crucial to grow high-purity MgO single crystals with large sizes. In order to build proper temperature gradient, firstly finite element method (FEM) was used to study the temperature field distributions, and then a temperature controller with adaptive neurofuzzy inference system (ANFIS) was developed based on the result of FEM and practical experiences. When the temperature in MgO single crystal furnace was changed, the controller would regulate the positions of threephase electrodes and the voltage of the power simultaneously. The experimental results indicate that using the adaptive neuro-fuzzy control system can improve the quality and the quantity of the MgO single crystal production.

Temperature Field Analysis and Process Control Strategies for MgO Single Crystal Production Using Adaptive Neuro-Fuzzy Inference System

To grow high-purity and large sizes MgO single crystals with twin-electrode DC submerged arc furnace requires that the temperature distribution be well understood and the processing temperature be precisely controlled. For the complexity of the production of MgO single crystals and the difficulty to measure the temperature inside the furnace, the temperature distribution was studied by using finite element method (FEM), and the temperature control was realized by the process control strategies with adaptive neuro-fuzzy inference system (ANFIS). Experiments were carried out to verify the effectiveness of the method. The result of experiments indicated that using the adaptive neuro-fuzzy control system can improve the quality and the quantity of the MgO single crystal production.

Adaptive neuro-fuzzy control of a flexible manipulator

This paper describes an adaptive neuro-fuzzy control system for controlling a flexible manipulator with variable payload. The controller proposed in this paper is comprised of a fuzzy logic controller (FLC) in the feedback configuration and two dynamic recurrent neural networks in the forward path. A dynamic recurrent identification network (RIN) is used to identify the output of the manipulator system, and a dynamic recurrent learning network (RLN) is employed to learn the weighting factor of the fuzzy logic. It is envisaged that the integration of fuzzy logic and neural network based-controller will encompass the merits of both technologies, and thus provide a robust controller for the flexible manipulator system. The fuzzy logic controller, based on fuzzy set theory, provides a means for converting a linguistic control strategy into control action and offering a high level of computation. On the other hand, the ability of a dynamic recurrent network structure to model an arbitrary dynamic nonlinear system is incorporated to approximate the unknown nonlinear input–output relationship using a dynamic back propagation learning algorithm. Simulations for determining the number of modes to describe the dynamics of the system and investigating the robustness of the control system are carried out. Results demonstrate the good performance of the proposed control system.

Adaptive Neuro-fuzzy Control System by RBF and GRNN Neural Networks

Recently, adaptive control systems utilizing artificial intelligent techniques are being actively investigated in many applications. Neural networks with their powerful learning capability are bein...