Adaptive Network-based Fuzzy Inference System Controller Research Articles

Neuro-fuzzy-based anti-swing control of automatic tower crane

Controlling the position of the final load and the anti-swing control of the loads during the operation of the tower crane are challenging tasks. These are the most important control issues for safe operation, which are difficult to achieve easily with conventional control systems. Hence, the need to integrate the concepts of soft-computing into the tower crane control system. The aim of this research work is to design an adaptive-network-based fuzzy inference system (ANFIS) controller to move the payload to the final position with the lowest possible swing angle. To evaluate the ability of the proposed controller to meet the control requirements, its performance was compared to three other controllers: a conventional proportional derivative (PD) controller, a fuzzy-tuned PD controller and a fuzzy controller. MATLAB-based computer simulations of the crane and controllers were carried out to verify and compare the performance of the proposed controllers. The obtained results show the effectiveness of the ANFIS-based controller in adjusting the load position while keeping the load fluctuations small at the final position. The load oscillation angle is about ±2.28° with the ANFIS controller while it is about ±10° when using the PD controller. In addition, only one ANFIS controller is used for both load position and swing angle control.

Shared control based on ANFIS for brain-controlled driving

Using electroencephalography (EEG) signals to drive a vehicle could help disabled people expand their range of motion and improve their independence. A brain-controlled vehicle (BCV) is a vehicle that is commanded by analyzing EEG signals. However, the analysis and transmission effect of EEG signals is not ideal, the driving performance of the BCV solely relying on EEG signals is relatively poor. In this paper, to solve this problem, we propose a dynamic shared control method based on adaptive network-based fuzzy inference system (ANFIS). First, an ANFIS intelligent controller is designed to automatically make decisions according to the state of the vehicle. Then, safety coefficient and intention coefficient are proposed to evaluate the safety and driving intention of the brain-controlled driver. Finally, a fuzzy controller with safety and intention coefficients as inputs and brain-controlled driver weights as outputs is designed. The controller is the embodiment of a human–machine interaction, which allows the driver to maintain maximum control authority over the BCV under safe conditions by dynamically balancing the control authority of the brain-controlled driver and the ANFIS controller on the BCV. To verify the effectiveness of the proposed method, a joint simulation platform of Carsim and Matlab is established, and several groups of comparative simulation experiments are carried out, through which, it is demonstrated that the proposed method can effectively avoid road deviation while well maintaining the control authority of the brain-controlled driver.

Design and Analysis of ANFIS Controller for High Accuracy Magnetic Levitation (ML) System

Magnetic Levitation (ML) System is a significant particular research center model for the planning and examination of criticism control frameworks. Due to the usual sensitivity of mass, a solid choppiness powers here between magnets, and also due to the effects of commotion spilling out of the sensor and information channels, the superiority of attractive lift frameworks is dangerous. Thereafter, the design of a control framework for height is a complex issue that must be considered when developing models that are not 100% exact. Elite is the goal, all else being equal, of being better, faster, or much more productive over others. In this paper, a plan is made for an Adaptive Network-Based Fuzzy Inference System (ANFIS) regulator to get the MLS of a stable region by suspending a ball in mid-air in the sight of potential vulnerabilities, controller with a (PID). The main objective is to achieve perfect behavior through improving regulators' incentives. Reenactments have been conducted using MATLAB Version 2019b, and the positive results obtained demonstrate that the planned control unit meets the elite versus vulnerability in the model and allows exceptionally precise positioning of the raised article

Performance Analysis and Implementation of Brushless DC Motor by PID, Fuzzy and ANIFS Controllers

Now a day’s Brushless DC (BLDC) motors are more popular because of its good electrical and mechanical property. These motors are now changing to brushed DC motors and induction motors in various applications. The main problem that occurs in the traditional PID controller is that the parameters obtained from the drive control system of the BLDC servo motor cannot produce a steady state under various operating conditions such as adjusted gain plus transient response and parameter variation, load disturbance. So a fuzzy controller provides good speed response for start-up. In this paper how the design and simulation of ANFIS controller is better for the performance of brushless DC motor (BLDC) servomotor as well as the design and implementation of ANFIS controller and its performance are compared is shown. The capability of BLDC servomotor based on Fuzzy, ANFIS and PID controller is investigated under different operating conditions. Paper represents the PID controller and fuzzy controller to demonstrate error tracking capability and usefulness of ANFIS controller in control applications. By using the PID, Fuzzy and ANFIS controller we can implement, analyze the overall performance of brushless DC motor. KEYWORDS: Proportional-Integral-Derivative (PID) controller, Brushless DC (BLDC) motor, Adaptive-Network- Based Fuzzy Inference System (ANFIS), (FLC) Fuzzy logic controller (IGBT) Insulated Gate Bipolar Transistor.

Strategy to reduce transient current of inverter-side on an average value model high voltage direct current using adaptive neuro-fuzzy inference system controller

Growing-up of high voltage direct current (HVDC) penetration into modern power systems (PS) makes difficulty on the PS operation. The HVDC produces high and slow transient current (TC) at start-time, especially for higher up-ramp rate (Urr>20 pu/s). Its condition makes the HVDC cannot be linked and synchronized into the PS rapidly. A strategy to reduce the TC is proposed by an adaptive network based fuzzy inference system (ANFIS) control on inverter HVDC-link to cope up this problem. The ANFIS control is tuned with the help of conventional control in various train-data by using offline mode. Response of ANFIS scheme is improved by suppressing TC at the values of 3.75% for the both phases (A and C), and 3.95% for phase B, for the Urr=30 pu/s. While the conventional control achieved at 9.1% for the both phases (A and B), and 9.2% for the phase C. The ANFIS control gives shorter settling time (0.553 s) than the conventional control (0.584 s) for all phases. The proposed control is more effective than the conventional control at all the scenario.

Improving The Controllability of Grid-Tie Inverter for Small-Scale Rooftop Photovoltaic System

This paper aims to apply an Adaptive Network-based Fuzzy Inference System (ANFIS) controller in a DC-link voltage regulator to decrease the power fluctuation for enhancing the performance of the grid-tie inverter for a small-scale rooftop solar Photovoltaic (PV) system. For tracking Maximum Power Point (MPP), a Perturb and Observe (P&O) algorithm was presented. To demonstrate the effectiveness of the proposed controllers for a small-scale 5 kWp rooftop solar PV system, several time-domain simulation results under a three-phase short-circuit fault that happened at a grid-tie inverter system was systematically performed by employing Matlab/ Simulink toolbox. On the basis of the simulation results, it is obvious that the proposed P&O algorithm for MPP tracking and the designed ANFIS controller for DC-link voltage regulator of the grid-tie inverter shows better performance to reduce overshoot and settling time in transient responses of the studied small-scale 5 kW grid-tie rooftop solar PV inverter under the most severe operating condition.

Application of An Adaptive Network-based Fuzzy Inference System to Control a Hybrid Solar and Wind Grid-Tie Inverter

In this paper, the application of an Adaptive Network-based Fuzzy Inference System (ANFIS) to control a hybrid solar and wind grid-tie inverter in order to reduce power oscillations and enhance power quality is presented. To extract the maximum power from the PV system, a Perturb and Observe (P&O) algorithm is presented that tracks the Maximum Power Point (MPP). Time-domain simulation results of the studied system are performed in MATLAB/SIMULINK under different operating conditions such as changing irradiation and short-circuit faults in the power grid. From the simulation results, it can be concluded that the designed ANFIS controller and the proposed P&O algorithm perform better than the traditional PI controller and improve transient responses under severe operating conditions.

Path optimization for multiple humanoid robot using TLBO based ANFIS controller in obscure environment

Humanoid robot NAO is an intelligent reprogrammable agent designed to execute desired work using wireless control system by sensing the working environment at the same time. This article concentrates on the analysis of hybrid intelligent navigation approaches for single and multiple humanoid NAOs. It describes the optimization of a collision-free path in a static and dynamic terrain. The hybridization introduced is designed in two steps. ANFIS (Adaptive Network-based Fuzzy Inference System) controller produces a transitional driving angle (TDA) using a robot location with respect to obstacle distance in all directions. It is fed to Teacher Learner based Optimization (TLBO) approach that produces optimum driving angle (ODA) for the humanoid robot to guide along with its predefined target. It optimizes the path selection by taking Euclidean distance and ODA as the key factor. The hybridized controller is examined in an environment that consists of single and multiple NAOs. Case of inter-collision may occur in the path planning of multiple humanoid NAOs. It is eliminated with the integration of the dining philosopher controller in the base algorithm to prioritized one robot towards the assigned target. Simulation results demonstrate the proposed controller's efficacy and explain that it gives optimized path length and travel time. In addition, a comparative study with previously applied effective algorithms is done.

Optimization of ANFIS controllers using improved ant colony to control an UAV trajectory tracking task

Development of unmanned aerial vehicles (UAVs) has become the most important research areas in the field of autonomous aeronautical control. This paper proposes a robust and intelligent controller based on adaptive-network-based fuzzy inference system (ANFIS) and improved ant colony optimization (IACO) to govern the behavior of a three degree of freedom quadrotor UAV. The quadrotor was chosen due to its simple mechanical structure; nevertheless, these types of aircraft are highly nonlinear. Intelligent control such as fuzzy logic is a suitable choice for controlling nonlinear systems. The ANFIS controller is used to reproduce the desired trajectory of the quadrotor in 2D Vertical plane and the IACO algorithm aims is to facilitate convergence to the ANFIS’s optimal parameters in order to reduce learning errors and improve the quality of the controller. To evaluate the performance of the proposed IACO tuned ANFIS controller, a comparison between the proposed ANFIS-IACO controller and other controller’s performance such us ANFIS only and proportional–integral–derivative controllers is illustrated using the same system. As expected, the hybrid ANFIS-IACO controller gives very satisfactory results than the others methods already developed in the same study.

Hybrid ANFIS-ant colony based optimisation for quadrotor trajectory tracking control

This study proposes a robust, accurate and intelligent control for trajectory tracking of a three degree of freedom quadrotor unmanned aerial vehicle (UAV). The controller is based on adaptive-network-based fuzzy inference system (ANFIS) and ant colony optimisation (ACO) algorithm. Intelligent control such as fuzzy logic is a suitable choice for controlling nonlinear systems. The ANFIS controller is used to reproduce the reference trajectory of the quadrotor in 2-D vertical plane. The ACO algorithm provides an automatic adjustment of ANFIS parameters in order to reduce learning errors and improve the quality of the controller. To evaluate the performance of the proposed ANFIS controller tuned by ACO, it is compared with ANFIS and proportional-integral-derivative (PID) controllers. As expected, the hybrid ANFIS-ACO controller gives more satisfactory results than the other methods already developed in the same study.

Utilization of an ANFIS-STSM Algorithm to Minimize Total Harmonic Distortion

Recently applications of second order super-twisting sliding mode (STSM) algorithm become more convenient in control of AC machines. The conventional technique, the capability of creating a high value of current or voltage. On the other hand, the STSMC algorithm popular in more applications. There are several types of intelligent techniques such as neural networks (NNs), adaptive network-based fuzzy inference system (ANFIS) and fuzzy logic controller (FLC). The ANFIS controller is very popular than other controllers because its control is very simple in comparison with others. The new technique of STSMC is utilized to reduce the harmonic distortion in direct torque control (DTC). Cat ANFIS-STSM algorithm is introduced as a new STSM algorithm to minimize harmonic distortion and electromagnetic torque ripples. The simulation results have been carried out using Matlab/Simulink software.

ACS algorithm tuned ANFIS-based controller for LFC in deregulated environment

Abstract In this paper, an artificial cooperative search (ACS) algorithm tuned adaptive network-based fuzzy inference system (ANFIS) controller for optimal gain tuning of load frequency control (LFC) operation in deregulated scenario has been offered. The conventional controllers for load frequency control operation are having fixed gain values intended for nominal operating conditions of the power system and they do not afford effective and efficient performance over a large range of operating scenarios in the deregulated environment. To progress the system performance to its near optimum for all probable operating circumstances of the power system, the controller gains have to be computed for the equivalent operating conditions by using the restructured parameters. For this intention, a controller based on an adaptive network-based fuzzy inference system seems to be the most excellent and valuable preference. The ANFIS is trained by off-line data obtained using a new optimization technique, artificial cooperative search optimization algorithm and the corresponding gains are updated in real-time as per the changing operating conditions. ACS is a swarm intelligence algorithm developed for solving numerical optimization problems. The swarm intelligence philosophy behind ACS algorithm is based on the migration of two artificial superorganisms as they biologically interact to achieve the global minimum value pertaining to the problem. To exhibit the competence and robustness of the projected ACS algorithm tuned ANFIS controller, the controller has been implemented on a two-area two-unit interconnected deregulated power system having one reheat unit and one non-reheat unit in each area. The simulation results exhibit the ability of the designed ACS algorithm tuned ANFIS controller for online LFC operation in deregulated environment.

ANFIS-based integral terminal sliding mode control for disturbed chaotic system

An adaptive network based fuzzy inference system (ANFIS)-based integral terminal sliding (ITS) mode (ANFISITS) control is developed in this paper to deal with the disturbed chaotic systems. This ANFISITS control system is composed of an ANFIS controller and an ITS controller. The ANFIS controller is designed to approach an ideal controller and the ITS controller is designed to compensate for the approximation error between the ANFIS controller and the ideal controller; meanwhile, the ANFISITS control can cope with the disturbances. In simulations, the chaotic system control is illustrated to verify this proposed ANFISITS control system. The simulation results of the proposed control method possess better performance and robustness compared with that of fuzzy sliding parallel distributed compensation (FS-PDC) control.

Stability Enhancement of a PMSG-Based Offshore Wind Farm Fed to a Multi-Machine System Through an LCC-HVDC Link

<?Pub Dtl=""?> Adaptive-network-based fuzzy inference system (ANFIS) is an adaptation and robustness method since it combines the advantages of artificial neural network (ANN) and fuzzy logic controller (FLC). Besides, ANFIS is also a nonlinear controller that can be used to improve stability of the studied system under different operating points. This paper presents the stability-improvement results of an offshore wind farm (OWF) fed to a multi-machine system through a high-voltage direct-current (HVDC) link based on line-commutated converter (LCC). An effective control scheme using a designed ANFIS damping controller at the inverter station of the HVDC link to achieve damping improvement of the studied system is proposed. A frequency-domain scheme based on eigenvalue and root-loci technique is carried out to compare the effectiveness of the proposed ANFIS control scheme and a modal-control designed PID damping controller. A time-domain scheme based on a nonlinear system model subject to a three-phase short-circuit fault is also utilized to examine the effectiveness of the proposed control scheme. Comparative simulation results show that the two damping controllers can contribute adequate damping characteristics to the dominant modes of the studied system while the designed ANFIS damping controller is shown to be superior for improving the stability of the studied system subject to a severe disturbance.

On Dynamics and Control of an Adaptable Wheeled Climbing Robot

This paper introduces a re-configurable wheeled climbing robot, which is capable of doing a multitude of tasks that no other single robot could do in the past. It can climb staircases, move inside empty ducts and pipes, climb up the ropes and poles of varying cross sections, and even jump over obstacles with proper motion coordination. It can also move inside narrow passageways by reconfiguring itself. In this paper, a comprehensive dynamic model of the robot is derived for the first time. A real-time simulator to test different control strategies by a human operator using conventional human-machine interfaces has been developed. This simulator can be employed to quickly size the electromechanical actuators and synthesize different control strategies.The data obtained was further used to design a human-analogous autonomous controller. A novel human-analogous control strategy based on an Adaptive Network-based Fuzzy Inference System (ANFIS) was implemented to control the position of the robot climbing a straight pole against gravity, based on data obtained from the real-time Human-In-The-Loop (HITL) simulator. Relevant input/output data were stored, filtered, and used offline to tune the parameters of an ANFIS-based controller. The ANFIS controller whose parameters were optimized was then implemented on the real system autonomously. Based on the information obtained via the HITL simulator system, the controller can extrapolate needed data for untrained cases. This control strategy provides some advantages over conventional controllers; for example, this avoids actuator saturation, minimizes the energy expenditure, and circumvents the gain-scheduling process.

Modeling a Paper-Making Wastewater Treatment Process by Means of an Adaptive Network-Based Fuzzy Inference System and Principal Component Analysis

In this paper, a predictive control system based on an adaptive network-based fuzzy inference system (ANFIS) was employed to develop models for predicting and controlling the performance of a paper-making wastewater treatment process. The system includes an ANFIS predictive model and an ANFIS controller. In order to improve the network performance, fuzzy subtractive clustering, euclidean distance clustering, and principal component analysis (PCA) were used to identify model architecture and extract and optimize the fuzzy rule of the model. For the developed predictive model, when predicting, mean absolute percentage error (MAPE) lay 6.06% adopting ANFIS, root mean square normalized error (RMSE) was 24.4485 and R was 0.9731. The control model, taking into account the difference between the predicted value of chemical oxygen demand (COD) and the set point, can effectively change the additive dosages. In order to verify the developed predictive control model, a paper-making wastewater treatment process was p...

Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system

This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order nonlinear hydrogenerator model.