Fuzzy Logic Controller In Terms Research Articles

A Fuzzy Logic-Based Tuning Model in an Indoor Lighting System for Energy and Visual Comfort Management

This paper proposes a fuzzy logic-based tuning model (FLTM) for daylight-linked control of the lighting system in an office room. The proposed FLTM considered a new method of dimming levels of light-emitting diode (LED) luminaires updating process to improve the performance of the developed fuzzy logic controller (FLC) in terms of energy consumption and visual comfort metric and, at the same time, fully complies with the European Standard EN 12464-1. The artificial lighting system and daylight simulation were carried out using DIALux to model artificial lighting and daylight illuminance levels matrices. The proposed FLTM was developed and simulated using MATLAB and validated and compared with other controllers, including developed FLC and artificial neural network (ANN) based control. The simulation results showed that the proposed FLTM successfully improved the performance of developed FLC in terms of a fully satisfied visual comfort set-point. It also attained higher energy savings of 2% than ANN and achieved the closest to preset visual comfort compared with other controllers. Moreover, the proposed method consumes less computational effort, and it is easy to integrate with developed FLC and daylight-linked control of the lighting system.

Modeling and Trajectory Tracking Control for a Multi-Section Continuum Manipulator

This paper presents a dynamic modeling and trajectory tracking control design for a hyper-redundant continuum manipulator (HRM), which is able to continuously bend along its length. The HRM has the advantages of high flexibility, large workspace, and low inertia, but also poses challenges of complex kinematics, and dynamic coupling. To address these challenges, the kinematics model was developed based on the piecewise constant curvature (PCC) assumption that captures the relationship between the cable lengths, joint angles, and end-effector pose. Inverse kinematics is presented geometrically by solving trigonometric equations. The dynamic model for the continuum robot (CR) is derived using the Euler–Lagrange representation, which incorporates the effects of gravity and elasticity. Accordingly, three control strategies are developed and applied to a two-section continuum robot, which are the inverse dynamic proportional integral derivative controller (PID), fuzzy logic controller (FLC), and sliding mode controller (SMC). We validate our proposed methods through simulations on various 3D trajectories, utilized MATLAB symbolic math toolbox in conjunction with Simulink simulation; demonstrate and evaluate different close-loop dynamic control responses for different scenarios. This simulation used in the creation of a unique animated simulation for a whole 3D continuum robot while tracking its desired trajectories. Finally, the simulation results demonstrate that our proposed methods can effectively control the HRM with high tracking performance, exhibiting enhanced responses in terms of settling time, rising time, and steady-state error. While the developed SMC outperforms Poth's PID and FLC in terms of robustness and settling time (51% and 10.3%), respectively.

Design of a Saving-Energy Fuzzy Logic Controller for a Differential Drive Robot Based on an Optimization

In the problem of designing a Fuzzy Logic Controller (FLC) for robots in general and a Differential Drive Robot (DDR) in particular, the determination of the parameters of Membership Functions (MFs) and Fuzzy Rules (FRs) is a difficult, complicated, and time-consuming problem because this is mainly based on heuristics and the knowledge of experts. Therefore, this paper provides a new method to efficiently design an FLC for the DDR by using the Genetic Algorithm (GA). Here, the GA is used to generate and optimize both the parameters of MFs and the FRs according to the minimum kinetic energy loss criterion. For this purpose, a program is created in Google Colab® by using the Python language with the help of the “Pymoo” library to not only automatically generate all the suboptimal parameters of MFs and the suboptimal FRs but also the simulate and evaluate different used FLCs. This program is published as an open-source code so that all readers can browse, view, run, and modify the code themselves to design their FLC. The simulation results have shown that the designed FLC is much better than other used FLCs in terms of the minimum kinetic energy loss while other control performances are still good.

Comparative Functional Analysis of Three MPPT Techniques Applied on a Stand-Alone Photovoltaic System with a Charging Battery

This paper presents a comparative functional analysis of a typical stand-alone photovoltaic system (SPV) with a charging battery. Considering the nonlinear behavior of the PV system, a specific methodology based on Tayler’s expansion series is opted for the modelling, control, and optimization. During the proposed steps, a practical PID controller is designed using a dynamic state space averaging approach. This controller is used to track the maximum power point (MPP) and control the battery charging process. To check the validity of this controller, it is applied with three disparate MPPT algorithms to enhance system performances by preventing drift phenomenon against fast changing in cell temperature and solar irradiance, and control the battery pack to conform with the load rating voltage. A typical 2525 W SPV is simulated in short term to capture fast dynamics transitional details. By comparison with practical manufacturer's specifications of the PV panel, the load, and the battery pack; simulation results show particularly, good performances with fuzzy logic controller in terms of speed tracking, MPPT tracking accuracy, voltage quality, and reducing transient fluctuations. The findings of this research substantiate its efficacy, which may serve as a prototype study for the design and realization of stand-alone photovoltaic systems with energy storage.

Impact of Sampling in the Operation of Vehicle to Grid and Its Mitigation

Governments across the world have plans for a deep penetration of electric vehicles in the near future, for the transportation sector. This will require robust communications between the entities of the charging infrastructure, viz., the grid, aggregators, controllers, charging stations (CSs) and the electric vehicles (EVs). For analyzing the vehicle-to-grid and grid-to-vehicle infrastructure, it is important to model the electrical as well as the communication network together. This will help in determining the influences of the communication network in the operation of the controller and grid. In this paper, a distribution system with five CSs and sampled data transmission between the entities are modeled and simulated in MATLAB Simulink for understanding the potential impact of the networked communication system on the grid operation. A fuzzy-logic controller (FLC) is used in the model. The performance of the FLC in terms of root mean square values is found to improve when the inputs (node voltage and total energy available in CSs) are synchronized at faster sampling rates. Furthermore, it is shown that the performance of the FLC can be improved by bringing about changes in it (increasing the number of membership functions) and not merely synchronizing at a faster sampling rate.

Simplified fuzzy ‐Padé controller for attitude control of quadrotor helicopters

In this study, a simplified fuzzy-Pade controller (FPC) for attitude control of quadrotor helicopters is proposed, which is non-linear, fractional, and model independent. To determine some of the unknown coefficients of the Pade approximant, a proportional-derivative-type fuzzy logic controller (FLC) is first designed. The responses of the simplified FPC due to step set-point manoeuvres are compared with those of the FLC in terms of several performance indices such as rise time, settling time, percentage overshoot, integral absolute error, integral of time multiplied by AE, central processing unit time, and energy consumption. Simulation results demonstrate that in each case the proposed FPC presents an improved performance over the FLC. The asymptotic stability of the simplified FPC is proved with Lyapunov stability theory and La Salle's invariant set theorem. Unlike the FLC, the proposed FPC is simple enough to be implemented on the on-board electronics of the quadrotor. Flight experiments in hovering conditions are given to demonstrate the effectiveness of the simplified FPC.

Novel hybrid fuzzy-PID control scheme for air supply in PEM fuel-cell-based systems

This paper proposes a novel hybrid fuzzy-PID controller for air supply on Proton Exchange Membrane fuel cell (PEMFC) systems. The control objective is to adjust the oxygen excess ratio at a given setpoint in order to prevent oxygen starvation and damage of the fuel-cell stack. The proposed control scheme consists of three parts: a fuzzy logic controller (FLC), a fuzzy-based self-tuned PID (FSTPID) controller and a fuzzy selector. Depending on the value of the error between the current value of oxygen excess ratio and its setpoint value, the fuzzy selector decides which controller should play the greatest effect on the control system. The performance of the proposed control strategy is analysed through simulations for different load variations and for parameter uncertainties. The results show that the novel hybrid fuzzy-PID controller performs significantly better than the classical PID controller and the FLC in terms of several key performance indices such as the Integral Squared Error (ISE), the Integral Absolute Error (IAE) and the Integral Time-weighted Absolute Error (ITAE), as well as the overshoot, settling and rise time for the closed-loop control system.

A genetic algorithm‐optimized fuzzy logic controller to avoid rear‐end collisions

SummaryIn this paper, a rear‐end collision control model is proposed using the fuzzy logic control scheme. Through detailed analysis of car‐following cases, our fuzzy control system is established with reasonable control rules. Furthermore, a genetic algorithm is introduced into the fuzzy rules refining process to reduce the computational complexity while maintaining accuracy. Numerical results indicate that our genetic algorithm‐optimized fuzzy logic controller outperforms the traditional fuzzy logic controller in terms of better safety guarantee and higher traffic efficiency. Copyright © 2016 John Wiley & Sons, Ltd.

A Rear-End Collision Avoidance Scheme for Intelligent Transportation System

In this paper, a rear-end collision control model is proposed using the fuzzy logic control scheme for the autonomous or cruising vehicles in Intelligent Transportation Systems (ITSs). Through detailed analysis of the car-following cases, our controller is established on some reasonable control rules. In addition, to refine the initialized fuzzy rules considering characteristics of the rear-end collisions, the genetic algorithm is introduced to reduce the computational complexity while maintaining accuracy. Numerical results indicate that our Genetic algorithm-optimized Fuzzy Logic Controller (GFLC) outperforms the traditional fuzzy logic controller in terms of better safety guarantee and higher traffic efficiency.

Optimized and Self-Organized Fuzzy Logic Controller for pH Neutralization Process

To conform to strict environmental safety regulations, pH control is used in many industrial applications. For this purpose modern process industries are increasingly relying on intelligent and adaptive control strategies. On one hand intelligent control strategies try to imitate human way of thinking and decision making using artificial intelligence (AI) based techniques such as fuzzy logic whereas on the other hand adaptive mechanism ensures adjusting of the controller parameters. A self-organized fuzzy logic controller (SOFLC) is intelligent in nature and adapts its performance to meet the figure of merit. This paper presents an optimized SOFLC for pH control using performance correction table. The fuzzy adaptation mechanism basically involves a penalty for the output membership functions if the controller performance is poor. The evolutionary genetic algorithm (GA) is used for optimization of input-output scaling factors of the conventional fuzzy logic controller (FLC) as well as elements of the fuzzy performance correction table. The resulting optimized SOFLC is compared with optimized FLC for servo and regulatory control. Comparison indicate superior performance of SOFLC over FLC in terms of much reduced integral of squared error (ISE), maximum overshoot and undershoot, and increased speed of response.

An accurate and cost-effective fuzzy logic controller with a fast searching of moment equilibrium point

This paper proposes a cost-effective architecture of fuzzy logic controller (FLC) that works in an accurate and fast manner. The accuracy of the proposed FLC is obtained by using the center of gravity (COG) defuzzifier that considers both membership values and spans of membership functions in calculating a crisp value. The cost effectiveness of the proposed FLC is obtained by restructuring the conventionally simplified FLC in the following ways. Firstly, the MAX-MIN inference is replaced by a read-modify-write operation that can be implemented economically in the structure of register files. Secondly, the division in the COG defuzzifier is avoided by finding the moment equilibrium point. The proposed COG defuzzifier has two disadvantages, in that it requires additional multipliers and it takes a lot of computation time to find the moment equilibrium point. The first disadvantage is overcome by replacing the multipliers with stochastic AND operations and the second disadvantage is alleviated by using a coarse-to-fine searching algorithm. The hardware complexity of the proposed FLC is analyzed in terms of the number of building blocks. Simulation of the proposed FLC to the truck backer-upper control problem is performed in VHDL, and the control performance of the proposed FLC is compared with that of the conventionally simplified FLC in terms of average tracing distance.

A novel fuzzy state feedback controller for power system stabilization

A novel fuzzy logic controller (FLC) is developed for damping electromechanical modes of oscillations and enhancing power system synchronous stability. The proposed controller is based on state feedback control system. The input signals to the controller are the weighted sum of the mechanical states and the electrical states weighted sum, while its output signal is added to the conventional fixed-gain PI controller output signal to give the excitation control signal. The simulation results using the proposed controller are carried out on an synchronous machine infinite bus system. Moreover, a comparison between the conventional fixed-gain PI controller and the proposed FLC is presented. The results validate the effectiveness of the proposed FLC in terms of less overshoot/undershoot and enhancing the power system stability over a wide range variation of operating conditions.