Design Of Fuzzy Logic Controller Research Articles

A Novel Approach to a Recurrent Fuzzy Logic Controller Design Applied to Systems with Multiple Disturbances

In any process, control plays a vital role in the quality of the product. The influence of a controller's operation on a process is linked to its success or failure. However, using a controller that works properly presents several obstacles, such as non-linearity or the lack of a model that adequately represents the behavior of the process. Different approaches have been devised and studied to deal with these difficulties. One of the most promising and widely applied and studied in recent years is fuzzy control due to its ability to operate systems with no precise plant model. Herein, a set of rules and a fuzzy control structure are proposed, with fewer design parameters than a conventional fuzzy controller and better performance. The global stochastic optimization tool Differential Evolution is used to obtain the design parameters of the proposed fuzzy controller.

Fuzzy Logic Controller Design for Intelligent Lighting and Air-Conditioning Management Systems

In an effort to conserve energy and optimize the use of resources, this research explores the application of fuzzy logic control techniques to improve energy efficiency in intelligent lighting and air conditioning (AC) management systems. This research aims to investigate how fuzzy logic control strategies can be incorporated into intelligent systems to regulate lighting and air conditioning operations with greater precision, adaptability, and energy efficiency. By utilizing a fuzzy logic algorithm, this research develops a model that is able to dynamically adjust lighting levels and AC settings based on environmental conditions, housing patterns, and user preferences. Fuzzy logic control has proven useful for maintaining the desired level of comfort and optimizing electrical energy consumption through trials. The research results show that the integration of fuzzy logic control methodology offers significant potential to improve energy efficiency in lighting and air conditioning management systems, leading to reduced energy consumption and operational costs. These findings highlight the importance of intelligent control in the sustainable management of electrical equipment and provide valuable insights for the design and implementation of energy-efficient systems in a variety of contexts.

MIMO fuzzy logic controller design for temperature regulation of HVAC systems in cooling mode subjected to time-varying natural ventilation loads

MIMO fuzzy logic controller design for temperature regulation of HVAC systems in cooling mode subjected to time-varying natural ventilation loads

Self fuzzy-genetic approach for boosting photovoltaic efficiency

The generation of power in Photovoltaic systems is reduced when they operate far from their maximum power point. For optimal operation, it is essential to continuously track the maximum power point of the PV solar array. However, identifying the maximum power point is a challenge due to the nonlinear relationship of electrical characteristics of PV panels with external factors. To address this issue, we present a novel design approach for a self-organizing, self-tuning fuzzy logic controller, applied to the problem of maximum power point tracking in photovoltaic systems. We outline the basic structure of the fuzzy logic controller and address the design problems typically associated with conventional trial-and-error schemes. We also discuss the suitability of the genetic algorithm optimization technique for determining and optimizing the fuzzy logic controller design. In our proposed approach, we translate the normalization factors, membership function parameters, and controller policy into bit-strings, which are then processed by the genetic algorithm to find a near-optimal solution. To achieve high dynamic performance, we choose a particular objective function as a performance index. We compare our approach with two variants of the maximum power point algorithm, one based on genetic algorithms and the other based on fuzzy logic, as well as with the methods described in references [34] and [35], in order to evaluate its effectiveness.

Fuzzy Logic Controllers Design for the Path Tracking of an Autonomous Coaxial Octorotor

Due to the essential characteristics and utility of coaxial octocopter vehicles in various real-time applications and many actual domains, this article aims to propose intelligent fuzzy logic controllers (FLC) for a six-degrees-of-freedom coaxial octorotor with a dynamic model. Using expert knowledge, the developed control structure is applied to track reference paths in the 3D environment with sufficient stability and effectiveness. The tested octorotor dynamic model is divided into six subsystems: altitude, pitch, roll, yaw, x position, and y position. Meanwhile, a Mamdani fuzzy inference controller is designed and applied for each subsystem to generate the appropriate control action. The simulation and the evolution tasks are done using a MATLAB dynamic model of the controlled process in order to perform and evaluate the proposed intelligent control structures of the 6 DOF octorotor model. The illustrated simulation results demonstrate the designed fuzzy controllers' effectiveness in motion stability and error tracking for different types of paths and all tested variables.

Fuzzy Logic Speed Regulator for D.C. Motor Tuning

A D.C. motor's rotational speed is regulated in this study using a PID controller and a fuzzy logic controller. In contrast to the fuzzy logic controller, which uses rules based on knowledge and experience, the proportional-integral-derivative (PID) controller requires a mathematical system model. This study investigates the regulation of a DC motor's velocity using PID and fuzzy logic controllers. The PID controller utilizes a mathematical model and parameter tuning by trial and error. Still, the fuzzy logic controller (FLC) operates on rule-based knowledge, enabling it to handle the nonlinear features of the DC motor effectively. The FLC design entails intricate determinations, including the establishment of a rule base and the process of fuzzification. A total of 49 fuzzy rules have been devised to achieve precise control. Based on MATLAB/SIMULINK simulations, the study concludes that the Fuzzy Logic Controller (FLC) beats the Proportional-Integral-Derivative (PID) controller. The FLC exhibits superior transient and steady-state responses, shorter response times, reduced steady-state errors, and higher precision. This study emphasizes the efficacy of the FLC (Fuzzy Logic Controller) in dealing with the difficulties associated with DC motor control. It presents a strong argument for the suitability and efficiency of FLCs in industrial environments compared to conventional PID (Proportional-Integral-Derivative) controllers. There are a wide variety of ways to construct a fuzzy logic controller. The speed error and the rate of change in the speed error are two inputs to the FLC. Defuzzification is done by focusing on the core of the problem. The results show that FLC is superior to PID controllers in efficiency and effectiveness due to its reduced transient and steady-state factors.

A Study on Kinematics, Dynamics, and Fuzzy Logic Controller Design for Remotely Operated Vehicles

A Study on Kinematics, Dynamics, and Fuzzy Logic Controller Design for Remotely Operated Vehicles

Fuzzy Logic Controller Design for Smart Watering System of Rose Cultivation

The traditional methods of rose cultivation often rely on manual irrigation practices, which may not always be precise or efficient. Additionally, the cost associated with implementing automated irrigation systems has been a limiting factor for many farmers. This research addresses these challenges by exploring the integration of Fuzzy Logic Controller (FLC) technology and low-cost electronic devices to develop an automated irrigation system tailored for rose cultivation, aiming to enhance precision and accessibility in agricultural practices. The study demonstrates the effectiveness of this approach in optimizing watering practices, showcasing a notable level of accuracy in providing irrigation recommendations. Moreover, the implementation of low-cost electronic devices enhances the accessibility and feasibility of such smart irrigation systems. The research lays a foundation for advancements in precision agriculture, particularly in the domain of rose cultivation, with potential implications for broader agricultural practices.

Implementation of Fuzzy Logic Controller Algorithms with MF optimization on FPGA

In this work, we propose the design and implementation of a parallel-structured fuzzy logic controller with integral action and anti-windup. The Grey Wolf Optimization (GWO) optimization technique is used to optimize fuzzy rules, which allows for the complicated algebraic ideas of type 1 fuzzy logic algorithms to be reduced to straightforward numerical equations for FPGA target implementation. The techniques for operating a geared DC motor are optimized by the membership function structure of our controller's data propagation. Our proposed controller was implemented in Xilinx System Generator (XSG) and co-simulated on hardware and software with VIVADO and XSG tools.

Design of Fuzzy Logic Controller for Inductor Based Cell Balancing in Battery Management System

Lithium-ion batteries are a popular energy storage used in various applications including electronic devices, electric vehicles, renewable energy systems, and microgrids. The limitations in capacity and voltage of individual battery cells necessitate arranging them in series and parallel configurations to meet the energy demands of the system. However, connecting cells in series can potentially lead to charge imbalance among cells, resulting in reduced battery pack capacity and triggering safety concerns. Numerous topologies and control methods for battery cell balancing systems have been explored in previous research. In this study, two fuzzy logic controllers with different membership function shapes are designed to drive the duty cycle of PWM signals for inductor-based cell to cell balancing. Based on simulation results, the designed controllers demonstrate good performance compared to conventional methods.

Multiobjective Reliability-Based Design Optimization of the Fuzzy Logic Controller for MR Damper-Based Structures

To devise an optimum and robust fuzzy logic controller for MR damper-based structures subjected to earthquake ground motions, the multiobjective reliability-based design optimization (RBDO) using the adaptive Kriging model is performed to determine the parameters of the fuzzy logic controller. The optimization problem is formulated with two objective functions, namely, the minimization of interstory drift and average control force of the concerned structure, and subjected to a probability constraint on structural dynamic responses under the effects of random structural stiffness and stochastic earthquake loadings. To reduce the computational cost of reliability assessment, a global Kriging model is constructed in an augmented space as a surrogate for computational evaluations. Subsequently, the trained metamodel combined with the nondominated sorting genetic algorithm (NSGA-II) is integrated into the framework of RBDO for solving the fuzzy logic control (FLC) optimization problem. The feasibility and effectiveness of the multiobjective RBDO in the FLC design are finally validated by conducting numerical simulations on both linear and nonlinear structures. As demonstrated in the linear case, the fuzzy logic controllers obtained from the multiobjective RBDO show more robustness than those derived from the multiobjective deterministic design optimization (DDO). In the nonlinear case, using the multiobjective DDO to prelocate a coarse safety domain can significantly reduce the number of samples for training the metamodel and facilitate the implementation of the multiobjective RBDO; in addition, the controlled structural performance with a specified fuzzy logic controller can be further improved by considering MR damper distribution optimization.

Design and development of brain emotional learning based adaptive membership functions for type-2 fuzzy systems

This paper presents the design and development of Brain Emotional Learning based adaptive Type-2 Fuzzy Systems for control of dynamical systems. The BEL controller belongs to the class of bio inspired controllers, as its architecture is based on limbic system of human brain and is capable of providing solutions for complex real time problems. In this work, dynamics of Brain Emotional Learning are used for the adaptation of membership functions in the design of Type-2 Fuzzy Logic Controllers. The stability of the overall system is analysed through Lyapunov Yakubovich’s criteria. The proposed approach is validated on the benchmark system such as inverted pendulum, CSTR and Ship heading control through simulation and in real-time environment using OPAL RT OP5600.

Two-Layer Parallel Fuzzy Logic Controller Design for Semiactive Suspension System with a Full Car Model

This paper presents research results on semiactive suspension using a fuzzy logic controller. Firstly, a full car model is built with 7 degrees of freedom using a semiactive suspension system at all four wheels. The dynamic equations are converted to the state space representation to facilitate control design and improve accuracy in simulation design. Then, a two-layer parallel fuzzy logic controller is designed with 49 and 25 rules for two fuzzy inference systems. In this way, the fuzzy logic controller is able to respond to a wide operating range of the system and the output force is smoother. Finally, the evaluation results are performed in the frequency and time domains with a real random road profile, and they showed that the root mean square value of the signals when using a semiactive suspension system with the proposed fuzzy logic controller decreased by over 25% compared to the passive suspension system. This clearly demonstrates the effectiveness of the proposed controller in improving the ride comfort, the road holding and ensuring the suspension travel of cars.

Design of a novel mixed interval type-2 fuzzy logic controller for 2-DOF robot manipulator with payload

This paper investigates a novel coupling-based mixed interval type-2 fuzzy logic controller (MIT2FLC) for trajectory tracking problems of highly nonlinear and complex robot manipulator plants. The major impediment for these types of plants is coupling between the robotic links at the time of operation. In addition, the performances of these plants are adversely affected by parameter uncertainties, random noise and external disturbances. Therefore, the MIT2FLC approach with an additional degree of freedom in the membership functions is proposed to efficiently deal with the problem of uncertainty and provide robust performance. The Grey wolf optimization (GWO) is implemented to calculate the optimal parameters of the proposed controller. For checking the performance, the MIT2FLC approach is compared with its type-1 fuzzy counterparts, namely mixed type-1 fuzzy logic controller (MT1FLC), type-1 fuzzy logic controller (T1FLC) and PID controllers. Robustness analysis of the proposed controller is investigated for external disturbances, varying system parameters, and random noise.

Simulation and Modeling of Different Types of MPPT Techniques Based on PV System

This paper endeavored to analyze the performance of several maximum power point tracking (MPPT) strategies for use with PV installations. Methods like "perturb and observe," "incremental conductance," and "fuzzy logic controller" are evaluated here. Using the simulation program MATLAB/Simulink, we modeled a PV module and DC/DC boost converter and tested them with a variety of MPPT implementations. This research examines three distinct methods for calculating MPPT output: the traditional Perturb and Observe approach, the incremental conductance method, and the fuzzy logic controller. It is evident that when compared to the conventional P&O controller and the incremental conductance approach, the tracking speed achieved by the fuzzy logic controller is more consistent and faster. However, P&O controller's operating point keeps changing around the maximum power point even in steady state operation, which is a major drawback. In this study, we focus on the fuzzy logic controller design and contrast it with the incremental conductance controller and the P&O controller. This algorithm was used to record the daily peak power use. In this setup, a DC-DC converter and fuzzy logic controller work together to keep the PV system's output power at its maximum. MATLAB/SIMULINK is used for all simulations.

LQR+I and Fuzzy Logic Controller Design for DC Motor Position Control

Sabit mıknatıslı Doğru Akım (DA) motorları kontrol eğitimi uygulamalarında sıklıkla tercih edilmektedir. Kapalı çevrim DA motor kontrol uygulamaları için farklı birçok metot geliştirilmiştir. Geliştirilen metotların en önemli hedefi kontrolcü performansında yüksek başarım elde edebilmektir. Bu çalışmada, bir DA motorun açısal konum kontrolü, integratör ilave edilmiş doğrusal karesel regülatör (LQR+I) ve bulanık mantık kontrol (FLC) yöntemleri kullanılarak tasarlanmış ve öncelikle simülasyon ortamında daha sonra gerçek zamanda başarılı bir şekilde test edilmiştir. Model tabanlı LQR ve model tabanlı olmayan FLC kontrol yöntemleri deneysel bir sistem üzerinde uygulanmış ve elde edilen sonuçlar karşılaştırılmıştır. Kontrol algoritması hızlı kontrol prototipleme tekniği ile Waijung kütüphanesi kullanılarak hazırlanmış ve STM32F4 Discovery kiti aracılığıyla sistemde testler gerçekleştirilmiştir. Kontrolcü performanslarının daha net ortaya çıkması için gerçek zamanlı farklı referans sinyalleri sisteme uygulanmış, bu sinyaller sonucunda ise her iki kontrolcü de başarılı sonuçlar elde etmiştir.

Enhanced Control Designs to Abate Frequency Oscillations in Compensated Power System

The interconnection of transmission, distribution, and generation lines has established a structure for the power system that is intricate. Uncertainties in the active power flow are caused by changes in load and a growing dependence on renewable energy sources. The study presented in this paper employs several controlling strategies to reduce frequency variations in series-compensated two-area power systems. Future power systems will require the incorporation of flexible AC transmission system (FACTS) devices, since the necessity for compensation in the power system is unavoidable. Therefore, a static synchronous series compensator (SSSC) is installed in both areas of our study to make it realistic and futuristic. This makes it easier to comprehend how series compensation works in a load–frequency model. With the integration of electrical vehicles (EVs) and solar photovoltaic (PV) systems, several control strategies are presented to reduce the frequency oscillations in this power system. Particle swarm optimization (PSO) is used to obtain the best PI control. To improve results, this work also covers the design of fuzzy logic control. In addition, the adoption of neural network control architecture is proposed for even better outcomes. The outcomes clearly show how well the proposed control techniques succeeded.

Design of type-2 fuzzy logic controller in a smart home energy management system with a combination of renewable energy and an electric vehicle

Home energy management is one of the most important parts of a smart home that manages the efficient use of energy in the smart home. This paper aims to design two type-2 fuzzy logic controllers in the demand-side energy management system. For this purpose, a combination of renewable energy sources, such as fuel cells, photovoltaic solar panels, vertical axis wind turbines with a helical savonius rotor, and an electric vehicle and energy storage system along with an external grid are utilized to supply the electricity usage of home appliances in a smart home. By using the proposed method, higher quality, more economical, and environmentally friendly energy can be accomplished. To this end, the outputs of the proposed controllers are defined to make appropriate decisions about the generated energy, how to supply energy for own consumption, and controllable loads. The number and distribution of membership functions of type-2 fuzzy logic controllers are selected according to the measured real input data over a year in Tehran, Iran. To demonstrate the effectiveness of the proposed method it is simulated by MATLAB/Simulink software, which indicates that the proposed system causes the smart home to receive 49.186 kWh less electricity energy from the grid to supply daily power for home appliances and therefore can weekly consume approximately 343.95 kWh less electricity energy from the grid. it is shown that after applying the proposed strategy, electricity costs were reduced by 71.5%, and the peak-to-average ratio was reduced by 64.6%.

Design of LQR-based FLC for the Optimal Regenerative Braking Controlling of Solar PV-based Electric Vehicle System

The article presents an analysis of the optimal control of speed and regenerative braking in an electric vehicle (EV). The presence of the continuous generating power is one of the major challenges for battery charging of electric vehicle. In order to meet the demand of continuous power, electric vehicles are driven by solar PV arrays. Initially, maximum power extraction (MPPT) is assessed from a solar photovoltaic (PV) array using the P&O method. The outputs of the solar PV array are integrated with electric vehicles. Such integration creates the problem of maintaining the power quality. The existing methods also address the issue of optimal speed and torque control with high transients and more settling time. In this article, an electric vehicle is modeled first and then operated in regenerative braking mode. To address the shortcomings of existing methods, the linear quadratic regulator (LQR) method is used; then, fuzzy logic controller (FLC)-based LQR is used for optimal speed and torque control during electric vehicle regenerative braking. In terms of reduced settling time and improved peak overshoot, optimal speed and regenerative torque of electric vehicles are better realized with LQR-based FLCs than with LQR controllers. In addition to this, stopping speed characteristics with various electrical parameters (voltage, current, and flux) have been analyzed with different methods in which LQR-based FLC shows its dominance to attain zero speed in regenerative mode with more sharp characteristics.

Design of PID, FLC and Sliding Mode Controller for 2-DOF Robotic Manipulator: A Comparative Study

Controlling the manipulators in a precise manner is a challenging task. To overcome this difficulty around the world, many researchers have developed various control algorithms but are not providing optimal results. To obtain the optimal results in the current research the authors designed a proportional, integral, and derivative (PID) controller, fuzzy logic controller (FLC), and sliding mode controller (SMC) for a 2-DOF manipulator. The concept of forward and inverse kinematics was initially solved after assigning the D-H parameters for each joint. The purpose of forward or direct kinematics is to obtain the position and orientation of the end effector. Further, the concept of inverse kinematics is used to estimate the joint angles. Later on, the Lagrange-Euler formulation was used to calculate the dynamics of the 2-DOF manipulator, which is required to estimate the torque required for each joint of the robotics arm. The main goal of this research problem is to optimize the angular error between the two successive events. Finally, the developed algorithm is compared with the existing algorithms such as PID and Fuzzy logic controller.