Parameters Of Fuzzy Logic Controller Research Articles

Development of Fuzzy Logic Controller in Automatic Vehicle Navigation using IoT

The integration of fuzzy logic controllers in automatic vehicle navigation systems represents a significant advancement in intelligent transportation systems, especially when paired with Internet of Things (IoT) functionalities and optimized through genetic algorithms. This innovative fusion harnesses the precision of fuzzy logic, the connectivity of IoT, and the optimization capabilities of genetic algorithms to transform automatic vehicle navigation. Fuzzy logic controllers excel in managing uncertainty and imprecision, providing decision-making capabilities akin to human reasoning. By simultaneously assessing multiple inputs and determining actions based on degrees of truth, fuzzy logic enables safe and efficient navigation in dynamic driving environments with fluctuating variables like obstacle proximity and traffic flow. IoT integration enhances navigation systems by enabling real-time data collection and sharing among vehicles and infrastructure, fostering adaptive route planning and improving the overall navigation experience. Genetic algorithms further optimize system performance by iteratively adjusting fuzzy logic controller parameters, ensuring efficient decision-making tailored to specific performance criteria such as travel time and fuel consumption. This collaborative integration of fuzzy logic controllers, IoT, and genetic algorithms offers a holistic solution to the challenges of automatic vehicle navigation, enhancing safety, efficiency, and adaptability in complex driving scenarios. Beyond enhancing individual vehicle performance, this approach contributes to overall transportation system efficiency and safety by mitigating traffic congestion, reducing emissions, and minimizing accidents. Consequently, these integrated systems address crucial societal challenges and pave the way for widespread adoption of autonomous vehicles in the future.

Unified fuzzy logic controller and power management for an isolated residential hybrid PV/diesel/battery energy system

Abstract Hybrid systems based on renewable energies for the electrification of remote sites controlled by power management systems (PMSs) aim to reduce fossil fuels and increase the efficiency of renewable energy sources to minimize greenhouse gas emissions. The influential role of the PMS contributes to improving the efficiency and effectiveness of these systems by ensuring a balance between the different sources and loads in all operating modes. However, the abrupt transitions between the various operational modes selected by the PMS generate power loss and imbalance. To handle this issue, a fuzzy logic controller (FLC)-based PMS controlling a photovoltaic (PV) and diesel hybrid system with a battery storage element connected to a DC bus is proposed in this paper. The proposed PMS is wholly based on FLC to ensure a smooth transition between the different modes of the system. The success of using the suggested PMS lies in how well the FLC parameters are chosen before the system is processed. For this purpose, the particle swarm optimization algorithm is adapted to tune the FLC parameters. The resulting optimal intelligent PMS is tested and compared with a classical one using comprehensive simulations performed in a Simscape ElectricalTM MATLAB® environment. The obtained results show an overshoot attenuation at the DC-bus voltage of 2% when changing the mode and an improvement in the PV generator efficiency by 99.5%.

Performance analysis of a robust adaptive fuzzy logic controller for wind turbine power limitation

Power limitation and load reduction are considered the main challenges for the large scale wind turbine when the system operates in the above rated wind speeds. The controller design still a complex and critical task in the presence of the system nonlinearities and the wind turbulent behavior to produce a cleaner energy without switching harmonics. In this context, an adaptive controller based on fuzzy logic concept is proposed to limit the extracted power at rated value with improving its quality and to mitigate the loads applied on the turbine and the drive train in full loads conditions by adjusting the pitch angle. First, the different parts of the DFIG wind system are modeled. Thereafter, the main control structures in different operating regions are briefly described. The second part is focused on the pitch control technique. The initial parameters of fuzzy logic controller (FLC) are defined based on literature studies. Then, those parameters are tuned to derive the optimized FLC. Finally, the scaling factors of this last configuration are automatically adjusted by means of a correction factor to develop the Adaptive FLC. By the end, a technical and economic comparative studies are conducted between the three fuzzy controller configurations and the classical structures which are classical PI controller and Gain scheduled PI controller using several performance indicators. The responses assessment obtained from virtual simulation using MATLAB/SIMULINK show that the adaptive FLC is the best solution for the pitch control in terms of rapidity, stability and robustness and allow producing higher electrical energy with lowest harmonics rate.

Optimization of fuzzy logic controller parameters using modern meta-heuristic algorithm for gantry crane system (GCS)

Robust control of underactuated nonlinear systems is a challenging task using conventional methods because of uncertainties which may lead to failure especially for mechanical and robotic applications. Fuzzy logic controller (FLC) is one of the methods specifically used for designing a robust controller that is able to deal with nonlinearity and achieve satisfactory performance for control applications. FLC uses heuristic information or expert operator knowledge to create its mechanism. However, nonlinearities exist in the control systems make its rule-based FLC design a non-trivial task. Manual trial and error tuning method is common way used for tuning the parameters of FLC to achieve the desired performance of the system. Hence, an automatic tuning using optimization algorithm is necessary. Meta-heuristic optimization algorithms are applied to tune the parameters of FLC automatically. In this paper, three popular meta-heuristic algorithms are used to optimize the scaling factors of FLC, i.e. Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Differential Evolution (DE) to improve the effectiveness of FLC design. A gantry crane system, which is a nonlinear system, is used as test system for this project. The simulation of the optimization of FLC parameters by using meta-heuristics algorithms was successfully achieved. The simulation results show that the optimization techniques for FLC are effective to improve the performance of conventional FLC for gantry crane control, i.e. position control and anti-swing control. In addition, PSO performs better than CS and DE in optimization of FLC for gantry crane system.

CANFIS‐Based Semi‐active Vibration Control of Stochastically Excited High‐Rise Civil Engineering Structures with Nonlinearities and Uncertainties

AbstractThis study introduces a co‐active neuro‐fuzzy inference system (CANFIS) based vibration control strategy for high‐rise civil engineering structures, such as buildings, wind turbines and towers. The proposed approach uses semi‐active dampers, which are distributed in the structure. Attached to the structural members, semi‐active dampers generate adaptive restoring forces to reduce the structural vibrations by autonomously tuning their parameters. The nonlinear behavior and the uncertainties involved in the mathematical models of both the semi‐active dampers and the structures require sophisticated control strategies. Previous studies have shown that fuzzy logic controllers (FLCs) can overcome these challenges and perform effectively with semi‐active dampers to mitigate the structural vibrations. Nevertheless, FLCs require a predefined parameter tuning of their fuzzy rules and membership functions, which must be performed based on the knowledge of a human expert. Combining FLCs with artificial neural networks (ANNs), hybrid systems are proposed, which can determine the parameters of FLCs by using learning based training and adaptation strategies of ANNs. However, there is only limited research investigating the performance of these adaptive systems in the field of vibration control of civil engineering structures. The present work is concerned with a numerical formulation of such an adaptive system, in particular the CANFIS, and its implementation on a non‐linear seismically excited three‐story steel benchmark building with semi‐active dampers. To determine the premise parameters of the membership functions and the consequent parameters of the fuzzy rules a hybrid training method is performed using the recursive least‐square algorithm and the steepest descent method. Investigations with historic earthquakes show a significant reduction of the roof displacements of the building. For instance, during the Hachinohe earthquake the peak value of the displacement is reduced by 87 %.

An interval type-2 fuzzy logic controller design method for hydraulic actuators of a human-like robot by using improved drone squadron optimization

Hydraulic actuator becomes an increasingly concerned driver for human-like robots. However, its dynamic performance under the control should be still further improved because hydraulic system is a typical nonlinearity system. Interval type-2 fuzzy logic controller is an advanced control method featured with high performance to deal with uncertain and nonlinear dynamics, so designing an interval type-2 fuzzy logic controller for the control of hydraulic is a feasible method. In this article, an improved drone squadron optimization-based approach is proposed to optimize interval type-2 fuzzy logic controller parameters. To verify the feasibility and priority of improved drone squadron optimization, a comparison on three different typical plants including proportional-derivative (PD) system, proportional-integral (PI) system, and PI nonlinear system between improved drone squadron optimization and other meta-heuristic algorithms is carried out. Simulation results demonstrate that improved drone squadron optimization not only gets an appropriate interval type-2 fuzzy logic controller for system control but also outperforms other popular algorithms in accuracy of performance.

К ВОПРОСУ ОБ ЭФФЕКТИВНОСТИ ПРИМЕНЕНИЯ НЕЧЕТКОЙ ЛОГИКИ В СИСТЕМАХ УПРАВЛЕНИЯ ИСПОЛНИТЕЛЬНЫМИ ГИДРОПРИВОДАМИ

The paper considers the possibility to apply intelligent controllers as setting devices and adjusters of hydraulic actuators of different purpose. The authors proposed a method of positioning control of a hydraulic steering actuator based on a fuzzy logic controller (FLC). FLC corrective action is implemented based on three input parameters similar to the traditional proportional-integral-derivative controller. To evaluate the efficiency of FLC application in the system of hydraulic actuator positioning, the authors developed a mathematical model describing the work of a complex electro-hydromechanical system including electromechanical converter, fluidic hydraulic actuator, and hydraulic cylinder actuator. Non-linear mathematical model of an electro-hydraulic servo is implemented in MatLab Simulink software; the proportional-integral-derivative FLC was formed using the Fuzzy Logic Toolbox means. The authors set the FLC parameters using the linguistic rule-based expert system and the change in the membership function form, varying which it is possible to get linear and non-linear output characteristics. The obtained piecewise-linear hyperplane of FLC controlling characteristic determining the non-linear nature of correction theoretically allows getting the improved quality of transient processes. Using the numerical experience method, the authors assessed the efficiency of control algorithms with traditional controllers and the equivalent fuzzy controllers within the known mathematical model of a control object (electro-hydraulic follower actuator). When synthesizing the proportional-type FLC, the authors formed the nonlinear control law which allowed compensating the nonlinearity of the hydraulic actuator system. When implementing the linear dependence of the proportional-integral FLC, the system demonstrates the behavior attributable to the traditional controller. Generated FLC with nonlinear characteristics did not show significant quality improvement of the transient process compared to the traditional controller. Based on the received results, the authors concluded the reasonability of FLC application within the control systems with hydraulic actuating elements.

Maximum Power Point Tracking for Shaded Photovoltaic Generation Using P&O Fuzzy Logic Approach

A fuzzy logic based photovoltaic controller for shaded photovoltaic power generation is proposed based on P&O MPPT method. To obtain stable operation and maximum output, the parameters of fuzzy logic controller is adjusted in real time for various shaded conditions. The performance of proposed method of fuzzy controller is examined and validated using MATLAB/Simulink environment.

A Comparison of DE and SFLA Optimization Algorithms in Tuning Parameters of Fuzzy Logic Controller

A Comparison of DE and SFLA Optimization Algorithms in Tuning Parameters of Fuzzy Logic Controller

FPGA implementation of rule optimization for stand-alone tunable fuzzy logic controller using GA

In this paper, self-tuned, rule-optimized multi-input and multi-output (MIMO) fuzzy logic controller (FLC) is implemented on field programmable gate arrays (FPGA). The design of membership functions, rule base are made with aid of genetic algorithm (GA). Flexibility in FPGA design is implemented through tuning of FLC parameters. The system is modularized as rule base development, rule base transfer and computations on FPGA. Based on the system, an experimental dataset is obtained, which is utilized in a capable computing platform so as to develop a fine-tuned fuzzy rule base. The synthesized rule base is transferred to FPGA along with user provided inputs through a GUI. The GUI also displays the output result sent by FPGA. The communication between the GUI and the FPGA is done via universal asynchronous receiver and transmitter. Rule-optimized FLC is implemented on Xilinx Virtex-5 LX110T board. This dedicated single chip architecture performs high-speed fuzzy inferences with processing speed up to 760 KFLIPS at a clock frequency of 247 MHz using 8 rules, 2 input variables at 16-bit resolution. Experiments of software implementation and hardware software co-design implementation are presented and compared.

Design and Implementation of Active Two Axes Solar Tracking System Using Particle Swarm Optimization Based Fuzzy Logic Controller

This paper presents the design and execution of an active two axes solar tracker with fuzzy controller based on PSO. The proposed system uses the light sensors to detect the position of the sun. The output of the sensors are used as the control inputs in moving the PV panel according to the sun’s position. Two PSO based fuzzy controllers were designed and implemented to the solar tracker in which PSO was used to tune the parameters of fuzzy logic controller to obtain better performance. The conventional fuzzy controllers were also applied to the system to measure the improvement that might be achieved by the proposed method. Before the system is implemented into the real plant, it must be first modeled using MATLAB/ simulink for simulation purpose. Therefore, the performance of the controllers and power gain of PV panel are then observed, investigated and analized in this work. The results show that there are some agreements of both experiment and simulation.

Comparative study of soft computing techniques for mobile robot navigation in an unknown environment

An autonomous mobile robot operating in an unstructured environment must be able to deal with dynamic changes of the environment. Navigation and control of a mobile robot in an unstructured environment are one of the most challenging problems. Fuzzy logic control is a useful tool in the field of navigation of mobile robot. In this research, fuzzy logic controller is optimized by integrating fuzzy logic with other soft computing techniques like genetic algorithm, neural networks, and Particle Swarm Optimization (PSO). Soft computing techniques are used in this work to tune the membership function parameters of fuzzy logic controller to improve the navigation performance. Four methods have been designed and implemented: manually constructed fuzzy logic (M-Fuzzy), fuzzy logic with genetic algorithm (GA-Fuzzy), fuzzy logic with neural network (Neuro-Fuzzy), and fuzzy logic with PSO (PSO-Fuzzy). The performances of these approaches are compared through computer simulations and experiment number of scenarios using Khepera III mobile robot platform. Hybrid fuzzy logic controls with soft computing techniques are found to be most efficient for mobile robot navigation. The GA-Fuzzy technique is found to perform better than the other techniques in most of the test scenarios in terms of travelling time and average speed. The performances of both PSO-Fuzzy and Neuro-Fuzzy are found to be better than the other methods in terms of distance travelled. In terms of bending energy, the PSO-Fuzzy and Neuro-Fuzzy are found to be better in simulation results. Although, the M-Fuzzy is found to be better using real experimental results. Hence, the most important system parameter will dictate which of the four methods to use.

IJIREEICE - Electrical, Electronics, Instrumentation and Control

A novel speed sensor less adaptive robust control method is proposed to improve the trajectory tracking performance of induction motors. The proposed design employs the so called vector control (or field oriented control) theory for the multilevel inverter fed induction motor drives. The inverter design is based on three- level Neutral Point Clamped (NPC) inverter with hysteresis current control technique. Two Mamdani type fuzzy logic controllers are used; one as speed controller and the other is in Luenberger Observer in order to estimate the actual rotor speed. The Particle Swarm Optimization algorithm is used to optimize the parameters such as membership functions, normalizing and de- normalizing parameters of fuzzy logic controller. The performance of proposed scheme is investigated under various load and speed conditions. The simulation results show its stability and robustness for high performance sensor less drive applications.

Two Stage Optimized Fuzzy Controlled Multi-Area System for AGC in Smart Grid Age

Abstract A model of two area deregulated power system with automatic generation control (AGC) is developed. Two conventional control strategies, integral controller optimized by Genetic Algorithm (GA) and Proportional derivative integral (PID) controller tuned with Ziegler-Nichols (ZN), have been compared with proposed two step GA optimized fuzzy logic controller (FLC). In proposed controller, first step optimization is carried out for range optimization with uniform scaling of membership functions of input and output variables and in second step, scaling and gain parameters of FLC are optimized. The proposed control scheme shows the better dynamic response following a step load change with combination of PoolCo and bilateral contracts in deregulated environment.

Seismic control response of structures using an ATMD with fuzzy logic controller and PSO method

This study focuses on the application of an active tuned mass damper (ATMD) for controlling the seismic response of an 11-story building. The control action is achieved by combination of a fuzzy logic controller (FLC) and Particle Swarm Optimization (PSO) method. FLC is used to handle the uncertain and nonlinear phenomena while PSO is used for optimization of FLC parameters. The FLC system optimized by PSO is called PSFLC. The optimization process of the FLC system has been performed for an 11-story building under the earthquake excitations recommended by International Association of Structural Control (IASC) committee. Minimization of the top floor displacement has been used as the optimization criteria. The results obtained by the PSFLC method are compared with those obtained from ATMD with GFLC system which is proposed by Pourzeynali et al. and non-optimum FLC system. Based on the parameters obtained from PSFLC system, a global controller as PSFLCG is introduced. Performance of the designed PSFLCG has been checked for different disturbances of far-field and near-field ground motions. It is found that the ATMD system, driven by FLC with the help of PSO significantly reduces the peak displacement of the example building. The results show that the PSFLCG decreases the peak displacement of the top floor by about 10%-30% more than that of the FLC system. To show the efficiency and superiority of the adopted optimization method (PSO), a comparison is also made between PSO and GA algorithms in terms of success rate and computational processing time. GA is used by Pourzeynali et al for optimization of the similar system.

An Educational Tool for the Genetic Algorithm-Based Fuzzy Logic Controller of a Permanent Magnet Synchronous Motor Drive

In this study, a novel educational tool is introduced for the genetic algorithm (GA)-based fuzzy logic controller (FLC) of a permanent magnet synchronous motor (PMSM) drive. This tool provides the operating principle of PMSM, essentials of GAs, and effects of FLC parameters on the performance of GA-based FLC. The proposed educational tool regarding GA-based FLC is a unique one in its field in the literature, as such a computer-based study aiming to teach the corresponding intelligent system has not been studied before, though both GA and FLC are well-known powerful techniques. The graphical interface, developed in Microsoft Visual C#. NET platform, has a flexible and functional structure. By changing motor operating conditions as well as GA and controller parameters, useful practices can be done on the whole system under various situations without wasting time. An evaluation of the tool is given based on student feedback.

Modeling and Control Techniques for Microstructure Development

True stress-strain data is obtained for 6061Al/ 10% SiC composites by hot compression test. Mathematical models for % volume of recrystallization and diameter of the recrystallized grains are developed with process parameters such as strain, strain rate and temperature. These models are applied for optimization of the grain size and % volume of recrystallization. An attempt has been made to control microstructure evolution during hot deformation using fuzzy logic controller through simulation in MATLAB software. The fuzzy logic controller parameters are tuned using genetic algorithm.

PSO and GA designed Pareto of Fuzzy Controller in AC Motor Drive

In electric vehicles EV, can be implemented a controller has highest extend of battery’s lifetime meet a superior dynamic speed behavior? This research offer multi-objective approaching planned by a Particle Swarm Optimization (PSO) complementary with Genetic Algorithm (GA). This hybrid evolutionary learning is utilized as an automated method to generate the optimal parameters of fuzzy logic controller (FLC) type Mamdani. The Pareto front characterizes the speed controller of an Induction Motor (IM). where the first function is the error between the actual speed and desired speed, and the other function is the energy dissipated from the electrical supply during (10 sec), in present work the multi-objective optimization of PSO and GA have been implemented separately using two M-file/MATLAB, and compacted the results to sketch the global Pareto, while the evaluation of “fitness functions” of the two computational algorithms have been determined using SIMULINKMATLAB, the simulation has a completely mathematical model of induction motor IM, voltage source inverter VSI and FLC. The FLC have been implemented using RSLogix 5000. The empirical results demonstrate that the proposed method realized a limited disbursed energy as possible as better dynamic behavior of the IM speed along the Pareto front.

Damping of low‐frequency oscillations using an SVC‐based supplementary controller

AbstractIn this paper, the optimal tunning problem of parameters of a conventional lead–lag controller (LLC) and a fuzzy logic controller (FLC) based on the static Var compensator (SVC) is considered. The solution is obtained using an improved particle swarm optimization (IPSO) algorithm. The membership functions and scaling factors of the FLC and LLC parameters are optimized using the above‐mentioned technique. The proposed controllers are settled down to an SVC that is installed at the middle of a transmission line connecting a single machine to an infinite bus system. Simulation results show the superiority of the optimized FLC compared to the optimized LLC and also when the SVC is without the supplementary controller under different disturbances and loading conditions. The simulations and analyses are implemented in MATLAB environment. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Incorporate intelligence into the differentiated services strategies of a Web server: an advanced feedback control approach

This paper presents an investigation into the application of advanced feedback control strategies to provide better web servers quality of service (QoS). Based on differentiated service strategies, fuzzy logic based control architectures are proposed to enhance the system capabilities. As a first control scheme, a Mamdani fuzzy logic controller (FLC) is adopted. Then, the Simulated Annealing (SA) algorithm (SAA) is used to optimize the FLC parameters with efficient tuning procedures. The SA optimized FLC (SAOFLC) is also implemented and applied to improve the system QoS. Simulation experiments are carried out to examine the performances of the proposed intelligent control strategies.