Performance Of Fuzzy Controller Research Articles

Enhancing voltage stability through wavelet-fuzzy control of hydrogen flow in OC-PEM fuel cell

Open cathode proton exchange membrane fuel cells (OC-PEM fuel cells) serve as electricity generators, utilizing hydrogen as an input source. While effective for fixed loads like residential applications, challenges arise in dealing with output voltage fluctuations caused by rapid load changes. These fluctuations not only impact fuel cell performance but also introduce instability in the supplied power. To solve this issue, the study proposes an innovative hydrogen flow control system employing a feedforward wavelet- fuzzy method. The primary goal of this control system is to enhance fuzzy control performance using wavelets, mitigating signal fluctuations and achieving optimal stability in fuel cell output voltage under constant load conditions. Wavelet functions act as filters on the fuzzy control input, minimizing fluctuations and refining the entire process. Additionally, a feedforward system is incorporated to maintain hydrogen flow at the set point value. The proposed control system is implemented on a validated model using experimental data. Performance analysis reveals that the proposed method effectively stabilizes voltage by accelerating the recovery time from disturbances.

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.

Effects of the structural period variations and near field earthquakes on a fuzzy controller performance for the variably baffled tuned liquid damper

Effects of the structural period variations and near field earthquakes on a fuzzy controller performance for the variably baffled tuned liquid damper

Soft Computing Algorithm-Based Intelligent Fuzzy Controller for Enhancing the Network Stability of IPS

The work furnished in this paper projects the application of fuzzy-aided tilt-integral-derivative (TID) with filter (TIDF) for regulating the dynamical behavior of the electrical interconnected power system (IPS) during uncertain conditions. The optimal design of the fuzzy TIDF is performed using the efficient soft computing technique of the water cycle algorithm (WCA). The suggested fuzzy TIDF controller performance is tested on the multi-area diverse source realistic (MADSR) IPS (MADSRIPS) subjected to control area 1 with step load disturbance (SLD) of 10%. Moreover, the analysis carried out on MADSRIPS is considered with the communication time delays (CTDs) for the investigation near to realistic environment. The efficacy of fuzzy-aided TIDF is contrasted with reported controllers available in literature, and the simulation analysis is acquired from the framework of MATLAB, which revealed the sovereignty of the suggested controller. Later, the MADSRIPS is operated with the integration of a HVDC line in conjunction with the tie-line to improve the performance.

Research on Artificial Intelligence Controller of DC Motor

This paper combines PID control and fuzzy control to control the DC motor. First of the DC motor PID control simulation, given the large range of parameters, the setting process numerous lock, so adopt the genetic algorithm of PID parameters, through the simulation, setting process greatly simplified, and then adopt fuzzy self-setting method to control DC motor, finally, various control scheme for simulation, the results of comparative analysis, shows that the fuzzy self-setting controller performance not only has good dynamic and static quality, and has strong anti-interference ability and parameter time-varying adaptability.adjust

Type-2 fuzzy-logic based control of photovoltaic-hydrogen production systems

Green hydrogen will play an important role for sustainable development, it can be considered as a promising solution for energy transition. In this paper, direct and indirect-coupled configuration of hydrogen production system consisted of Photovoltaic (PV) source and electrolyzer stack are investigated. A study on power loss and efficiency in PV-electrolyzer system is conducted for both cases. Optimal matching conditions in directly coupled hydrogen system can be achieved by modifying the configuration of series/parallel cells in electrolyzer stack or PV generator. However, the energy transfer can be improved by incorporating a buck converter between PV string and electrolyzer stack. In this article, the type-2 fuzzy inference system is proposed to control the operating point of PV string by adjusting the duty cycle of control signal of buck converter. A comparison with classical MPPT methods is carried out in order to verify the efficiency of suggested technique. It has been found that the indirect-coupled hydrogen system can achieve higher energy transfer rate by ensuring continuous extracting of maximum power of PV source under various operating conditions. The obtained results, under Matlab/Simulink environment, demonstrated a satisfactory performance of type-2 fuzzy logic controller in both transient and permanent regimes. We obtain a minimal power loss value (less than 8.1%) in directly-coupled hydrogen production system, in addition a satisfactory value of efficiency (more than 93%) has been obtained.

Comparative Analysis of Type-1 and Type-2 Fuzzy Controllers: Exploiting Synergies for Improved Control System Performance

The utilization of fuzzy controllers in control systems has gained significant attention in various fields due to their ability to handle complex and uncertain systems. This paper focuses on comparing the performance of two types of fuzzy controllers: type-1 fuzzy controller and type-2 fuzzy controller. While type-1 fuzzy controllers have been widely studied and applied, the advantages of type-2 fuzzy controllers in dealing with uncertain and ill-defined systems have garnered attention. A thorough analysis and comparison are conducted to investigate the practical benefits of type-2 fuzzy controllers. The study includes an overview of type-1 and type-2 fuzzy sets, followed by the design methodology for the two controllers. The designed controllers are implemented practically, and the results are analyzed and discussed. Previous research studies have shown the potential of type-2 fuzzy controllers to achieve superior performance compared to type-1 controllers. This paper contributes to the existing body of knowledge by presenting empirical evidence and practical insights into the performance of type-2 fuzzy controllers in real-world applications. The findings shed light on the benefits and limitations of utilizing type-2 fuzzy controllers, particularly in scenarios where system knowledge is limited or incomplete. The results of this study provide valuable guidance for control system designers and practitioners in selecting the most appropriate controller for their specific applications. Furthermore, the insights gained from this research contribute to the ongoing exploration and development of fuzzy control systems, enhancing their effectiveness in various domains.

Analytical Structure and Performance of Interval Type-2 Fuzzy Two-Term Controllers with Varying Footprint of Uncertainty

While type-2 fuzzy control has gained a lot of attention in the recent years, several challenges still remain with respect to controller design. The footprint of uncertainty (FoU) plays a very important role in designing type-2 fuzzy controllers, since the performance of type-2 fuzzy controllers largely depends on the choice of FoU. In this paper, we propose a simplified model of interval type-2 (IT2) fuzzy two-term controllers of Takagi–Sugeno (TS) type with only two rules in the rule base. The controller model is derived with varying FoUs based on only two type-2 input fuzzy sets (the simplest case). An extension to multiple input fuzzy sets is also presented. We investigate the variation in control surface and computational aspects of the IT2 fuzzy two-term controller as the FoU is varied. The performance of the proposed IT2 fuzzy controller with respect to varying FoUs is evaluated in the simulation study.

Design of Fin Stabilizer Controller Based on Fuzzy Control Principle

As the ship is sailing in the sea, the PID control parameters set may not be the optimal parameters under different sea conditions according to the changes of sea conditions and wind, waves and currents, so the PID control of fin stabilizer cannot achieve the ideal control effect in the actual ship operation process. This paper studies the design of fuzzy controller with better robustness than traditional PID control, and explores the actual control performance of fuzzy controller under different sea conditions.

Fermat-curve based fuzzy inference system for the fuzzy logic controller performance optimization in load frequency control application

Fermat-curve based fuzzy inference system for the fuzzy logic controller performance optimization in load frequency control application

Optimal Battery Management System Utilized in Electric Vehicle Using Fuzzy Logic Controller (FLC) and Artificial Eco-System Algorithm (AEO)

In real life applications, using electronics switch in high voltage and high current applications is very challenging due to limitation of hardware and safety. In this manuscript, a hybrid system is proposed for battery management system (BMS) used in electric vehicle (EV) is a critical part for effective utilization of the energy storage. The proposed hybrid technique is joint performance of fuzzy logic controller (FLC) and Artificial eco-system algorithm (AEO), hence it is called FLTAEO technique. The existing battery management control system is not effective for the Induction motor drive based solar PV powered electric vehicle system. In this manuscript, the control strategies of charging with discharging of Li-ion batteries coupled Induction motor with solar PV power are used with proposed technique. Here, the introduced FLC and AEO are discussed with detailed control abilities for charging/discharging. The simulations are done in MATLAB platform to analyze the superiority of the proposed method. Here, BESS life comparison, fitness function for charging, fitness function for discharging, error Distribution, RMSE calculation for 1 hour and 5 minutes ahead prediction interval (PI) are analyzed and compared with various existing techniques, like Emperor Penguin Algorithm (EPA) and Seagull Optimization Algorithm (SOA).

Simulation design of automobile automatic clutch based on mechatronics

Abstract This article aims to study the simulation and design of automobile automatic clutch under mechatronics. A new control strategy for the automatic clutch of the electromagnetic transmission is proposed. The clutch mechanism model, clutch drive model, clutch system model, and internal combustion engine model are constructed. The fuzzy logic control performance of the automatic clutch was verified in different operating modes, including starting on flat roads and mountain roads. The method provides a reasonable reference for the design of an automatic clutch of electromagnetic continuously variable transmission.

Altitude control of a quadcopter using interval type-2 fuzzy controller with dynamic footprint of uncertainty

Footprint of uncertainty (FOU) characteristics of interval type-2 membership functions (IT2-MFs) play an important role in the performance and robustness of interval type-2 fuzzy controllers (IT2-FCs). In literature, fixed FOU structures are used in almost all IT2-FC designs. In this study, an IT2-FC with dynamic FOU is proposed to provide high performance and robustness in the altitude control of a quadcopter. A proportional–derivative (PD) type FC with Takagi–Sugeno rule structure is used in the design procedure of the proposed controller. Additionally, input variables (error and derivative of error) are defined by using triangular IT2-MFs. To provide a dynamic FOU structure, the height of the lower MF (LMF) of each interval type-2 fuzzy set is defined as a function of the system error. In this way, FOU levels of IT2-MFs are adjusted dynamically since the height value of the LMF directly determines the FOU level of the IT2-MF. To evaluate the effectiveness of the proposed controller, comparison studies are performed under different conditions, including unmodeled measurement noises, an external disturbance, and a system parameter uncertainty. A classical PD, a type-1 fuzzy PD, and an interval type-2 fuzzy PD with fixed FOU controllers are used in the comparisons. The comparison results demonstrate that the proposed interval type-2 fuzzy PD controller with dynamic FOU exhibits better performance and more robustness than the other controllers.

A neural-network based variable universe fuzzy control method for power and axial power distribution control of large pressurized water reactors

This paper proposes a neural network-based variable universe fuzzy controller (NN-VUFC) for power and axial power distribution control of large pressurized water reactors (PWRs). First, a two-node reactor kinetics model is introduced, and the fuzzy inference rules are formulated for the reactor power and axial power distribution control according to their deviations from corresponding setpoint values. Then, based on the idea of variable universe, the scaling factor of the input fuzzy universe is designed to maximize the utilization of fuzzy rules. Finally, a neural network is designed to optimize the proportionality coefficient of the scaling factor online to further improve the fuzzy control performance. Simulation results of the CPR1000 reactor under typical transient conditions show that the NN-VUFC can realize good control of both the reactor power and axial power distribution, and its control performance is effectively improved compared to traditional fuzzy controllers.

Leader–follower Stackelberg game oriented adaptive robust constraint-following control design for fuzzy exoskeleton robot systems

To comprehensively enhance the robust tracking performance, a leader–follower Stackelberg game oriented adaptive robust constraint-following control scheme has been proposed for a class of uncertain mechanical systems. In the proposed scheme, a fuzzy set-theoretic description represents the uncertainties (possibly fast time-varying), and an adaptive robust constraint-following control ensures the robust stability. With the fuzzy uncertainty description and control performance analysis, a leader–follower game theory is employed to obtain multiple optimal gains for the proposed control scheme. Further, the existence of these optimal parameters can be verified. The proposed approach is successfully applied to a lower limb exoskeleton (LLE) robot system for rehabilitation training.

Lyapunov-based Robust Adaptive Configuration of the UAS-S4 Flight Dynamics Fuzzy Controller

AbstractIn tandem with the fast-growing demand for Unmanned Aerial Vehicles (UAVs) for surveillance and reconnaissance, advanced controllers for these critical systems are needed. This paper proposes a flight dynamics controller design that considers various uncertainties for the Hydra Technologies UAS-S4 Ehécatl. In order to be realistic, in addition to flight dynamics nonlinearities, three main sources of uncertainties are considered, as those caused by unknown controller’s parameters, modeling errors, and external disturbances. A Robust adaptive fuzzy logic controller is designed, in charge of nonlinear flight dynamics in presence of a variety of uncertainties. The nonlinear flight dynamics is modeled based on the Takagi-Sugeno method relying on the soft association of local linear models. Since this controller is model-based, an optimal reference model is defined, which is stabilised by the Linear Quadratic Regulator procedure. A fuzzy logic controller is then designed for the nonlinear model. Lastly, with the aim to handle the uncertainties, the gains of the fuzzy controller are reconfigured, and are continuously adjusted by Lyapunov-based robust adaptive laws. The performance of the UAS-S4 Robust adaptive fuzzy logic controller is evaluated in terms of lateral and longitudinal flight dynamics stabilisation, and the reference model state variables tracking under various uncertainties.

Investigation of performance of fuzzy logic controllers optimized with the hybrid genetic-gravitational search algorithm for PMSM speed control

Fuzzy logic controllers (FLCs) are widely used to control complex systems with model uncertainty, such as alternating current motors. The design process of the FLC is generally based on the designer’s adjustments on the controller until the desired performance is achieved. However, doing the controller design in this way makes the design process quite difficult and time-consuming, so it is often impossible to make a suitable and successful design. In this study, the output membership functions of the FLC are optimized with heuristic algorithms to reach the best speed control performance of the permanent magnet synchronous motor (PMSM). This paper proposes a new hybrid algorithm called H-GA-GSA, created by combining the advantages of the Genetic Algorithm (GA) and Gravitational Search Algorithm (GSA) to optimize FLC. The paper presents a convenient adjustment and design method for optimizing FLC with heuristic algorithms considered. To evaluate the effectiveness of H-GA-GSA, the proposed hybrid algorithm has been compared with GA and GSA in terms of convergence rate, PMSM speed control performance and electromagnetic torque variations. Optimization performance and results obtained from simulation studies verify that the proposed hybrid H-GA-GSA outperforms GA and GSA.

Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics

In this work, we propose, through the use of population-based metaheuristics, an optimization method that solves the problem of autonomous path tracking using a rear-wheel fuzzy logic controller. This approach enables the design of controllers using rules that are linguistically familiar to human users. Moreover, a new technique that uses three different paths to validate the performance of each candidate configuration is presented. We extend on our previous work by adding two more membership functions to the previous fuzzy model, intending to have a finer-grained adjustment. We tuned the controller using several well-known metaheuristic methods, Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Harmony Search (HS), and the recent Aquila Optimizer (AO) and Arithmetic Optimization Algorithms. Experiments validate that, compared to published results, the proposed fuzzy controllers have better RMSE-measured performance. Nevertheless, experiments also highlight problems with the common practice of evaluating the performance of fuzzy controllers with a single problem case and performance metric, resulting in controllers that tend to be overtrained.

Design and Simulation of a Robust Combustion Control System for a Steam Power Plant Using MATLAB Simulink

Steam power plants are extensively utilised worldwide for electricity generation and whose operation efficiency should be improved to make better utilisation of fossil fuels. Inside a steam power plant, the combustion system is probably the most imperative system to be controlled since it guarantees the effective and secure operation of the boiler. In this paper, firstly, the block diagram and corresponding transfer functions of the combustion system were built and assumed. Then, the conventional and fuzzy PID controllers were designed and applied to control the boiler combustion system. Through the simulation using MATLAB Simulink, the closed-loop responses of the conventional PID and the fuzzy controller were obtained and compared. Furthermore, the robustness of the fuzzy PID controller was tested and verified. The results show that the overall performance of fuzzy PID controller is more excellent than the conventional one. Additionally, it is also proven that the fuzzy PID controller possesses outstanding robustness, and it can handle quite well with the disturbances appearing in the system and the uncertainties that occurred in system modelling, which is rather advantageous and practical in a steam power plant.

Fuzzy control of active suspension system based on quarter car model

In this paper, the performance of model-free intelligent Fuzzy Controller analyzed with the model-based conventional controller concerning body displacement and body acceleration. The development of a mathematical model for active suspension is quite difficult which increases the difficulty of designing a model-based controller for active suspension. So, the model-free intelligent adaptive fuzzy controller has been proposed for active suspension. The Fuzzy Controller predicts and controls the performance of active suspension better than a conventional controller. The results obtained from the simulation of the road profile show that the proposed fuzzy control performs better than the conventional controller in terms of body displacement and body acceleration.