Design Of Fuzzy PID Controller Research Articles

Design and Application of Fuzzy PID Controller Based on MATLAB

In an era where the development of intelligent technology is a pivotal force propelling economic advancement, enhancing production efficiency, diminishing costs, and reshaping daily life, the field of intelligent vehicle control technology has garnered significant attention among automotive researchers. The dynamics of smart cars during driving often exhibit nonlinearity and are susceptible to external environmental disturbances, such as wind resistance and road surface inequality. Traditional PID controllers have limited ability to suppress these interferences, so this study uses fuzzy PID algorithms to solve the inherent challenges in intelligent vehicle control. Through comprehensive analysis of the current landscape and technological advancements in intelligent vehicle control systems, coupled with rigorous MATLAB/Simulink simulations and real-world testing, the study showed that the overshoot of the adaptive fuzzy PID was reduced by 86% compared to the traditional PID. Consequently, it markedly enhances the precision and stability of speed control in intelligent vehicles, providing a groundbreaking perspective and practical methods for the enhancement of intelligent vehicle control systems with considerable application potential.

Fuzzy-PID controller design for RGV speed track based on improved PSO algorithm

Fuzzy-PID controller design for RGV speed track based on improved PSO algorithm

Fuzzy PID Controller Design for a Coupled Tank Liquid Level Control System

In industrial processes, the most important loop is the liquid-level control loop. The coupled tank system CT-100 becomes an essential apparatus for process control researchers. This paper introduces two controller techniques to control the water level in the second tank as a single input single output system. The PID controller is designed to control the linearized model, where the controller parameters are tuned using the Ziegler- Nichols tuning method. In addition, Fuzzy PID is designed based on adequate knowledge and experience. The proposed control approaches are simulated using MATLAB Simulink. Then, the obtained results using these controllers are compared in terms of time response specifications and the ITAE criterion. It is also, tested for step-change tracking signal and disturbance rejection. Finally, the simulation results showed that the Fuzzy PID controller has a robust performance.

Determination of control area and design of fuzzy rule-tuned PID controller for LFC of multimachine power system

Determination of control area and design of fuzzy rule-tuned PID controller for LFC of multimachine power system

WAMS-Based Fuzzy Logic PID Secondary Voltage Control of the Egyptian Grid

This paper presents the application of fuzzy logic PID secondary voltage control to the Egyptian power system model. The study included tertiary voltage control, Wide Area Measurement System (WAMS) configuration, a selection of pilot buses, and fuzzy logic PID secondary voltage control to improve the system performance. The secondary voltage control was applied using a fuzzy PID coordinated controller, a reactive power integral controller, Automatic Voltage Regulators (AVRs), and regional generators. The tertiary voltage control was implemented based on the optimal power flow to maximize the reactive power reserve. A novel optimization technique is presented to select pilot buses based on different operating conditions and compared to other techniques. The optimal WAMS configuration included the best allocation of Phasor Measurement Units (PMUs), Phasor Data Concentrators (PDCs), and the required communication infrastructure considering geographical regions with minimum cost. The Egyptian power grid considering 500/220 kV level is simulated by using DIgSILENT software to perform static and dynamic analyses, while the WAMS optimization problems and fuzzy logic PID controller design are performed by employing MATLAB software.

Design of Fuzzy PID Controller Based on Sparse Fuzzy Rule Base for CNC Machine Tools

The robustness of the control algorithm plays a crucial role in the precision manufacturing and measurement of the CNC machine tool. This paper proposes a fuzzy PID controller based on a sparse fuzzy rule base (S-FPID), which can effectively control the position of a nonlinear CNC machine tool servo system consisting of a rotating motor and ball screw. In order to deal with the influences of both the internal and external uncertainties in the servo system, fuzzy logic is used to adjust the proportion, and integral and differential parameters in real-time to improve the robustness of the system. In the fuzzy inference engine of FPID, a sparse fuzzy rule base is used instead of a full-order fuzzy rule base, which significantly improves the computational efficiency of FPID and saves a lot of RAM storage space. The sensitivity analysis of S-FPID verifies the self-tuning ability of its parameters. Furthermore, the proposed S-FPID has been compared with the PID and FPID via simulation and experiment. The results show that compared with the classical PID controller, the overshoot of the S-FPID controller is reduced by 74.29%, and the anti-interference ability is increased by 62.43%; compared with FPID algorithm, the efficiency of the SPID is improved by 87.25% on the premise of a slight loss in robustness.

An AEFA-Based Optimum Design of Fuzzy PID Controller for Attitude Control Flywheel with BLDC Motor

A new method for optimizing the fuzzy PID controller, based on an artificial electric field algorithm (AEFA), is proposed in this paper, aiming at improving the stability indicator of the Brushless DC (BLDC) motor for the small satellite attitude control flywheel. The BLDC motor is the basic part of the small satellite attitude control flywheel. In order to accurately control the attitude of the small satellite, a good motor control system is very important. Firstly, the mathematical model of the BLDC motor is established and the BLDC motor speed control system using traditional PID control is designed. Secondly, considering that the small satellite speed control system is a nonlinear system, a fuzzy PID control is designed to solve the shortcomings of the fixed parameters of the traditional PID control. Finally, we find that the control accuracy of the fuzzy PID control will change with the range of the input. Therefore, we introduce the AEFA to optimize fuzzy PID to achieve high-precision attitude control of small satellites. By simulating the BLDC motor system, the proposed fuzzy PID controller based on AEFA is compared with the traditional PID controller and the fuzzy PID controller. Results from different controllers show that the proposed control method could effectively reduce steady state error. In addition, the proposed fuzzy PID–AEFA controller has the better anti-jamming capability.

Design and Evaluation of Self-Tuning Fuzzy PID Controller with Reference Model for Ball and Beam Mechanism

—Fuzzy Logic controller is one of the popular techniques in control system. However, the accuracy depends on numbers and the design of membership functions. Further, the output does not always fulfill specific design requirements. In this study, Fuzzy PID controller with first-order reference model had been designed and evaluated. The proposed new structure is able to guide fuzzy mechanism to produce desired output response as specified by the first-order model such as the time constant, rise time and settling time. The study was implemented on a ball and beam system to control the position of the ball while rolling on a beam. The control of a ball and beam system is challenging because it is an open-loop unstable system as the ball will continuously moves on the beam without control. The study also compares the results acquired from Fuzzy PID and Fuzzy PI plus PD against the proposed Fuzzy PID with first-order reference model controller. The evaluation covers for a pole starting from 0 until 1.0 for the first-order model. As a results, the proposed controller is able to produce an output response as specified where the fastest response was acquired when pole is set to 0.9. Whereas the Fuzzy PID and Fuzzy PI plus PD always produce the same transient response during step input test.

Optimal design of fuzzy-PID controller for automatic generation control of multi-source interconnected power system

This paper suggests a fuzzy logic controller (FLC) structure from seven membership functions (MFs) and its input–output relationship rules to design a secondary controller to reduce load frequency control (LFC) issues. The FLC is coupled to a proportional–integral–derivative (PID) controller as the proposed FPID controller, which is tuned by an optimized water cycle algorithm (WCA). The proposed WCA: FPID scheme was implemented with two models from the literature under the integral time absolute error cost function. Initially, a two-area non-reheat unit was implemented, and the gains of PID and FPID controllers were adjusted to verify the suitability of WCA in solving LFC issues. Then, in order to identify the robustness of the closed-loop system, sensitivity analysis is carried out. Additionally, a two-area non-reheat unit was tested under the governor dead band nonlinearity. To guarantee the suitability of the proposed FPID controller, a model with a mixture of power plants, such as reheat, hydro, and gas unit in each area was carried out with and without the HVDC link, which can increase practical issues with LFC. The proposed controller's robustness was studied for all models under numerous scenarios, step load perturbations (SLP), and different objective functions. Simulation results proved that the proposed FPID controller provided superior performance compared to recently reported techniques in terms of peaks and settling time.

Design of interval type-2 fuzzy fractional order PID controller based on particle swarm optimization

Abstract In this paper, an interval type-2 fuzzy fractional order PID controller (IT2F-FOPIDC) based on particle swarm optimization is proposed. The proposed controller has two adjustable degree of freedom parameters, so the control range of the controller parameters becomes larger and can control the controlled object more flexibly. Aiming at the difficult problem of many controller parameters and tuning, this paper introduces particle swarm optimization algorithm to optimize the controller parameters. In order to avoid the problems of long iteration time and high computational cost caused by Karnik-Mendel reduction algorithm, the enhanced iterative with stop condition algorithm is selected for the proposed controller. Finally, the proposed controller is applied to the second order systems with time delays. Simulation results show that the proposed strategy has faster response time and smaller oscillation amplitude, and its performance is better than fuzzy controller and interval type-2 fuzzy controller.

Self-tuning Fuzzy PID Controller Design and Energy Management in DC Microgrid: Standalone and Grid Connected Mode

This paper presents a control system and energy management strategy with a standalone and grid-connected mode in the microgrid. The microgrid is energized by distributed generation system of a photovoltaic panel, wind turbine and lithium-ion batteries. In this paper, the controller structure for regulating the voltage regulation in this microgrid consisting of renewable energy sources and battery system is covered. Overshoot, rise time, settling performances of designed self-tuning fuzzy proportional-integral-derivative controller and conventional proportional-integral-derivative controller examined comparatively. In addition, an energy management system has been proposed for loads which fed in the microgrid. Here, the aim is to make maximum use of renewable energy sources as much as possible, to maintaining voltage regulation and to ensure continuity in the feeding of the critical load. The system switches between the standalone and grid-connected modes if necessary. A model of the microgrid's dynamic behavior was constructed and it is simulated in the MATLAB®/Simulink environment. The results show that despite the changes in the production and load side of the grid, voltage and frequency oscillations are stabilized within a short time and allowed tolerance limits.

Design and robustness analysis of fuzzy PID controller for automatic voltage regulator system using genetic algorithm

In this paper, a Fuzzy proportional–integral–derivative (Fuzzy PID) controller design is presented to improve the automatic voltage regulator (AVR) transient characteristics and increase the robustness of the AVR. Fuzzy PID controller parameters are determined by a genetic algorithm (GA)-based optimization method using a novel multi-objective function. The multi-objective function, which is important for tuning the controller parameters, obtains the optimal solution using the Integrated Time multiplied Absolute Error (ITAE) criterion and the peak value of the output response. The proposed method is tested on two AVR models with different parameters and compared with studies in the literature. It is observed that the proposed method improves the AVR transient response properties and is also robust to parameter changes.

Fractional‐order fuzzy PID controller design on buck converter with antlion optimization algorithm

The design of a Fractional-Order fuzzy-based PID (FO-F-PID) control structure is presented for Buck converter in the presence of harmful disturbances. A fractional-order proportional-integral-derivative (FO-PID) control scheme is utilized initially to damp the oscillations and remove the steady-state error. To increase the tendency rate of the error to zero, the FO-PID method is applied to a fuzzy-logic-based compensatory stage. At the same time, the fuzzy part gathers the data based on the error and error derivative. The FO-PID control scheme has the capability to enhance the robustness of the control technique against disturbances and parametric variations. Furthermore, to optimize the control parameters, an efficient algorithm so-called Antlion Optimization (ALO) algorithm, is used. Utilizing the ALO algorithm for tuning the FO-PID gains depicts more accurate responses in solving constrained problems with diverse search spaces. Considering numerous disturbances on DC-DC converters, an FO-F-PID controller can be an appropriate alternative since it is more robust against load variations and noise. Moreover, PSO-PID and FO-PSO-PID controllers are designed to drive a comparison between them. Finally, the merits of the presented controller are validated for various scenarios. It can be seen that the FO-F-ALO-PID method provides much better results with faster dynamics. Matlab-Simulink environment is used for the simulations, and the experimental results are tested by the micro-processor to validate the superiority of the proposed method.

Control System and Algorithm of Sewage Treatment Plant Based on Artificial Intelligence

With the improvement of people’s awareness of environmental protection in recent years, the related problems of water pollution treatment have gradually come into people’s view. As the source of life, water accounts for a huge proportion in our lives, but at the same time, water pollution is quietly spreading in places we don’t know. The continuous discharge of heavy industrial wastewater, agricultural wastewater and domestic sewage leads to increasingly serious water pollution. Sewage treatment(ST) is imperative, and its social benefits are huge, but the corresponding cost is high, and the return on investment is low. Traditional ST methods can not load large-scale ST. How to carry out ST based on artificial intelligence(AI), build ST plant control system, and make ST enter the era of automation is the problem to be solved. The purpose of this paper is to put forward the reform of control system for ST plant based on AI, apply AI into ST, and realize the automation and precision of ST plant. This paper mainly uses the fuzzy self-tuning PID control system algorithm, through the analysis of ST control object, analysis of fuzzy self-tuning PID controller design to complete the ST control system settings. In this paper, the literature review method and data analysis method are used. By collecting relevant data, the control system of ST plant is constructed to simulate ST, and the real-time data of ST is analyzed. The traditional PID control and fuzzy self-tuning PID control are compared. The experimental results show that the wastewater treatment plant system based on AI input, in the aspect of wastewater treatment, the concentration of COD and BOD in the treated wastewater are reduced by a certain proportion, the dissolved oxygen content in the wastewater reaches about 2.0mg/l, which meets the national discharge standard, and its rising time is reduced to 25 seconds, and the adjustment time is saved by 50 seconds.

Design of fuzzy PID controller for mine local ventilator optimized by improved genetic algorithm

Design of fuzzy PID controller for mine local ventilator optimized by improved genetic algorithm

Design of optimal fractional order fuzzy PID controller based on cuckoo search algorithm for core power control in molten salt reactors

In case of the adverse effects of unforeseeable or uncertain disturbances on control systems, stability robustness of control performance is crucial for controller design. Accordingly, in this study, an optimal design scheme for Fractional Order Fuzzy Proportional Integral Derivative (FOFPID) controller with adoption of Cuckoo Search (CS) optimization algorithm is presented in order to examine the application of the FOFPID in the core power control of molten salt reactors (MSRs) as an example. By using the proposed scheme, PID, FOPID and fuzzy PID controllers are also designed by the CS for the purpose of showing the superiority of the FOFPID controller. Firstly, a nonlinear dynamic model of the MSR system is introduced by means of point kinetic equations considering the time delay in the fluid's transit connecting the core and the heat exchanger. Then, the nonlinear model is linearized for obtaining a linear model of MSR core system. Also, for the purpose of performing the sensitivity analysis, transit behaviors for both the linear and nonlinear approaches are shown and compared for different reactivity inputs. The core power control of molten salt reactor system is examined for each tuned controller in terms of stability, time domain and the robustness analysis under instantaneous and continuous disturbances. The results showed that the proposed CS based FOFPID controller has better control performance as compared to the PID, FOPID and fuzzy PID controllers tuned by the CS, especially in point of capability of disturbance suppression, improving tracking performance, and robustness against parameter uncertainty.

Application of a simplified Grey Wolf optimization technique for adaptive fuzzy PID controller design for frequency regulation of a distributed power generation system

A Simplified Grey Wolf Optimizer (SGWO) is suggested for resolving optimization tasks. The simplification in the original Grey Wolf Optimizer (GWO) method is introduced by ignoring the worst category wolves while giving priority to the better wolves during the search process. The advantage of the presented SGWO over GWO is a better solution taking less execution time and is demonstrated by taking unimodal, multimodal, and fixed dimension test functions. The results are also contrasted to the Gravitational Search Algorithm, the Particle Swarm Optimization, and the Sine Cosine Algorithm and this shows the superiority of the proposed SGWO technique. Practical application in a Distributed Power Generation System (DPGS) with energy storage is then considered by designing an Adaptive Fuzzy PID (AFPID) controller using the suggested SGWO method for frequency control. The DPGS contains renewable generation such as photovoltaic, wind, and storage elements such as battery and flywheel, in addition to plug-in electric vehicles. It is demonstrated that the SGWO method is superior to the GWO method in the optimal controller design task. It is also seen that SGWO based AFPID controller is highly efficacious in regulating the frequency compared to the standard PID controller. A sensitivity study is also performed to examine the impact of the unpredictability in the parameters of the investigated system on system performance. Finally, the novelty of the paper is demonstrated by comparing with the existing publications in an extensively used two-area test system.

Design and Implementation of Fuzzy PID Controller for Controlling the Speed of Robotic Vehicle System in MATLAB

In recent years, researchers have been developed robotic vehicles for different applications especially in agriculture system. This system consists of a Robotic Vehicle which directs through the area in the required path doing digging as it goes through the path. This paper includes the modeling, design and analytical results of fuzzy PID and PID controller for tracking the speed of the robotic vehicle. The proposed controller was employed to track the desired speed and tracks the direction of the robot vehicle. Ziegler Nicholas and Auto tune based PID algorithm have been used by some authors to find the tuning parameters of P, PI and PID Controllers for achieving the desired set point. The Conventional PID and fuzzy PID algorithms are modelled and simulated in the MATLAB environment using programming code and Simulink. Finally, the performance assessment is contrasted with standard controller PID and fuzzy PID. Fuzzy PID controller tracks the desired speed of robotic vehicle with accuracy in performance indices as compared to conventional PID controller, specified by the designer.

Design of Fuzzy-PID controller using logarithmic approximations for IMC-LFC Model

This paper provides a Artificial Intelligent controller layered to analyze the power system’s logarithmic load frequency control problems. The control system ensures the preservation of steady frequency errors and of inadvertent bonding in a tolerance limit. The proposed control system was developed for a two-area hydrothermal heating system. The approach to control design is based on the two-degree internal model control (2DOF), which combines the fuzzy control technique for model order reduction to increase frequency excursions. Different techniques of the two-area reheating hydraulic energy system are integrated into the scheme and a correct combining of energy resources is seen in different operational conditions as a pragmatic solution. The IMC- FUZZY-PID architecture of the reduced order model, proposed with the original high order framework, delivers strong dynamic response and load sturdiness.

Design of gain scheduled fuzzy PID controller for multi-link robot manipulators

Gain Scheduling (GS) controller is one of the most popular approaches to nonlinear control design. Commonly, Gain Scheduling (GS) controllers have better performance than robust ones. In this paper, the structure of a proposed gain scheduled PID fuzzy controller (GS-PIDF) designed and analyzed. The Fuzzy PID controller gains weighted through gain scheduling designed system to grantee both the stability and the best performance of a multi links robot manipulator system. The overall designed controller system is applied to two links robot manipulator system, which is subject to modelling using Lagrangian dynamics method and simulation vie MatLab and Simulink package to minimize the vibration and acquire best performance.