Optimal Fuzzy PID Research Articles

Dung Beetle Optimized Fuzzy PID Algorithm Applied in Four-Bar Target Temperature Control System

With the widespread application of infrared thermal imagers in various fields, the demand for thermal imagers and their performance parameter testing equipment has increased significantly. There are particularly high demands on the detection accuracy of minimum resolvable temperature difference (MRTD) testers. Traditional MRTD testers have an issue with the four-bar target temperatures being easily affected by the external environment, resulting in non-uniform temperatures and imprecise detection results. This paper proposes an improvement to the four-bar targets by making them temperature-controllable. Temperature is controlled by installing thermoelectric coolers (TECs) and thin-film platinum resistors at the center and periphery of the four-bar targets with different spatial frequencies. The dung beetle algorithm is used to optimize fuzzy PID parameters to regulate the TEC’s heating and cooling, improving the overall temperature uniformity of the four-bar targets. Temperature simulations of the four-bar targets were conducted on the COMSOL platform, with the control part simulated on the Simulink platform. The simulation results show that, compared to traditional PID, the fuzzy PID controller reduces overshoot by approximately 3.6%, although the system still exhibits mild oscillations. The fuzzy PID controller optimized by the dung beetle optimization (DBO) algorithm, in comparison to standard fuzzy PID, reduces the settling time by about 40 s and lowers overshoot by around 7%, with oscillations in the system nearly disappearing. Comparing the fuzzy PID optimized by the particle swarm optimization (PSO) algorithm with the fuzzy PID optimized by the DBO algorithm, the DBO-based controller shows shorter rise and settling times, further illustrating the superiority of the fuzzy PID control optimized by the dung beetle algorithm. This provides a theoretical foundation for improving the accuracy of MRTD detector measurements. Finally, experimental verification was carried out. The experimental results indicate that DBO (drosophila-based optimization) has significant advantages, and its optimized results are closer to the actual values.

Particle swarm optimized fuzzy proportional-integral-derivative controller-based transverse leaf spring active suspension for vibration control

The transverse leaf spring (TLS) suspensions are a promising option for van vehicles due to their high load-carrying capacity and excellent handling stability. However, its ride comfort remains a major challenge. This paper investigates and compares the effects of semi-active and active control strategies to enhance the ride comfort of TLS suspensions. Firstly, a four-degree-of-freedom (4-DOF) half-car model and a multi-body dynamics (MBD) model of the TLS suspensions are established. The MBD model has higher accuracy and can describe the medium and high frequency characteristics of the TLS suspensions, such as the suspension offset frequency and the frequency response function of the body vertical acceleration (BVA). Therefore, based on the MBD half-car model with TLS suspensions, this paper proposes an optimal fuzzy PID active control strategy considering the left and right suspension coupling. The optimization objectives are the BVA, the left and right suspensions dynamic deflection, and the left and right wheels dynamic displacement. The integral absolute error is used as the evaluation criterion. The left and right fuzzy PID controllers’ parameters are obtained through particle swarm optimization. Simulation results demonstrate that the particle swarm optimization fuzzy PID active control strategy effectively controls the low-frequency vibration of the TLS suspensions and suppresses the medium- and high-frequency vibration characteristics compared with the traditional skyhook semi-active control strategy. This technology provides a reference for improving the ride comfort of the TLS suspensions.

Optimal Fuzzy-FOPID, Fuzzy-PID Control Schemes for Trajectory Tracking of 3DOF Robot Manipulator

The present study explores the guidance of a robotic arm along a predefined path by implementing an optimal fuzzy fractional order PID controller-based control strategy. This method serves as a means to address the nonlinearity and unpredictability of the robotic manipulator, contingent upon the fuzzy logic controller's specifications and the employment of a clonal selection algorithm. The dynamic equation of the manipulator was considered as an initial point, followed by designing a fuzzy controller for this purpose. To validate the effectiveness of this approach, it was compared to other techniques, such as Fuzzy, Fuzzy-PID, and fuzzy-FOPID controllers, with PID and FOPID controller parameters optimized using clonal selection algorithms. Simulation results reveal that the fuzzy-FOPID variant outperformed other methods under varying load conditions and model uncertainties, using SIMULINK/MATLAB 2014a.

A novel PID controller for pressure control of artificial ventilator using optimal rule based fuzzy inference system with RCTO algorithm

In order to improve the pressure tracking response of an artificial ventilator system, a novel proportional integral derivative (PID) controller is designed in the present work by utilizing an optimal rule-based fuzzy inference system (FIS) with a reshaped class-topper optimization algorithm (RCTO), which is named as (Fuzzy-PID). Firstly, a patient-hose blower-driven artificial ventilator model is considered, and the transfer function model is established. The ventilator is assumed to operate in pressure control mode. Then, a fuzzy-PID control structure is formulated such that the error and change in error between the desired airway pressure and actual airway pressure of the ventilator are set as inputs to the FIS. The gains of the PID controller (proportional gain, derivative gain, and integral gain) are set as outputs of the FIS. A reshaped class topper optimization algorithm (RCTO) is developed to optimize rules of the FIS to establish optimal coordination among the input and output variables of the FIS. Finally, the optimized Fuzzy-PID controller is examined for the ventilator under different scenarios such as parametric uncertainties, external disturbances, sensor noise, and a time-varying breathing pattern. In addition, the stability analysis of the system is carried out using the Nyquist stability method, and the sensitivity of the optimal Fuzzy-PID is examined for different blower parameters. The simulation results showed satisfactory results in terms of peak time, overshoot, and settling time for all cases, which were also compared with existing results. It is observed in the simulation results that the overshoot in the pressure profile is improved by 16% with the proposed optimal rule based fuzzy-PID as compared with randomly selected rules for the system. Settling time and peak time are also improved 60–80% compared to the existing method. The control signal generated by the proposed controller is also improved in magnitude by 80–90% compared to the existing method. With a lower magnitude, the control signal can also avoid actuator saturation problems.

Research on algorithms of system vibration in yaw process of large wind turbine

Aiming at the problem of system vibration during yawing of large wind turbines, the mathematical model is obtained by simulating the structure model of the yawing system, which is then represented by a general free vibration equation. The system vibration equation is modeled and simulated by MATLAB/Simulink. The model is controlled by PID, Fuzzy PID and Optimal Fuzzy PID intelligent algorithm, and the control result is obtained. By comparing the control results between different algorithms, it can be shown that although the PID algorithm and the Fuzzy PID algorithm can obviously and effectively control the vibration model of the yaw system, and the control effect of the Fuzzy PID algorithm is better than that of the PID algorithm, the PID parameters obtained by the “trial and compact method” are not the optimal parameters, which will affect the control effect of the Fuzzy PID. The Optimal Fuzzy PID algorithm can automatically optimize by MATLAB program to obtain the optimal PID parameters and achieve the optimal control effect.

Relativ Relative Rate Obser e Rate Observer Self-T er Self-Tuning of F uning of Fuzzy PID Vir uzzy PID Virtual Iner tual Inertia Control for An Islanded microgrid

Expanding the usage of renewable energy in islanded microgrids leads to a reduction in its total inertia. Low inertia microgrids have difficulties in voltage and frequency control. That affected saving its stability and preventing a blackout. To improve low inertia islanded microgrids' dynamic response and save their stability, this paper presented relative rate observer self-tuning fuzzy PID (RROSTF-PID) based on virtual inertia control (VIC) for an islanded microgrid with a high renewable energy sources (RESs) contribution. RROSTF-PID based on VIC's success in showing remarkable improvement in the microgrid's dynamic response and enhancement of its stability. Moreover, it handles different contingency conditions successfully by giving the desired frequency support. Ant colony optimization (ACO) technique is used to find the optimal values of the RROSTF-PID based on VIC parameters. Furthermore, using MATLAB TM/Simulink, RROSTF-PID based on VIC response is compared to Optimal Fuzzy PID (OF-PID) based VIC, Fuzzy PID (F-PID) based VIC, PID-based VIC, conventional VIC responses, and the microgrid without VIC response under different operation conditions.

Application of optimized adaptive crow search algorithm based two degree of freedom optimal fuzzy PID controller for AGC system

This work demonstrates Automatic Generation Control (AGC) of two and three area reheat thermal system with nonlinearities. A Two Degree of Freedom Optimal Fuzzy Aided PID (2DOF OFAPID) controller is applied in all areas as secondary controller. The performance of proposed 2DOF OFAPID controller is validated through a comparative study with PID, 2DOF PID, fuzzy PID with filter-fractional order integral and OFAPID controllers. The pertinent parameters, Rules (Ri) and Membership Functions (MFs) are established by using Crow Search Algorithm (CSA) and an Optimized Adaptive CSA algorithm. The relevant flight length (fl) of each crow in CSA is optimized by Sine Cosine Algorithm (SCA) and is entitled as Sine Cosine Algorithm Optimized Adaptive Crow Search Algorithm (SCAOACSA). The primacy of suggested algorithm over CSA and SCA algorithms is demonstrated by solving 8 benchmark equations. From the comparative results, the SCAOACSA based 2DOF OFAPID controller is corroborated as admirable strategy to shorten the frequency and power deviations of interconnected power system.

Whale Optimization Algorithm Optimized Fuzzy-PID Plus PID Hybrid Controller for Frequency Regulation in Hybrid Power System

Whale Optimization Algorithm Optimized Fuzzy-PID Plus PID Hybrid Controller for Frequency Regulation in Hybrid Power System

Automated Generation Control of Multiple-Area Electrical System with an Availability-Based Tariff Pricing Scheme Regulated by Whale Optimized Fuzzy PID Controller

In this research, a whale-optimized fuzzy PID controller was developed to manage automatic generation control in multiple-area electrical energy systems with an availability-based tariff (ABT) pricing scheme. The objective of this work is to minimize the power production costs, area control errors (ACEs), and marginal costs of the multiple-area electrical energy system with real-time load and frequency variation conditions. The generation of power, deviation of power in the tie line, and deviation of frequency of the interconnected three-area electrical energy system, including the hydrothermal steam power plant and gas power plant, will be measured and analyzed rigorously. Based on the output from the whale optimization, the fuzzy PID controller regulates the deviation of power in the tie line and the deviation of frequency of the interconnected three-area electrical energy system. The reliability and suitability of the proposed optimization, i.e., whale-optimized fuzzy PID controller, are investigated against already presented methods such as particle swarm optimization and genetic algorithms.

Optimal Fuzzy PID Controller Design for Stability of FlexRay Bus in NCS

Optimal Fuzzy PID Controller Design for Stability of FlexRay Bus in NCS

Load Frequency control(LFC) of a Multiarea Restructured Hybrid Powersystem on Multi objective SSA

The aimof the paper tune the paramters of the load frequency controller using a latest and novel algorithm named as Salp sarm of algorithm with multiobjective approach. The test system choosen is a Two area interconnected hybrid power system under deregulated-environment integrated with Distributd genertion (DG) resource.The DG systems consists of Windturbine generator(WTG), SolarPV systems, Diesels engines generators(DEG), Fuelcells with Aqua electrolyzers and Energy storages like Batteries energy storage systems(BESS). To minimise the frequency of oscillations, Secondarycontroller opted was an optimal Fuzzy PID plus double integral controller (FPID-II). The effectiveness of proposed controller is determined with the comparison of nominal PI, PID and Two degree freedom PID (TDOFPID) controller. Furthermore the dynamic responses of SSA tuned FPID-II controller are been compared with other optimization techniques. The results depit the superiority of the proposed controller in suppressing the deviations of frequency

An Improved ACO Algorithm Optimized Fuzzy PID Controller for Load Frequency Control in Multi Area Interconnected Power Systems

In this paper, an Improved Ant Colony Optimization (IACO) algorithm optimized fuzzy PID (FPID) controller is proposed for Load Frequency Control of multi area systems. The nonlinear incremental evaporation rate and improvement of pheromone increment updating are proposed in the IACO algorithm to improve the quality of solution. And, a modified objective function using integral time multiply absolute error (ITAE), overshoot, undershoot and settling time with appropriate weight coefficients is proposed to improve the performance of the controller. Initially, a two-area non-reheat thermal system is applied and the FPID controller parameters are optimized by the IACO algorithm with five different objective functions. The modified objective function has better performances than four conventional objective functions. To demonstrate the robustness of the proposed control method, sensitivity analysis is implemented under wide variation of operating conditions and system parameters. Further, the proposed approach is also extended to two-area four-sources hydro thermal power system with/without High Voltage Direct Current (HVDC) link. The superiority of the proposed approach is shown by comparing the results with ZN, GA and hPSO-PS algorithms. The robustness of the proposed method is verified under random load disturbances. Finally, the proposed approach is extended to a two-area power system with governor dead band nonlinearity and results show that the proposed approach can cope with nonlinearity well. The results obtained from all simulations show that the proposed algorithm and modified objective function achieves better performances, such as minimum objective values (ITAE = 0.0255, ITSE = 9.30e-5, ISE = 1.91e-4 and IAE = 0.0273) obtained for the two-area non-reheat thermal system.

Optimal fuzzy-PID controller with derivative filter for load frequency control including UPFC and SMES

A newly adopted optimization technique known as sine-cosine algorithm (SCA) is suggested in this research article to tune the gains of Fuzzy-PID controller along with a derivative filter (Fuzzy-PIDF) of a hybrid interconnected system for the Load Frequency Control (LFC). The scrutinized multi-generation system considers hydro, gas and thermal sources in all areas of the dual area power system integrated with UPFC (unified power flow controller) and SMES (Super-conducting magnetic energy storage) units. The preeminence of the offered Fuzzy-PIDF controller is recognized over Fuzzy-PID controller by comparing their dynamic performance indices concerning minimum undershoot, settling time and also peak overshoot. Finally, the sensitiveness and sturdiness of the recommended control method are proved by altering the parameters of the system from their nominal values and by the implementation of random loading in the system.

A Novel Optimal Combined Fuzzy PID Controller Employing Dragonfly Algorithm for Solving Automatic Generation Control Problem

In this paper, a novel optimal combined fuzzy PID controller employing Dragonfly Algorithm (DA) was proposed for solving Automatic Generation Control (AGC) problem in interconnected power systems. The DA algorithm was employed in this work to optimize the controller parameters including the scaling factors of fuzzy logic and the PID gains. To show the potential of the proposed approach, initially, a two equal areas non-reheat thermal power system was investigated, and then the proposed approach was extended to a typical AC/DC two-area multisource power system connected via an AC tie-line as well as High Voltage Direct Current (HVDC) transmission link. The superiority of the proposed controller was proven by comparing the obtained results with the previous AGC techniques reported in the literature. Moreover, sensitivity analysis was done. Further, the proposed control strategy was also extended to a nonlinear three-area thermal power system model by considering the effect of appropriate Generation Rate Constraint (GRC) with nonlinearity in each area. Finally, the results were tabulated in terms of settling time and peak overshoot. The comparative study demonstrates the effectiveness of the proposed control strategy and shows its robustness to enhance frequency regulation loop of linear and nonlinear interconnected power systems.

Load Frequency Control of Multi Source Multi-Area Nonlinear Power System with DE-PSO Optimized Fuzzy PID Controller in Coordination with SSSC and RFB

Load Frequency Control of Multi Source Multi-Area Nonlinear Power System with DE-PSO Optimized Fuzzy PID Controller in Coordination with SSSC and RFB

Application of multi-verse optimiser-based fuzzy-PID controller to improve power system frequency regulation in presence of HVDC link

This paper presents the design of a novel optimal fuzzy-PID controller-based multi-verse optimiser (MVO) for load frequency control (LFC) of a two-area power system interconnected via high voltage direct current (HVDC) transmission link. The MVO algorithm was adopted to estimate the unknown parameters of the test system and model the HVDC link for the LFC analysis, and then, was used to optimise the fuzzy-PID controller parameters including the scaling factors of fuzzy logic and the PID controller gains. To demonstrate the effectiveness of the proposed control strategy, a two-area power system with HVDC link connection was investigated for the simulation. A comparative study of performance of proposed controller, fuzzy logic and conventional PID controller was performed. The obtained results satisfy the LFC requirements and reveal that the optimised fuzzy-PID controller-based MVO algorithm enhances power system frequency regulation in presence of HVDC link.

Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor

In this paper, Antlion algorithm optimized Fuzzy PID supervised on-line Recurrent Fuzzy Neural Network based controller is proposed for the speed control of Brushless DC motor. Learning parameters of the supervised on-line recurrent fuzzy neural network controller, i.e., learning rate (η), dynamic factor (α), and number nodes (Ni) are optimized using Genetic algorithm, Particle Swarm optimization, Ant colony optimization, Bat algorithm, and Antlion algorithm. The proposed controller is tested with different operating conditions of the Brushless DC motor, such as varying load conditions and varying set speed conditions. The time domain specifications such as rise time, overshoot, undershoot, settling time, recovery time, and steady state error and also integral performance indices such as root mean square error, integral of absolute error, integral of squared error, and integral of time multiplied absolute error are measured and compared for above optimized controller. Simulation results show Antlion algorithm optimized Fuzzy PID supervised on-line recurrent fuzzy neural network based controller has proved to be superior than other considered controllers in all aspects. In addition, the experimental verification of proposed control system is presented to test the effectiveness of the proposed controller with different operating conditions of the Brushless DC motor.

Application of Hybrid Differential Evolution–Grey Wolf Optimization Algorithm for Automatic Generation Control of a Multi-Source Interconnected Power System Using Optimal Fuzzy–PID Controller

This paper deals with an optimal hybrid fuzzy-Proportional Integral Derivative (fuzzy-PID) controller optimized by hybrid differential evolution–Grey Wolf optimization algorithm for automatic generation control of an interconnected multi-source power system. Here a two area system is considered; each area is provided with three types of sources namely a thermal unit with reheat turbine, a hydro unit and a gas unit. The dynamic performance of the system is analyzed under two cases: with AC tie-line and with AC-DC tie-line. The efficiency and effectiveness of the proposed controller is substantiated equally in the two cases. The sturdiness of the system is proved by varying the values of the system parameters. The supremacy of the recommended work is additionally ascertained by comparison with the recently published results like differential evolution optimized PID Controller and hybrid Local Unimodal Sampling-Teaching Learning based Optimization (LUS-TLBO) optimized fuzzy-PID controller. The dynamic performance of the system is observed in terms of settling time, peak overshoot and peak undershoot. Finally the analysis is extended by applying the proposed control technique in two different models namely (i) A three area unequal thermal system considering proper generation rate constraints (GRC) and (ii) A three area hydro-thermal system with mechanical hydro governor. These test results reveal the adaptability of the proposed method in multi-area interconnected power system.

Design and Evaluation of an Optimal Fuzzy PID Controller for an Active Vehicle Suspension System

Design and Evaluation of an Optimal Fuzzy PID Controller for an Active Vehicle Suspension System

An optimal fuzzy PID control approach for docking maneuver of two spacecraft: Orientational motion

This paper describes a scheme for a Fuzzy-Proportional Integral Derivative (FPID) controller based on genetic algorithm (GA), in a docking maneuver of two spacecraft. The docking maneuver consists of two parts: translation and orientation. Euler’s gyroscopic equation is applied to obtain governing equations of orientational phase. Here, a designed fuzzy-PID controller for stabilization purpose of orientational phase of a docking maneuver is presented based on the Single Input Fuzzy Inference Motor (SIFIMs) dynamically connected Preferrer Fuzzy Inference Motor (PFIM). This fuzzy-PID controller takes the error signal of Euler’s angles and the error of angular velocities of the chaser as its input items, and the driving force as its output. The parameters of the controller are ascertained by using a genetic algorithm. Conflicting objective functions (which their 3D pareto frontiers are obtained by Multi-objective Genetic Algorithm (MOGA)) are distance errors from the set point, angle errors from the set point, and control efforts. Optimization constraint is maximal of the momentum produced by momentum wheels. The result of optimum point demonstrates that the designed controller makes an efficient performance in the orientational phase of the chaser spacecraft. Compared to similar works, some of system parameters like settling time are improved and overshoot (as a critical parameter in docking maneuver) is decreased.