Optimal Fuzzy PID Controller Research Articles

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.

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.

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.

Hybrid alopex based DECRPSO algorithm optimized Fuzzy-PID controller for AGC

This paper is corroborated the hybrid Alopex based DECRPSO algorithm (ADECRPSO) over DE, ADE, PSO, and CRPSO algorithms to pursuit the gain parameters of the PID and Fuzzy PID (FPID) controller. In a two area thermal-hydro-diesel power system, primacy of FPID controller is endorsed with PID controller tuned with assorted techniques. The hybrid ADECRPSO algorithm is affirmed over above mentioned algorithms to tune PID controller in a two area hydro-thermal system. PSO, DE, CRPSO, ADE and ADECRPSO are executed individually to optimize the controller to enhance the transient analysis by conceding undershoot, overshoot, and settling time of the system. The compilation of advantages of alopex based DE and craziness based PSO causes an adequate hybrid algorithm which enhances the performance of Automatic Generation Control (AGC). The step load uprise in area-1 is imposed to observe the activities of AGC. Undeniably, FPID controller optimized by ADECRPSO commits superior performance over PSO, DE, CRPSO, and ADE optimized controller as proposed AGC system. So, the modified mutation of DE by alopex scheme enhances the potentiality to tune the system variables.

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 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.

Optimal fuzzy PID controller design for an active magnetic bearing system based on adaptive genetic algorithms

We propose an adaptive genetic algorithm (AGA) for the multi-objective optimisation design of a fuzzy PID controller and apply it to the control of an active magnetic bearing (AMB) system. Unlike PID controllers with fixed gains, a fuzzy PID controller is expressed in terms of fuzzy rules whose consequences employ analytical PID expressions. The PID gains are adaptive and the fuzzy PID controller has more flexibility and capability than conventional ones. Moreover, it can be easily used to develop a precise and fast control algorithm in an optimal design. An adaptive genetic algorithm is proposed to design the fuzzy PID controller. The centres of the triangular membership functions and the PID gains for all fuzzy control rules are selected as parameters to be determined. We also present a dynamic model of an AMB system for axial motion. The simulation results of this AMB system show that a fuzzy PID controller designed using the proposed AGA has good performance.

Modeling and Co-Simulation of Stop and Go Cruise Control System

Vehicle stop and go frequently can cause driver fatigue, even traffic accidents. In order to mitigate driver fatigue and the risk of rear-end collision accidents, a set of hierarchical vehicle stop and go cruise control system(S&G) was established. A vehicle dynamics model was created with Carsim, the determination of safety distance was based on headway method. Then upper controller and lower controller were designed separately based on optimal control and fuzzy PID control principle. Finally, a simulation loop was built in Simulink. Through a co-simulation conducted under typical S&G driving condition, got speed change curves,acceleration change curves and relative distance change curves of host vehicle while following the target vehicle. The results show that the S&G system designed can make host vehicle well follow the speed change of leading vehicle while keeping an appropriate safety distance, stop and go automatically, reduce driver fatigue and improve vehicle active safety.

Multi-Objective Optimal Fuzzy Fractional-Order PID Controller Design

The design of the optimal fuzzy fractional-order PID controller is addressed in this work. A multi-objective genetic algorithm is proposed to design rule base and membership functions of the fuzzy logic systems. Three conflicting objective functions in both time and frequency domains have been used in Pareto design of the fuzzy fractional-order PID controller. The simulation results reveal the effectiveness of the proposed method in comparison with the results produced by the fractional-order PID controllers.

Using optimal fuzzy logic controller to improve transient fluctuations in deregulated power system with random variable load

In power system any unpredictable variations of consumption load will cause fluctuation in frequency and the transferred power between the lines. Load frequency control is one of the main problems in the power system which synchronizes with automatic generation control. Checking the different factors that cause unbalance in power system is necessary and one of the important factors of them is the load that randomly varies. In this paper, a three area power system in deregulated environments with participation of synchronized generation of wind with Doubly Fed Induction Generator (DFIG), hydro and thermal power plants are studied. The load in this power system is considered as a random variable which periodically and randomly has frequency of 0.25 around nominal amount. Fluctuations are caused due to considering load as a random variable and using wind-powered turbine with DFIG in power system. To improve the response of power system, the optimal fuzzy logic, PID controller and FACTS devices will be used. Imperialist Competitive Algorithm (ICA) has been used for optimizing the parameters of a fuzzy logic controller. PID Controller parameters have been optimized with three intelligent algorithms, ICA, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Algorithm and the results will be compared with each other. With comparison the result it is observed that in the case of using optimal fuzzy logic controller (OFLC) we have the fastest response with the minimum over shoot and under shoot, also in this case the transferred power between the areas is settled in the lowest value. The results show a significant reduction in losses in transmission lines.

Design of the Intelligent Control System for Mine Hoist

Traditional mine hoists have the characteristics of using more meters, complex operation and low production efficiency. To solve these problems, this article introduces an intelligent control system for mine hoist. In the system the industrial fieldbus is combined with the wireless network, the communication protocol of a point to multipoint is developed to realize wireless data collection and wireless data transmission between industrial computer with monitoring computers. For the speed control, a compound control algorithm of ant colony algorithm for optimal control and fuzzy self-adaptive PID control is presented to control hoist operation. Actual application shows that the system worked well.

Application of electrolyzer to alleviate power fluctuation in a stand alone microgrid based on an optimal fuzzy PID control

Due to the high intermittent power generations from wind and photovoltaic in the microgrid (MG) system, these result in the severe power fluctuation. When the fuel cell (FC) equipped with the aqua electrolyzer (AE) has been installed in the MG, in addition to hydrogen production for FC, the absorbed power by AE can be controlled to alleviate the power fluctuation. This paper proposes the coordinated control of AE and FC to solve the power fluctuation problem in the MG. By control of the power absorption by AE and the power production by FC, the power fluctuation in the MG can be suppressed. The optimal fuzzy logic based-proportional-integral-derivative (FLPID) is used to design the controllers of AE and FC. Without trial and error as in the conventional FLPID controller design, scale factors, membership functions and control rules of the optimal FLPID controller are automatically and simultaneously tuned by a bee colony optimization. Simulation results confirm the superior stabilizing effect of the proposed optimal FLPID controller in comparison with the conventional FLPID controller under several system disturbances.

A PSO-based adaptive fuzzy PID-controllers

In this paper, a novel design method for determining the optimal fuzzy PID-controller parameters of active automobile suspension system using the particle swarm optimization (PSO) reinforcement evolutionary algorithm is presented. This paper demonstrated in detail how to help the PSO with Q-learning cooperation method to search efficiently the optimal fuzzy-PID controller parameters of a suspension system. The design of a fuzzy system can be formulated as a search problem in high-dimensional space where each point represents a rule set, membership functions, and the corresponding system’s behavior. In order to avoid obtaining the local optimum solution, we adopted a pure PSO global exploration method to search fuzzy-PID parameter. Later this paper explored the improved the limitation between suspension and tire deflection in active automobile suspension system with nonlinearity, which needs to be solved ride comfort and road holding ability problems, and so on. These studies presented many ideas to solve these existing problems, but they need much evolution time to obtain the solution. Motivated by above discussions this paper propose a novel algorithm which can decrease the number of evolution generation, and can also evolve the fuzzy system for obtaining a better performance.