Hybrid Fuzzy PID Controller Research Articles

Robust Fuzzy-PID Technique for the Automatic Generation Control of Interconnected Power System with Integrated Renewable Energy Sources

This paper proposes a robust hybrid fuzzy PID controller, with the PID gain values tuned using the particle swarm optimization (PSO) technique for the automatic generation control (AGC) of the two-area hybrid power system. PSO Algorithm is applied to reduce the integral of time-weighted absolute error (ITAE) of the frequency and tie-line power variations of the two-area interconnected power system. The suggested method’s efficacy is examined on an interconnected two-area power system with Area 1 containing thermal reheat plants, hydropower plants, and diesel generating units, and Area 2 comprising thermal reheat plants and renewable energy sources (RES): wind energy conversion system, solar photovoltaic system, and electric vehicles. The thermal reheat power plants are modelled taking into consideration the governor dead band (GDB) and the generation rate constraints (GRC). The performance of the presented controller is compared with the genetic algorithm-optimized PID AGC and the PSO-optimised PID AGC. The simulation outcomes show superior results of the proposed controller against other controllers. Further, sensitivity analysis reflects that the proposed technique provides better dynamic response and robustness than the other techniques.

Automatic Generation Control of a Multi-Area Hybrid Renewable Energy System Using a Proposed Novel GA-Fuzzy Logic Self-Tuning PID Controller

Human activities overwhelm our environment with CO2 and other global warming issues. The current electricity landscape necessitates a superior, continuous power supply and addressing such environmental concerns. These issues can be resolved by incorporating renewable energy sources (RESs) into the utility grid. Thus, this paper presents an optimized hybrid fuzzy logic self-tuning PID controller to control the automatic generation control (AGC) of various renewable sources. This controller regulates the frequency deviations of the power system and governs the change in the tie-line load of a multi-area hybrid energy system composed of wind, biomass, and photovoltaic energy sources. MATLAB Simulink software was applied to design and test the system. The PID controller has been tuned using four algorithms, namely, genetic algorithm (GA), pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO), and we compared the results with the proposed novel optimized PID controller (GA-fuzzy logic self-tuning technique) to validate it. The results show the superiority of the proposed hybrid GA-fuzzy logic self-tuning algorithm over the other algorithms in bringing the power system back to its regular operation. The paper also proposes an operation strategy to lower the utilization of biomass energy in the presence of other renewable energy sources.

Model Based Controller Design for Hydrogen Fuel Cell Systems

In this paper, fuel cell system modelling alongside controller development for performance improvements, are investigated. Essentially and in general terms there are two approaches to modelling; black box modelling and detailed dynamic modelling. This work addresses both modelling approaches. These models are then used for hydrogen fuel cell (FC) controller development for improved system performance. A fuzzy-PID hybrid controller has been designed and tested and the results indicate that the fuzzy-PID hybrid control strategy can improve the system’s performance significantly.

Smart Dc to DC converter for a Small Drone Based upon Deep Learning Technique

Flying a small robot like a drone needs more conservative energy, small solar cell size needs to robust efficient DC-to-DC converter. Mathematical analysis for finding the optimal duty cycle in the PWM (pulse width modulation) is presented at different supplies voltage, considering a low voltage of 8 volts for low-speed motor rotation speed and 18 volts for high speed of the rotation motor. The dc-dc converter gets the power desired from the small robotic by using a dc-dc buck-boost converter with (PID control, smart tuning different rules fuzzy logic control, and a hybrid fuzzy-PID controller as one solution to be improved the controller to be capable on process any disturbances on the inductor or the desired value of the output voltage of the dc-dc converter

Self-tuning fuzzy PID speed controller for quarter electric vehicle driven by In-wheel BLDC motor and Pacejka's tire model

Vehicles using internal combustion engine (ICE) adversely affect the environment. Therefore, all vehicle makers have started their mutation toward electric vehicles. In this paper, a quarter vehicle model is developed considering brushless DC (BLDC) motor actuation. The longitudinal behavior of the drive wheel is modeled using the Pacejka model or magic formula. This model describes the interaction between the tire and the road surface, which reflects road condition effect. The purpose of the control is to provide smooth speed control in both acceleration and deceleration modes, regardless of changes in road conditions. Due to the high complexity of the control model, a hybrid fuzzy-PID controller is designed to serve as a speed controller for the system motor-tire-road-surface. It is checked by numerous simulations that the proposed controller performs well, ensuring comfortable steering operation despite road characteristic changes.

Development and Performance Study of Temperature and Humidity Regulator in Baby Incubator Using Fuzzy-PID Hybrid Controller

Technological developments in the health sector for the safety of neonates are essential. Such efforts are needed to curb the increase in premature infant mortality cases caused by bacteria, asphyxia, infections, and poor management of facility equipment. Furthermore, preterm and other at-risk babies have low ability to regulate temperature and produce body heat as characterized by their dry skin conditions; hence, the need for baby incubators. For their operation, these baby incubators provide strict regulated energy change that is influenced by heat transfer caused by the surrounding atmospheric temperature and humidity. This paper presents the design, construction, and performance study of a proposed Fuzzy-PID hybrid control system for regulating temperature and humidity in a baby incubator. To accomplish its goal, the proposed controller must be able to distribute heat and maintain humidity in the incubator under fluctuating atmospheric conditions to keep the baby’s body warm. Performance tests of the proposed hybrid controller were conducted by comparing temperature and humidity outputs in the baby incubator against predetermined expected values. Results show that the proposed controller is able to successfully achieve and maintain the temperature and humidity set points. Further examination also suggests that the proposed Fuzzy-PID hybrid control offers an improved overall system response performance compared to the PID controller.

Research on a high-accuracy and high-pressure pneumatic servo valve with aerostatic bearing for precision control systems

In this paper, a novel single-stage high-pressure electro-pneumatic servo valve (HESV) directly driven by a voice coil motor (VCM) is proposed for a precision control system. The HESV is a three-position three-way spool-type flow valve. As the friction force of the spool increases, the clearance between the spool and sleeve decreases. The aerostatic bearing is designed to reduce the friction wear and improve the motion sensitivity of the spool. A nonlinear mathematical model of the HESV is derived including the electromagnetic subsystem, dynamic force balance equation, and so on. Furthermore, considering the highly nonlinear steady gas flow force, a hybrid fuzzy logic PID controller with a disturbance observer (DOB) is adopted to improve the performance of the HESV. In this study, a numerical analysis and experiments are carried out, and their results indicate that the static friction force of the developed HESV decreases from 15 N to 4.4 N because of the aerostatic bearing. The developed HESV exhibits a strong anti-interference performance because of the hybrid controller with the DOB; the valve spool can be recovered to the original control position rapidly within 0.03 s. The position control accuracy of the novel HESV is greatly improved. Thus, the HESV can be applied to high-pressure pneumatic precision control systems.

Design and control of a MRI-compatible pneumatic needle puncture robot

Percutaneous needle puncture operation is widely used in the image-guided interventions, including biopsy and ablation. MRI guidance has the advantages of high-resolution soft tissue imaging and thermal monitoring during energy-based ablation. This paper proposes the design of a 5-DOF pneumatic needle puncture robot, with all the cylinders, sensors and structure material MRI-compatible. Also, a hybrid fuzzy-PID controller is designed for the pneumatic driven system to adjust the PID parameters adaptively. The experiment validation result shows that, compared with the traditional fix-parameter PID control, the proposed hybrid fuzzy-PID control has no overshoot, and the settle time/steady state error remains low even with increasing load. This proves that the hybrid fuzzy-PID control strategy can increases the positioning accuracy and robustness of the pneumatic driven needle puncture robot, which is significant for the safety of percutaneous needle puncture operation.

A hybrid fuzzy-PID controller based on gray wolf optimization algorithm in power system

This paper addresses a grey wolf optimizer to optimally set the PID controller with low pass filter for simulating multi area restructured power system with considered superconducting magnetic energy storage plant that works under various loading conditions and uncertainties. To make a real model, the reheat effect nonlinearity of the steam turbine and governor is considered into automatic generation control (AGC) model. The proposed AGC model is formulated as a nonlinear constrained optimization problem which is solved by an intelligent algorithm so that it shows powerful search in optimization problem especially the proposed AGC. The effectiveness of the proposed technique is demonstrated on a deregulated power system with possible contracted scenarios under large load demand and area disturbances in comparison with other method through several performance indices. Obtained results show that the proposed control strategy achieves good robust performance for a wide range of system parameters and load changes in the presence of system nonlinearities and is superior to the other controllers.

Hybrid fuzzy-PID control of a nuclear Cyber-Physical System working under varying environmental conditions

In real-time control of Cyber-Physical Systems (CPSs), physical process variables are monitored and processed by intelligent controllers for keeping the values of safety parameters between given thresholds. Environmental conditions can affect the system dynamics and also the controller function. This paper presents a hybrid fuzzy-PID (Proportional-Integral-Derivative) controller, which learns the optimal PID controller parameters and adapts them according to the environmental parameters and process variables values.The proposed intelligent controller is applied to respond to the changes and transients in the environmental inlet air temperature of the secondary loop of a Lead-Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS), for keeping the secondary average coolant temperature in a range of values between two safety thresholds.

Fuzzy adaptive control system of a non-stationary plant with closed-loop passive identifier

Typically chemical processes have significant nonlinear dynamics, but despite this, industry is conventionally still using PID-based regulatory control systems. Moreover, process units are interconnected, in terms of inlet and outlet material/energy flows, to other neighboring units, thus their dynamic behavior is strongly influenced by these connections and, as a consequence, conventional control systems performance often proves to be poor. This paper proposes a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller, also exploiting the results coming from an identification procedure that is carried on when an unmeasured step disturbance of any shape affects the process behavior. In addition, this paper compares a fuzzy logic based PID with PID regulators whose tuning is performed by standard and well-known methods. In some cases the proposed tuning methodology ensures a control performance that is comparable to that guaranteed by simpler and more common tuning methods. However, in case of dynamic changes in the parameters of the controlled system, conventionally tuned PID controllers do not show to be robust enough, thus suggesting that fuzzy logic based PIDs are definitively more reliable and effective.

An Electro-Hydraulic Servo with Intelligent Control Strategy

Versatile engineering applications have been developed to assist, reduce, and avoid human being from any heavy or harmful manufacturing processes. The gradually increased demand in force and position controls have simultaneously increased the usage of Electro-Hydraulic Servo (EHS) system. However, the time varying characteristics such as high-speed, outburst starting and stopping dynamic have led the EHS system to suffer from uncertainties and nonlinearities effects. Therefore, in order to enhance the performance of an EHS to surmount the uncertain and nonlinear effects, a hybrid Fuzzy-PID control strategy is developed which particularly improve the accuracy of the system by enhancing the control performance during the positioning tracking. By measuring the performance of the proposed control approach, the transient response and steady-state analysis will be performed which taking linear and intelligent control strategies as the references in the assessment process. The finding indicates the capability of a hybrid Fuzzy-PID controller in reducing the control effort applied to the EHS system.

Speed Control of Permanent Magnet Brushless DC Motor Using Hybrid Fuzzy Proportional plus Integral plus Derivative Controller

The main core of this work is to investigate two different digital controllers (Conventional PID Controller and Hybrid Fuzzy PID) on closed loop control of Permanent Magnet Brush Less Direct Current (PMBLDC) Motor. The application of intelligent control (fuzzy logic) technique is attempted in a Proportional –Integral- Derivative (PID) controller for tuning their parameters automatically in an online process. In-order to achieve desired speed, Hybrid Fuzzy-PID provides the finest set of solutions. The conventional PID controller and Hybrid Fuzzy-PID controller performances are analyzed both in steady state and dynamic operating condition with various set point speeds. The rise time, dead time, settling time and steady state error are the parameters considered for comparison. The results prove that, the Hybrid Fuzzy-PID controller offers better performance (Improvement in rise time, Reduction in settling time, No steady sate error) over the conventional PID controller. The simulation makes use of LabVIEW Fuzzy–PID tool and it is implemented in real time through NI-DAQ 6009 with PMBLDC motor which is rated at 36V, 4A, 4000 RPM.

Fuzzy PID controller for optoelectronic stabilization platform with two-axis and two-frame

A hybrid fuzzy-PID controller scheme is proposed to maintain line of sight (LOS) stabilize against carriers’ disturbances and achieve a higher tracking precision. The control model is established based on two-axis and two-frame utilizing the rotation transformation and Euler equation. Membership functions (MFs) between E and EC of proposed hybrid controller are superior to the ordinary MFs, whether the number of MFs or the types of MFs. Experiments validate that proposed hybrid controller outperforms the conventional PID control scheme.

Novel hybrid fuzzy-PID control scheme for air supply in PEM fuel-cell-based systems

This paper proposes a novel hybrid fuzzy-PID controller for air supply on Proton Exchange Membrane fuel cell (PEMFC) systems. The control objective is to adjust the oxygen excess ratio at a given setpoint in order to prevent oxygen starvation and damage of the fuel-cell stack. The proposed control scheme consists of three parts: a fuzzy logic controller (FLC), a fuzzy-based self-tuned PID (FSTPID) controller and a fuzzy selector. Depending on the value of the error between the current value of oxygen excess ratio and its setpoint value, the fuzzy selector decides which controller should play the greatest effect on the control system. The performance of the proposed control strategy is analysed through simulations for different load variations and for parameter uncertainties. The results show that the novel hybrid fuzzy-PID controller performs significantly better than the classical PID controller and the FLC in terms of several key performance indices such as the Integral Squared Error (ISE), the Integral Absolute Error (IAE) and the Integral Time-weighted Absolute Error (ITAE), as well as the overshoot, settling and rise time for the closed-loop control system.

A GSA-based adaptive fuzzy PID-controller for an active suspension system

Purpose – The purpose of this paper is to propose a novel design for determining the optimal hybrid fuzzy PID-controller of an active automobile suspension system, employing the gravitational search algorithm (GSA). Design/methodology/approach – The hybrid fuzzy PID-controller structure is an improvement to fuzzy PID-controller by incorporating a fast learning PID-controller. Findings – The GSA can adjust the parameters of the PID-controller to achieve the optimal performance. Research limitations/implications – The GSA may have the advantage of quick convergence, but the required computation may be intensive. Practical implications – The simulation results demonstrate the effectiveness of the proposed approach on active automobile suspension system. Originality/value – In order to demonstrate the theoretical guarantee of the proposed method, comparisons with particle swarm optimization or other methods has also been carried out.

Passive Identification is Non Stationary Objects With Closed Loop Control

Typically chemical processes have significant nonlinear dynamics, but despite this, industry is conventionally still using PID-based regulatory control systems. Moreover, process units are interconnected, in terms of inlet and outlet material/energy flows, to other neighbouring units, thus their dynamic behaviour is strongly influenced by these connections and, as a consequence, conventional control systems performance often proves to be poor. However, there a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller, also exploiting the results coming from an identification procedure that is carried on when an unmeasured step disturbance of any shape affects the process behaviour. This paper presents procedure for identifying technological object control in a closed loop, i. e. that operates the automated control system. The variation in the controlled variable, caused by the change of the nonmeasurable disturbance, is considered the initial signal for the identification procedure. The parameters of the control object are found by optimization method Levenberg-Marquardt.

Fuzzy Adaptive Control System of a Non-Stationary Plant

This paper proposes a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller. In addition, this paper compares a fuzzy logic based PID with PID regulators whose tuning is performed by standard and well-known methods. In some cases the proposed tuning methodology ensures a control performance that is comparable to that guaranteed by simpler and more common tuning methods. However, in case of dynamic changes in the parameters of the controlled system, conventionally tuned PID controllers do not show to be robust enough, thus suggesting that fuzzy logic based PIDs are definitively more reliable and effective.

Load frequency control of power system under deregulated environment using optimal firefly algorithm

Abstract In this paper, a novel Firefly Algorithm (FA) optimized hybrid fuzzy PID controller with derivative Filter (PIDF) is proposed for Load Frequency Control (LFC) of multi area multi source system under deregulated environment by considering the physical constraints such as Generation Rate Constraint (GRC) and Governor Dead Band (GDB) nonlinearity. As the effectiveness of FA depends on algorithm control parameters such as randomization, attractiveness, absorption coefficient and number of fireflies are systematically investigated, the control parameters of FA are tuned by carrying out multiple runs of algorithm for each control parameter variation then the best FA control parameters are suggested. Additionally, the superiority of the FA is demonstrated by comparing the results with tuned Genetic Algorithm (GA). To investigate the effectiveness of the proposed approach, time domain simulations are carried out considering different contracted scenarios and the comparative results are presented. Further, sensitivity analysis is performed by varying the system parameters and operating load conditions. It is observed from the simulation results that the designed controllers are robust and the optimum gains of proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters. Finally, the effectiveness of the proposed control scheme is evaluated under random step load disturbance.

Fuzzy adaptive control system of a non-stationary plant with closed-loop passive identifier

Abstract Typically chemical processes have significant nonlinear dynamics, but despite this, industry is conventionally still using PID-based regulatory control systems. Moreover, process units are interconnected, in terms of inlet and outlet material/energy flows, to other neighboring units, thus their dynamic behavior is strongly influenced by these connections and, as a consequence, conventional control systems performance often proves to be poor. This paper proposes a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller, also exploiting the results coming from an identification procedure that is carried on when an unmeasured step disturbance of any shape affects the process behavior. In addition, this paper compares a fuzzy logic based PID with PID regulators whose tuning is performed by standard and well-known methods. In some cases the proposed tuning methodology ensures a control performance that is comparable to that guaranteed by simpler and more common tuning methods. However, in case of dynamic changes in the parameters of the controlled system, conventionally tuned PID controllers do not show to be robust enough, thus suggesting that fuzzy logic based PIDs are definitively more reliable and effective.