Fuzzy Logic PID Research Articles

Optimizing BLDC Motor Performance with Advanced Fuzzy Logic PID Controller under Dynamic Load Condition

Optimizing BLDC Motor Performance with Advanced Fuzzy Logic PID Controller under Dynamic Load Condition

Fuzzy Logic and Neural network-based self-tuned PID controller of Quadcopter

Quadcopters have been established as a viable tool for the current era of technology. Quadcopters are also known as drones or unmanned aerial vehicles. The quadcopter can be used for aerial surveys for deliveries, filming, and missions such as detecting and mapping unreachable areas, hills, forests metropolitan areas, rocks, and obstructions that can be a danger or risk to navigation for commercial and security projects. It conducts its survey mission without human operator intervention. After the mission is accomplished, data can be fetched by GPS or the Quadcopter returns to a pre-programmed location where the data can be downloaded and processed. It can lead to an improvement in the position tracking performance due to an intrinsic change in the object's weight, buoyancy, and aerodynamic forces. In addition, quadcopters must navigate the extremely dynamic airborne environment that is represented by urban, rural, metropolitan, or natural environments. In light of this, efficient dock tracking is required to attain the requisite high precision performance, resulting in less time consumption, when the system's dynamic characteristics are time-dependent or the operating conditions and vision guidance alter. The automatic identification of objects by classifying images and self-tuning the gains of a PID (Proportional + Integral + Derivative) controller has been proposed in this paper. The object recognition is based on machine learning guided vision and self-tuning PID controller based on fuzzy logic and neural network.

Comparing Temperature and Humidity Control Using PID and Fuzzy Logic in a Climatic Chamber

The presence of a thermohygrometer is important in some places, especially in hospitals and climate room equipment. A climate chamber is an enclosed space or isolated environment, which will provide the environmental conditions of relative humidity and temperature. In accordance with the Decree of the Minister of Health of the Republic of Indonesia. Certain rooms such as rooms in hospitals require special attention to environmental conditions, such as the surgical process that occurs in the operating room. A thermohygrometer is a tool used to monitor room conditions. The thermoygrometer used must be able to trace the measurement results using certain media. A climate chamber is a device that provides the desired climate regardless of the external environment. The purpose of this study was to analyze the comparison of PID and Fuzzy Logic temperature control systems on the Climatic Chamber (Fuzzy Logic System) device which plays a role in the process of measuring room temperature and humidity in the field. The method used in this study was to compare directly with a previously calibrated thermohygrometer. The measurement results at 25°C have a response time of 7 minutes 30 seconds and an overshoot of 0.1°C, at a temperature of 30°C has a response time of 5 minutes 15 seconds and an overshoot of 0.1°C, at a temperature of 35°C has a response time of 5 minutes 30 seconds and 0.2°C overshoot. At 50%RH Humidity has a response time of 13 minutes 30 seconds, at 60%RH Humidity has a 12 minute response time, At 70%RH Humidity has a 6 minute response time. The measurement results show that fuzzy logic control has more advantages than PID control. Fuzzy logic control has a faster response time to setpoint than PID control and fuzzy logic control has smaller overshoot compared to PID control.

An innovative fuzzy logic frequency regulation strategy for two-area power systems

Modern environmentally friendly power system designs offer several application benefits, but they also generate losses. In order for this structured power system to operate reliably, the total generation, total load demand, and system losses must be balanced. Changes in load demand disrupt both the real and reactive power balances. As a result, the system frequency and tie-line interchange power differ from their planned values. A large variance in system frequency can cause the system to crash. Multiple connected area systems use clever load frequency control techniques in this scenario to deliver dependable and high-quality frequency and tie-line power delivery. In this case, a freestanding hybrid power system is considered, with generated power and frequency intelligently managed. In addition to the unpredictability of the wind, frequent changes in the load profile can result in significant and damaging power variations. The output power of such renewable sources may fluctuate to the point where major frequency and voltage variations occur in the system. The fuzzy logic PID controller (FLPIDC) is an intelligent approach recently proposed to address the load frequency control (LFC) issue of an interconnected power system. Standard proportional integral derivative (PID) controllers operate each section of the system.

Optimal Fuzzy Logic Proportional Integral Derivative Controller Design by Bee Algorithm for Hydro-Thermal System

This paper proposes a robustness and an optimum of the fuzzy logic-proportional integral derivative (FLPID) controllers for automatic generation control (AGC) of a two area interconnected hydro-thermal system and an application of bee algorithm (BA) in order to design FLPID controllers. Traditionally, membership functions (MF) and control rules (CR) of the fuzzy logic controller (FLC) were achieved by trial and error methods of designers. To solve this problem, BA was proposed to concurrently tune PID gains, MF and CR of the FLPID controllers to minimize frequency deviations and tie-line power deviations of power system. Simulation results clearly showed that the performance and the robustness of the optimal FLPID controllers were superior to the conventional PID and the FLPID controllers in terms of settling time and overshoot.

Comparative Study of PID, PD-FLC and PID-FLC for Active Magnetic Bearing

In this manuscript, a closed loop active magnetic bearing system (AMB) is proposed, dynamically modelled and linearized in form of unstable transfer function. To achieve proper bearing operation, the proposed AMB system is controlled by two separate controllers, one to control the current in the electromagnet coils by forming an inner closed loop and second to stabilize the position of the suspending object at equilibrium. To maintain the position of suspending object at equilibrium, for the proposed AMB system (considering inner closed loop as unity) a conventional PID controller, a PD-fuzzy logic controller (PD-FLC) and a PID-fuzzy logic controller (PID-FLC) is designed and their performance is observed and compared. Later, the effect of designed controllers on the complete proposed AMB system is studied with the help of control system plots and improvement among their performances is observed. The plotted step responses and calculated transient state parameters will verify that by changing the conventional PID controller to a PID-FLC could result in 48.34% improvement in overshoot, 41.52% increment in speed of response and 32.23% increment in the relative stability.

Performance analysis of PID controller and fuzzy logic controller for DC-DC boost converter.

Demand of Power electronics converter is increased due to the rapid increase of green re-sources. The distributed nature of uncontrolled renewable energy resources are connected with the grid via power electronics converter. DC-DC converters are high-frequency current conversion circuits that use transformers, inductors, and capacitors to smooth switching noise into regulated DC voltages. In this study, a comparative performance analysis of Proportional integral Derivative (PID) and fuzzy logic controller (FLC) for DC-DC Boost Converter is presented. PID-based controller for dynamic boosting of DC-DC boost converter was replaced by Fuzzy Inference system (FIS) to study the system behavior. Five different membership functions were performed to observe the response of the system for DC -DC boost converter with FIS controller. The performances of FIS-based controller and PID-based controller for DC-DC boost converter were compared in detail. Also, the proposed techniques for the switching mode of DC-DC converter have been applied. The performance analysis of both PID and fuzzy controller was simulated using MAT LAB. The results of FIS based approach are better than PID based controller in terms of transient responses.

Trajectory control of a hydraulic system using intelligent control approach based on adaptive prediction model

Electro-hydraulic actuators (EHAs) have become a preferred alternative to traditional hydraulic actuators with valve control systems due to their numerous advantages, making them an ideal choice for applications requiring high-precision force or position control. However, the highly complex nonlinear nature of EHAs makes modelling and controlling them a challenging task. To address this challenge, a new position control approach has been proposed for an EHA system using a combination of a feedforward online-tuning PID (FOPID) controller and an adaptive grey predictor (AGP), known as the feedforward online-tuning adaptive grey predictor (FOAGP). The FOPID controller is constructed based on PID controller and fuzzy logic algorithm to control the EHA system towards referred trajectory, while an updating rule that consists of robust checking terms optimizes its parameters online to minimize control error. The AGP predictor is an important aspect of the proposed approach. It consists of a self-tuning step size mechanism, which estimates the performance of the plant to tune the parameters of the controller and create an additive control signal that is used to counteract environment noises and perturbations. This approach significantly improves control performance by reducing the effect of disturbances and sensor noises on the system. The FOAGP approach was tested in simulation to investigate its effectiveness. The results showed that the proposed approach outperformed other existing control methods, with a higher accuracy and better control performance. One of the significant advantages of the FOAGP approach is its ability to learn and adapt to changing system dynamics. The learning mechanism used in the FOPID controller allows the system to optimize its parameters online, which is especially useful in systems with varying operating conditions. The AGP predictor also continuously adjusts its parameters to accurately estimate the system output, making it an effective tool for controlling EHAs. The proposed approach offers a significant improvement in control performance, making it a better alternative to traditional control methods. This approach can be applied to various EHA systems, including those used in aerospace, automobile, and robotic applications, among others.

A Self–Tuning Intelligent Controller for a Smart Actuation Mechanism of a Morphing Wing Based on Shape Memory Alloys

The paper exposes some of the results obtained in a major research project related to the design, development, and experimental testing of a morphing wing demonstrator, with the main focus on the development of the automatic control of the actuation system, on its integration into the experimental developed morphing wing system, and on the gain related to the extension of the laminar flow over the wing upper surface when it was morphed based on this control system. The project was a multidisciplinary one, being realized in collaboration between several Canadian research teams coming from universities, research institutes, and industrial entities. The project’s general aim was to reduce the operating costs for the new generation of aircraft via fuel economy in flight and also to improve aircraft performance, expand its flight envelope, replace conventional control surfaces, reduce drag to improve range, and reduce vibrations and flutter. In this regard, the research team realized theoretical studies, accompanied by the development and wind tunnel experimental testing of a rectangular wing model equipped with a morphing skin, electrical smart actuators, and pressure sensors. The wing model was designed to be actively controlled so as to change its shape and produce the expansion of laminar flow on its upper surface. The actuation mechanism used to change the wing shape by morphing its flexible upper surface (manufactured from composite materials) is based on Shape Memory Alloys (SMA) actuators. Shown here are the smart mechanism used to actuate the wing’s upper surface, the design of the intelligent actuation control concept, which uses a self–tuning fuzzy logic Proportional–Integral–Derivative plus conventional On–Off controller, and some of the results provided by the wind tunnel experimental testing of the model equipped with the intelligent controlled actuation system. The control mechanism uses two fuzzy logic controllers, one used as the main controller and the other one as the tuning controller, having the role of adjusting (to tune) the coefficients involved in the operation of the main controller. The control system also took into account the physical limitations of the SMA actuators, including a software protection section for the SMA wires, implemented by using a temperature limiter and by saturating the electrical current powering the actuators. The On–Off component of the integrated controller deactivates or activates the heating phase of the SMA wires, a situation when the actuator passes into the cooling phase or is controlled by the Self–Tuning Fuzzy Logic Controller.

Dual fuzzy logic PID controller based regulating of dc motor speed control with optimization using Harmony Search algorithm

This paper discusses the implementation of a Proportional-Integral-Derivative (PID) controller for regulating the speed of a closed loop four quadrant chopper fed DC motor. The PID controller is combined with a Dual Fuzzy Logic Controller to form a DFPID controller for enhancing the performance of speed control of the DC motor. The DFLC is optimized using a metaheuristic algorithm known as Harmony Search Algorithm (HSA). The major aim of this research is to gain an effective control over the speed of the motor in the closed loop environment. For achieving this, the parameters for the DFPID are selected through time domain analysis which aims to satisfy the requisites such as settling time and peak overshoot. Initially, the fuzzy logic controller in the DFPID controls the coefficients of the PID achievement gain an effective control over the system error and rate of error change. Further, the DFPID is improved by the HAS for obtaining a precise correction. The solutions obtained by tuning the DFPID controller are evaluated from simulation analysis conducted on a MATLAB/SIMULINK platform. The closed loop performance is analyzed in both time and frequency domain analysis and the performance of DFPID is optimized using the HSA algorithm to obtain precise value of the control process. As observed from the Simulation analysis, the DFPID-HSA generates optimized control signals to the DC motor for controlling the speed. The performance of the intended speed control approach is analyzed in terms of different evaluation metrics such as motor speed, torque and armature current. Experimental outcomes show that the proposed approach achieves better control performance and faster speed of DC motor compared to conventional PID controllers and SMC controllers

A New Soft Computing Fuzzy Logic Frequency Regulation Scheme for Two Area Hybrid Power Systems

Modern renewable energy power system designs provide significant application benefits, but they also produce losses. The total generation, total load demand, and system losses must be balanced in order for this structured power system to operate reliably. The actual and reactive power balances are disturbed as a result of changes in load demand. System frequency and tie line interchange power deviate from their planned values as a result of this. A high system frequency deviation can cause the system to crash. In that case, multiple connect area systems use intelligent load frequency control techniques to deliver dependable and high-quality frequency and tie line power flow. Here, a standalone hybrid power system is taken into consideration, with generated power and frequency being controlled intelligently. In addition to the unpredictable nature of the wind, frequent adjustments in the load profile can produce sizeable and detrimental power variations. The output power of such renewable sources may fluctuate to the point that it causes significant frequency and voltage changes in the grid. An intelligent approach recently proposed to address the load frequency control (LFC) issue of an interconnected power system is known as fuzzy logic PID controller (FLPIDC). Standard proportional integral derivative (PID) controllers are used to control each section of the system.

Analysis and enhancement of MPPT technique to increase accuracy and speed in photovoltaic systems under different conditions

There has been tremendous growth in the use of renewable energy sources (RES) over the last few years. Solar systems, because of their unique properties, such as being noiseless, eco-friendly, and free of pollutants, are the most feasible source of energy among all RESs. However, a major drawback of photovoltaic (PV) systems is their low energy conversion efficiency, unstable and intermittent nature. In order to extract electrical energy from solar panels, it is necessary to connect them to the power system through DC-DC converters. As a result, maximum power point tracking (MPPT) algorithms are necessary in order to increase the amount of power harvested from solar PV systems. Accordingly, a method is presented in this paper for the design of an MPPT control approach for PV systems using fuzzy gain scheduling for proportional-integral-derivative (FGS-PID) controllers coupled with adaptive scaling factors for fuzzy input signals. The developed adaptive FGS-PID controller uses a two-level control system structure that incorporates the advantages of fuzzy logic and conventional PID control. Additionally, to fine-tune the member functions (MFs) of FGS, an integration of the fuzzy logic controller (FLC) and the modified fluid search optimization (MFSO) algorithm is employed. In order to evaluate the efficiency of the developed control systems, MATLAB software has been chosen for the simulations. The developed approach has proven highly effective in automatically regulating the triangular MFs of inputs and outputs and has shown superior performance. One of the key benefits of the proposed technique is that it enables fast and accurate tracking of the PV system's highest generated power in a short period of time.

Balance Control of a Flywheel Inverted Pendulum by Fuzzy Logic Controller

In this study, a flywheel inverted pendulum was modeled as simulation. The model controlled by fuzzy logic and PID controller for comparison. Fuzzy logic controllers were designed using triangular and Gaussian membership functions and various methods that are "and", "implication" and "aggregation". All gains from fuzzy logic controllers and PID were tuned by the trial-and-error method. The best performance was obtained by fuzzy logic controller that uses a triangular membership function and "prob/probor" functions. The results were evaluated in terms of three phenomena. In terms of Settling Time and Maximum Overshoot, Fuzzy Triangle MF with 0.15 s and 0 degrees, respectively, and PID and Fuzzy Triangle MF models with 0 degrees in terms of Steady-State error achieved the best success. In addition, the robustness of the control system was tested by applying two different types of disturbance inputs, random and impulse. The results show that fuzzy logic is a good alternative for balance control of a flywheel inverted pendulum, but PID has an acceptable performance.

Design of a novel mixed interval type-2 fuzzy logic controller for 2-DOF robot manipulator with payload

This paper investigates a novel coupling-based mixed interval type-2 fuzzy logic controller (MIT2FLC) for trajectory tracking problems of highly nonlinear and complex robot manipulator plants. The major impediment for these types of plants is coupling between the robotic links at the time of operation. In addition, the performances of these plants are adversely affected by parameter uncertainties, random noise and external disturbances. Therefore, the MIT2FLC approach with an additional degree of freedom in the membership functions is proposed to efficiently deal with the problem of uncertainty and provide robust performance. The Grey wolf optimization (GWO) is implemented to calculate the optimal parameters of the proposed controller. For checking the performance, the MIT2FLC approach is compared with its type-1 fuzzy counterparts, namely mixed type-1 fuzzy logic controller (MT1FLC), type-1 fuzzy logic controller (T1FLC) and PID controllers. Robustness analysis of the proposed controller is investigated for external disturbances, varying system parameters, and random noise.

Temperature and pH Control of Dark Fermentation Bioreactor to Produce Biohydrogen from Palm Oil Mill Effluent by Using Fuzzy Logic Controller

Hydrogen is the main choice of renewable fuel since it brings significant benefits compared with the other conventional fuels, such as the use of waste substrate, cleaner and highest energy density. Biological hydrogen production route is cost efficient since it can be processed in ambient conditions, easy operational techniques while keeping the environment safe. Dark fermentation to produce bio-hydrogen has received widespread attention from researchers in the present decades especially due to not requires light sources. This work is a study of the optimization of process conditions particularly pH and temperature of a dark fermentation bioreactor to produce Bio-H2 from palm oil mill effluent (POME) by using fuzzy logic controller. The simulation started by developing process and instrumentation diagram (P&ID) of bioreactor. Then a conventional PID controller and fuzzy logic controller were simulated by using MATLAB SIMULINK and the results were compared with each other in terms of safety aspect. The temperature of 37oC and pH of 6 is the optimum conditions that needs to be maintained to yield the hydrogen at 2.79 mol H2 mol-1 glucose. The pH is able to reach optimum value at 600s and 30s by using the PID controller and fuzzy logic controller, respectively. Same goes to temperature control, where the parameter reaches optimum value at 200s and 0.3s respectively. Based on these promising results, fuzzy logic controller is a good replacement for the conventional control systems since it requires shorter time to optimize process conditions, consequently making sure the safety aspect is well guaranteed.

Design of a fuzzy logic proportional integral derivative controller of direct current motor speed control

Direct current (DC) motor speed control is useful. Speed can be modified based on needs and operations. DC motors cannot control their speed. To control the DC motor’s speed, a dependable controller is needed. The DC motor speed will be controlled by a fuzzy logic proportional integral derivative controller (FLC-PID). The DC motor circuit’s electrical and mechanical components have been modeled mathematically. Ziegler-Nichols is used to tune the PID controller’s gain parameters. The FLC controller employs 3×3 membership function rules in conjunction with the MATLAB/Fuzzy Simulink toolbox. Real hardware was attached to the simulation to evaluate the DC motor speed control using the fuzzy logic PID controller. DC motors with FLC PID controllers, FLC controllers, and DC motors alone will be compared for the transient response. The DC motor with an FLC PID controller performed better in this study.

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.

Fuzzy Logic PID Controller with Both Coefficient and Error Modifications for Digitally-Controlled DC–DC Switching Converters

Fuzzy Logic PID Controller with Both Coefficient and Error Modifications for Digitally-Controlled DC–DC Switching Converters

Fuzzy Logic Control and PID Controller for Brushless Permanent Magnetic Direct Current Motor: A Comparative Study

Electrical machines based on permanent magnet material excitations have been applied in many sectors since they are distinguished by their high torque-to-size ratio and offer high efficiency. Brushless permanent magnetic direct current (BLPMDC) motors are one type of these machines. They are preferable over conventional DC motors. one of the main challengings of the BLPMDC motor drives is the inherited feature of nonlinearity. Therefore, a conventional PID controller would not be an efficient choice for the speed control of such motors. The object of this paper is to design an efficient speed control for the BLPMDC motor. The proposed controller is based on the Fuzzy logic technique. MATLAB/ Simulink has been employed to design and test the drive system. Simulations were carried out for three cases, the first without a controller, the other using conventional control, and the third using expert systems. The results proved the possibility of improving the engine's working performance using the control systems. They also proved that the adoption of expert systems is better than the traditional nonlinear systems. The simulation response shows that the Rise Time(tr) at PID equals 66.306ms, while it equals 19.530ms for the Fuzzy logic controller. Moreover, Overshoot for PID and Fuzzy logic controller are 6.989% and 1.531%, respectively. On the other hand, undershoot is equal to 1.788% and 11.924% for PID and Fuzzy logic controller, respectively.

Leveraging fuzzy Logic PID controllers for accelerating chemical reactor CFD

A control technique comprising three Fuzzy Logic Proportional Integral Derivative (FLPID) controllers was evaluated to determine their ability to shorten convergence times for the computationally expensive globally unsteady model of a low-density polyethylene reactor. The numerical reactor contained millions of computational elements, a rotating stirrer shaft, intricate near-wall geometry, and highly exothermic polymerization kinetics. Catalyst feed rates were instantaneously increased by 50% to evaluate the FLPID’s performance during a hypothetical process excursion. FLPID achieved quasi-steady state (QSS) 54% faster than conventionally tuned PID controllers. Reducing the percent overshoot of the error and rise time of the controller output, the FLPID demonstrated its ability to lower computational cost. FLPID in CFD offers the potential to improve control methods on actual plant scale processes. In particular, the reduction in error overshoot could reduce the likelihood of reactor overheat events as the temperatures are kept within a tighter operational window.