Fuzzy Logic Controller Controller Research Articles

Research of the FLC + PID switching control strategy based on real-time error for the pneumatic polishing force regulating system

This paper designs an active pneumatic polishing force control system and investigates its control strategy. A straightforward method based on the experiments is proposed for identifying and modeling the pneumatic system and a convenient and effective control strategy based on real-time error is proposed for pneumatic polishing force regulating. Corresponding function between the input duty cycles and the output force values is created based on the experiment data tested by the system. Then the pneumatic force regulating system is modeled by the corresponding function. Base on the results of simulations and experiments, a FLC (Fuzzy Logic Control) + PID (Proportion-Integration-Differentiation) switching control strategy is designed with a switching mechanism to choose different control algorithm based on real-time error which can take the advantages of both PID and FLC. The FLC + PID switching controller can eliminate the steady state errors occurring in FLC controller and achieve the same accuracy as the PID controller but with a faster response speed. The approach effectively stabilizes the polishing force, resulting in polishing force errors that are distributed within ± 1 N. The mean errors are less than 0.2 N and the absolute mean errors hover around 0.3 N. The pneumatic polishing force control system regulated by the FLC + PID switching control exhibits effectiveness, stability, high control precision and fast response speed, and satisfies the force control requirements of polishing process.

Performance evaluation of PSO-PID and PSO-FLC for continuum robot’s developed modeling and control

Continuum robots are complex structures that require sophisticated modeling and control methods to achieve accurate position and motion tracking along desired trajectories. They are highly coupled, nonlinear systems with multiple degrees of freedom that pose a significant challenge for conventional approaches. In this paper, we propose a system dynamic model based on the Euler–Lagrange formulation with the assumption of piecewise constant curvature (PCC), where we accounts for the elasticity and gravity effects of the continuum robot. We also develop and apply a particle swarm optimization (PSO) algorithm to optimize the parameters of our developed controllers: an inverse dynamic proportional integral derivative (PID) controller and an inverse dynamic fuzzy logic controller (FLC), where we use the integral time of absolute error (ITAE) as the objective function for the PSO algorithm. We validate our proposed model and optimized controllers through different designed trajectories, simulated using our developed unique animated MATLAB simulation. The results show that the PSO-PID controller improves the rise time, overshoot percentage, and settling time by 16.3%, 31.1%, and 64.9%, respectively, compared to the PID controller without PSO. The PSO-FLC controller shows the best performance among all controllers, with a settling time of 0.7 s and a rise time of 0.4 s, leading to the highest level of precision in trajectory tracking. The ITAE error for the PSO-FLC controller is 11.4% and 29.9% lower than that of the PSO-PID and FLC controllers, respectively.

A Fuzzy Logic-Based Tuning Model in an Indoor Lighting System for Energy and Visual Comfort Management

This paper proposes a fuzzy logic-based tuning model (FLTM) for daylight-linked control of the lighting system in an office room. The proposed FLTM considered a new method of dimming levels of light-emitting diode (LED) luminaires updating process to improve the performance of the developed fuzzy logic controller (FLC) in terms of energy consumption and visual comfort metric and, at the same time, fully complies with the European Standard EN 12464-1. The artificial lighting system and daylight simulation were carried out using DIALux to model artificial lighting and daylight illuminance levels matrices. The proposed FLTM was developed and simulated using MATLAB and validated and compared with other controllers, including developed FLC and artificial neural network (ANN) based control. The simulation results showed that the proposed FLTM successfully improved the performance of developed FLC in terms of a fully satisfied visual comfort set-point. It also attained higher energy savings of 2% than ANN and achieved the closest to preset visual comfort compared with other controllers. Moreover, the proposed method consumes less computational effort, and it is easy to integrate with developed FLC and daylight-linked control of the lighting system.

SEPIC-Based DC–DC Converter with Soft Switching Operation for Wide Conversion Operation of E-Automobiles

The article introduces a novel approach for designing a DC-to-DC converter utilizing the single-ended primary inductor converter topology for wide conversion operation in Electric Automobiles (E-Automobiles), with a focus on soft switching operation. A maximum power point tracking approach based on hierarchical multi-heuristic chicken swarm optimization is used to optimize the performance of the solar. The output of the converter is then fed into a three-phase inverter, where pulse width modulation is generated using a fuzzy logic controller (FLC). The inverted output is filtered using a low-pass filter before being supplied to the load, which is a brushless direct current motor in this case. The performance of the system is evaluated using traditional proportional-integral-derivative controllers and FLC controllers, considering parameters such as maximum amplitude (rise time), the time at which the system achieves its peak value (peak time), the time delay between input and output (delay time), the amplitude of the output voltage and current (output voltage and output current), and the discrepancy between the desired and actual values in a stable state (steady-state error). Comparative analysis indicates that the proposed design incorporating an FLC controller yields superior results compared to other approaches.

Simulation model of ACO, FLC and PID controller for TCP/AQM wireless networks by using MATLAB/Simulink

<span lang="EN-US">The current work aims to develop a suitable design for control systems as part of a queue management system using the transmission control protocol/and active queue management (TCP/AQM) protocol to handle the expected congestion in the network. The research also aims to make a comparison between the different control methods, including the traditional proportional integral derivative (PID) and the expert fuzzy logic control (FLC), as well as the optimal ant colony optimization (ACO) that is used according to the performance improvement criteria to reach the best values for parameters the traditional controller (kd, ki, k p), where the addition of the performance indicator time-weighted absolute error (ITAE) was adopted. The use of this method without any other optimization algorithm that can be applied to adjust the parameters of the PID to verify the possibility of improving performance and enhance that with experience and to know the level of improvement for this particular system being the subject of the study. The results showed the superiority of the optimal ACO over both the FLC expert and the conventional PID, as well as the superiority of the FLC expert over the traditional PID.</span>

Synthesis of Hybrid Fuzzy Logic Law for Stable Control of Magnetic Levitation System

In this paper, we present a method to design a hybrid fuzzy logic controller (FLC) for a magnetic levitation system (MLS) based on the linear feedforward control method combined with FLC. MLS has many applications in industry, transportation, but the system is strongly nonlinear and unstable at equilibrium. The fast response linear control law ensures that the ball is kept at the desired point, but does not remain stable at that point in the presence of noise or deviation from the desired position. The controller that combines linear feedforward control and FLC is designed to ensure ball stability and increase the system's fast-response when deviating from equilibrium and improve control quality. Simulation results in the presence of noise show that the proposed control law has a fast and stable effect on external noise. The advantages of the proposed controller are shown through the comparison results with conventional PID and FLC control laws.

Multifunctional Control for PV Integrated Battery Energy Storage System with Enhanced Power Quality

Abstract: This modest paper presents a study on the energy quality produced by a multifunction system consisting of a Photovoltaic (PV) power source connected to a battery. A three-level inverter was used in the system studied for the purpose of improving the quality of energy injected into the grid and decreasing the Total Harmonic Distortion (THD). A Maximum Power Point Tracking (MPPT) algorithm based on a Fuzzy Logic Controller (FLC) is used for the purpose of ensuring optimal production of photovoltaic energy. In addition, another FLC controller is used to ensure DC bus stabilization. The considered system was implemented in the Matlab/Simpower environment. The results show the effectiveness of the proposed inverter at three levels in improving the quality of energy injected from the system into the grid.

Implementation of Fuzzy Logic Control on a Tower Copter

Air transport has become a major attraction for scientists in the last decade, to carry out developments in the fields of firefighting, military, and commercial purposes. A quadcopter is a helicopter with four rotors. There are four arms connected to the main control and each arm has a motor with a rotor. In this study, the position control of a tower control is presented. A Fuzzy Logic Controller (FLC) is proposed, and it performance is compared with PID control. The hardware implementation test shows that FLC is superior to PID. The hardware testing shows that the settling time of the FLC control response is 0.5s while the PID control response is 1.2s. That means FLC settling time is faster by 58.33% compared to PID.

EPO Optimized FLC Controller for PQ Improvement in Wind Integrated Power Distribution System

Nowadays, Power Quality (PQ) problems are economically affecting customers and utilities. In our work, the PQ problems are mitigated using the Unified Power Quality Conditioner (UPQC) and the proposed controller. Fuzzy Logic Controller (FLC) and Emperor Penguin Optimization algorithm (EPO) are utilized to design the proposed controller. The proposed control scheme analyses the PQ issues of harmonics, oscillatory transients, swell, and voltage sag. The optimized FLC controller-based UPQC compensates the PQ problems at a common coupling point. Using the decomposition technique, the Total Harmonic Distortion (THD) and harmonics are moderated, and the voltages sag and swell problem is minimized to improve the compensation capability. The MATLAB/SIMULINK platform is utilized to evaluate the performances of PQ issues in UPQC compensating devices. The generated, injected, and load voltages results are compared with existing controller techniques.

Design of PID, FLC and Sliding Mode Controller for 2-DOF Robotic Manipulator: A Comparative Study

Controlling the manipulators in a precise manner is a challenging task. To overcome this difficulty around the world, many researchers have developed various control algorithms but are not providing optimal results. To obtain the optimal results in the current research the authors designed a proportional, integral, and derivative (PID) controller, fuzzy logic controller (FLC), and sliding mode controller (SMC) for a 2-DOF manipulator. The concept of forward and inverse kinematics was initially solved after assigning the D-H parameters for each joint. The purpose of forward or direct kinematics is to obtain the position and orientation of the end effector. Further, the concept of inverse kinematics is used to estimate the joint angles. Later on, the Lagrange-Euler formulation was used to calculate the dynamics of the 2-DOF manipulator, which is required to estimate the torque required for each joint of the robotics arm. The main goal of this research problem is to optimize the angular error between the two successive events. Finally, the developed algorithm is compared with the existing algorithms such as PID and Fuzzy logic controller.

Arduino Interfacing of FLC-based Power Flow Management in Smart Microgrid Systems

There is a rapid increase in power demand which has led to an increase in the need for smaller grids all over the world. The microgrid is such a small power supply system that consists of loads, distributed energy resources, and energy storage systems. With the development of the internet, the power sector has started the development of the newest type of grids, involving a smart technology called smart grids. Smart grids have got smart integration and advanced storage, along with dynamic optimization of grid operation and resources, digital information and control, distributed resources, demand-side resources, energy efficiency resources, and smart metering systems. The developments in intelligent controllers reflect in power flow management also. This paper gives an idea of how to achieve power flow management through a tariff-based Fuzzy Logic Controller (FLC). The control algorithm used here is the k factor-based Icosϕ algorithm, which decides the amount of power shared by microgrids and the grid based on the load demand. The power forecasting of the microgrid with a solar source is performed using ANN. The regression plot and the error histogram obtained show the accuracy of the ANN controller. The simulation of power flow management of the smart microgrid system is tested and analyzed and the proposed system is compared with other existing research. The hardware implementation of the FLC controller with the Arduino interface which is analogous to real-time measurement is explained in detail to compare the simulation results obtained.

Design of passive suspension system to mimic fuzzy logic control active suspension system

BackgroundThis paper proposes a method for designing a passive suspension system that determines the optimal suspension settings while offering feasible performance near an active suspension system. A mathematical model of a nonlinear quarter car is developed and simulated for control and optimization in MATLAB/Simulink® environment. The input road condition is a Class C Road, and the vehicle moves at 80 kmph. Fuzzy logic control (FLC) action is used to accomplish active suspension system control. An approach for investigating optimal suspension settings based on the FLC control force is described here. The optimized passive suspension system is supposed to have the same suspension travel and velocity as an active suspension system. The least square technique is implemented to optimize the suspension parameters of the passive suspension system.ResultsThe initial passive suspension system, FLC active system, and optimized suspension system are simulated in MATLAB/Simulink® environment. It is observed that RMS acceleration for the FLC system is 0.5057 m/s2, which is reduced by 46% (passive suspension system has RMS acceleration of 0.9322 m/s2, which is uncomfortable). For optimized system, RMS acceleration is 0.6990 m/s2. It is observed that the optimized passive suspension system almost mimics the initial FLC active suspension system. For the optimized system, sprung mass acceleration and VDV are improved by 30% and 27%, respectively, compared to the initial passive system.ConclusionIt is observed that the optimized passive suspension system mimics the initial FLC system. Also, an optimized FLC system has improved health criterion-based results compared to other suspension systems.

A Comparative Study of DC-DC Buck, Boost, and Buck-boost Converters with Proportional-integral, Sliding Mode, and Fuzzy Logic Controllers

Background: DC-DC converters are utilized in a wide range of industrial and commercial applications such as automobiles, renewable energy systems, DC motor control, portable chargers, uninterruptable power supply, etc. because of their efficiency, ease of use, simpler circuits, and cheaper solution. The most common ones are the buck, boost, and buck-boost converters. Methodology: In this paper, the implementation of these converters has been proposed using advanced controllers: Proportional plus Integral (PI), Sliding Mode Control (SMC), and Fuzzy Logic Control (FLC). The controllers have been implemented with practical circuit values in MATLAB Simulink. Results: The results show that all proposed controllers can track the input set-point voltage. The PI controller response was superior in eliminating the steady-state error in all converters. However, it showed greater overshoot and ripple factor in boost operation. The SMC controller response was superior in terms of the shortest settling time. However, it showed the greatest peak time in buck converter and boost mode of the buck-boost converter. The FLC controller showed the highest settling time and steady-state error in all operations. Novelty and Significance: The proposed work is novel as compared to others available in the literature such that no such comprehensive study was found with all types of DC-DC converters together up to our best knowledge. The proposed work is significant as it will give complete guidance regarding simulation implementation of DC-DC converters with advanced intelligent control algorithms.

Intelligent Torque Allocation Based Coordinated Switching Strategy for Comfort Enhancement of Hybrid Electric Vehicles

This paper proposes two intelligent torque distribution strategies based on particle swarm optimization (PSO) and fuzzy logic control (FLC) to provide convenient torque allocation that maximizes hybrid electric vehicle (HEV) propulsion power. PSO torque distribution strategy uses torque transfer ratio (TTR) as a fitness function to select the best torque candidates and differential arrangements that maximize HEV propulsion torque. A proposed FLC controller with adequate membership functions is designed to ensure convenient torque vectoring across vehicle wheels. New coordinated switching strategy is proposed in this paper to address the undesired transient ripples occurring during drivetrain commutations and power source switchings. The proposed coordinated switching strategy controls the switching period duration through transition functions fitting the transient dynamics of power sources. In non-uniform surfaces, intelligent torque allocation strategies converted 84~86% of the generated torque into propulsion torque whereas equal torque distribution strategy yielded a torque transfer ratio of 50%. Thanks to the proposed coordinated switching strategy, DC bus voltage ripples were reduced into a narrow band of ± 5V, transient power ripples were limited to a narrow band of 600 W and torque jerks were almost suppressed. Real-time simulation using the RT LAB platform confirms that the proposed coordinated switching strategy have reduced transient torque overshoot from 69% to almost zero and this is expected to improve HEV driving comfort.

A Novel PSO Based Fuzzy Controller for Robust Operation of Solid-State Transfer Switch and Fast Load Transfer in Power Systems

This paper proposes a novel particle swarm optimization (PSO) algorithm based fuzzy logic controller (FLC) for improving the performance of automatic solid-state transfer switch (SSTS) with respect to transfer time. The proposed technique generates adaptive membership functions (MFs) of voltage error and rate of change of voltage error for input and output based on the fitness function formulated by the PSO. An optimal PSO-based FLC (PSOF) fitness function is further employed to tune and minimize the mean absolute error (MAE) to improve the performance of the load transfer in a short duration. Results obtained from the proposed PSOF are compared with those obtained with the conventional FLC to validate the developed controller. It is observed that the proposed PSOF optimized controller can transfer the load faster than the conventional FLC controller. The accuracy of the developed PSOF is illustrated and investigated via simulation tests for SSTS in the IEEE 9-bus system. It can be concluded that the PSOF controller is better than the only fuzzy controller in all tested cases in terms of transfer time.

Modeling and design of an automatic generation control for hydropower plants using Neuro-Fuzzy controller

This paper presents the modeling, design, and experimental analysis of an Automatic Generation Control (AGC) for a hydropower plant using Adaptive-Neuro-Fuzzy Inference system (ANFIS). This was aimed at reducing the frequency deviations which occur during power generation. The Adaptive-Neuro-Fuzzy Inference system (ANFIS) was used to intelligently control the selection of parameters for the effective control of power in the hydropower plant. The proposed ANFIS was trained with input–output data of the fuzzy logic controller (FLC). The ANFIS model is used as a hybrid learning model which includes the Least Square Estimate (LSE) and back propagation algorithm (BPA). The conventional PID, FLC, and ANFIS controllers were investigated using MATLAB. In order to determine the best controller, the controllers were experimented and compared to determine the controller with the best performance. The results show that frequency deviations occur as a result of a continuous variation of loads, which make the deviations difficult to control when a governor is not applied. Furthermore, the response of the AGC of the hydropower plant (in the single and double area) with step load changes was studied. The simulation results show that the ANFIS controller performs better compared to the PID as well as the FLC. Further results indicate that the proposed ANFIS controller helps to speed up the performance of the AGC of the hydropower plant. The ANFIS controller not only improved the performance but also made the fuzzy inference system (FIS) less dependent on the expert system.

Pitch Angle Modulation of the Horizontal and Vertical Axes Wind Turbine Using Fuzzy Logic Control

The aim of this research work is to modulate the pitch angle of both types of wind turbines based on fuzzy logic control (FLC), as changes in the pitch angle have various functions in horizontal and vertical axis wind turbines. For HAWT, pitch angle control is applied to shield the electrical components of the turbine when the wind speed exceeds the rated speed without shutting down the turbine. FLC is used to control the angular velocity using two inputs and one output with three membership functions for both inputs and output. In VAWT, pitch angle control is applied to boost the performance of the turbine and its self-starting torque. FLC utilizes two inputs and one output with five membership functions for both inputs and output. For both turbine types, FLC produces a control signal that drives the actuator to achieve the desired pitch angle. The dynamics of HAWT and VAWT are simulated by the MATLAB/Simulink to demonstrate the influence of pitch controls on their dynamics. For HAWT, the FLC control has successfully maintained the angular speed of the rotor. The values of tip speed ratio and coefficient of performance are reduced in order to maintain the rotor angular velocity at its rated value. On the other hand, the results showed that the torque produced by the VAWT individual blade has improved with the pitch angle control. In addition, using FLC to control the pitch angle gives enhanced output and higher Cp at low tip speed ratios. Gain schedule PI controller is also used in both HAWT and VAWT for comparative study.

Voltage Stabilization in PV based Buck Boost Converter Using Pi and FLC Controllers due to Input Voltage Variation

Aim: The aim of this paper is to present innovative voltage stabilization in PV fed buck boost converter systems due to input voltage variation. One of the Artificial intelligence (AI) controllers known as fuzzy logic controller is used in this project. Materials & Methods: Proportional Integral (PI) and Fuzzy Logic Controller (FLC) based MPPT algorithm are implemented to analyze the Buck Boost converter output voltage under varying input conditions. Results: Based on the results obtained, it is inferred that the FLC based Buck Boost converter efficiently stabilizes the voltage under varying insolation conditions in the PV panel. Conclusion: FLC based MPPT provides better stabilization (35.67V) compared to PI algorithm (31.65V) for the selected data set.

PV Systems Control Using Fuzzy Logic Controller Employing Dynamic Safety Margin under Normal and Partial Shading Conditions

Because of the unpredictable activity of solar energy sources, photovoltaic (PV) maximum power point tracking (MPPT) is essential to guarantee the continuous operation of electrical energy generation at optimal power levels. Several works have extensively examined the generation of the maximum power from the PV systems under normal and shading conditions. The fuzzy logic control (FLC) method is one of the effective MPPT techniques, but it needs to be adapted to work in partial shading conditions. The current paper presents the FLC-based on dynamic safety margin (DSM) as an MPPT technique for a PV system to overcome the limitations of FLC in shading conditions. The DSM is a performance index that measures the system state deviation from the normal situation. As a performance index, DSM is used to adapt the FLC controller output to rapidly reach the global maxima of the PV system. The ability of the proposed algorithm and its performance are evaluated using simulation and practical implementation results for single phase grid-connected PV system under normal and partial shading operating conditions.

Intelligent Control Strategies for Three Degree of Freedom Active Suspension System

Suspension system plays a major role in both comfort and stability of a vehicle. This paper presents modeling and controlling for a 3 Degree of Freedom (DOF) active suspension system. Four controllers are designed to control the response of the active suspension system, namely PID, LQR, Fuzzy Logic Controller (FLC) and Artificial Neural Network (ANN). The response for both the active suspension system and the passive suspension system is compared. For passive suspension system, it has been found out that it is hard to improve both passenger comfort and road handling at the same time, because of the fixed parameters that cannot be changed during the work. On the other hand, in active suspension system, both ride comfort and road handling can be improved. This work has showed that ANN, FLC, LQR, and PID controllers can be used with an active suspension system in order to improve the performance, the stability, and the ride comfortability compared to the passive suspension system. All these controllers are simulated using MATLAB and Simulink. Different road profiles are used to test the active suspension system response, such as a step input of 0.1 m, and a sinewave of amplitude of 0.3m and a frequency of 0.318Hz. All the controllers show better response compared to passive suspension system. A compromise can be done to choose the controller depending on the desired states.