Fuzzy Logic Controller Research Articles

Novel adaptive fuzzy control for pendubot with actuator faults and uncertainties: Design and experiments

Pendubot has been widely applied as a benchmark platform for control research and education. In this paper, a novel adaptive fuzzy hierarchical sliding mode controller (AFHSMC) is proposed for the pendubot under actuator faults and uncertainties. The proposed controller is designed by combining hierarchical sliding mode control (HSMC), fuzzy logic control (FLC), and balancing composite motion optimization. The proposed controller preserves many advantages such as having a straightforward structure, simple implementation, chattering reduction, and high precision and robustness. The stability of the proposed controller is ensured by using the Lyapunov approach. To verify the control performance, various numerical simulations and experiments are conducted on a pendubot under conditions that involve actuator faults and uncertainties. Compared to the conventional HSMC and FHSMC controllers, the proposed AFHSMC improves by 0.43% and 0.38% for tracking precision of the first link's angle estimate, 3.26% and 0.08% for the second link's angle estimate when influenced by uncertainties, as well as 65.23% and 12.24% for the first link, 83.95% and 16.15% for the second link when influenced by faults.

Robust control based on type-2 fuzzy logic for permanent magnet synchronous motor (PMSM)

This paper introduces a novel control technique utilizing the orientation of stator flux based on type 2 fuzzy controllers to control the mechanical power generated by a permanent magnet synchronous motor and addressing parametric variation challenges to enhance robust performance against external fluctuations. The Permanent Magnet Synchronous Motors driven by stator variables via two converters relies on these components to interface with the electrical network in distinct manners. The network-side converter facilitates continuous bus control and enhances power factor, while the motor-side converter enables precise control and optimization of energy flow during motor operation. The paper outlines the motor and converter models, followed by the development of control commands to regulate mechanical power (speed, torque) for optimal efficiency and production quality and we will then present a comparative study between the developed controls to highlight the best performing and most robust of each, . Numerical simulations demonstrate the effectiveness of implementing type 2 fuzzy logic control in achieving these objectives.

A Coordinated Control Strategy for Efficiency Improvement of Multistack Fuel Cell Systems in Electric–Hydrogen Hybrid Energy Storage System

A two-layer coordinated control strategy is proposed to solve the power allocation problem faced by electric–hydrogen hybrid energy storage systems (HESSs) when compensating for the fluctuating power of the DC microgrid. The upper-layer control strategy is the system-level control. Considering the energy storage margin of each energy storage system, fuzzy logic control (FLC) is used to make the initial power allocation between the battery energy storage system (BESS) and the multistack fuel cell system (MFCS). The lower-layer control strategy is the device-level control. Considering the individual differences and energy-storage margin differences of the single-stack fuel cell (FC) in an MFCS, FLC is used to make the initial power allocation of the FC. To improve the hydrogen-to-electricity conversion efficiency of the MFCS, a strategy for optimization by perturbation (OP) is used to adjust the power allocation of the FC. The output difference of the MFCS before and after the adjustment is compensated for by the BESS. The simulation and experiment results show that the mentioned coordinated control strategy can enable the HESS to achieve adaptive power allocation based on the energy storage margin and obtain an improvement in the hydrogen-to-electricity conversion efficiency of the MFCS.

Trajectory tracking control of a mobile robot using fuzzy logic controller with optimal parameters

Abstract This work investigates the use of a fuzzy logic controller (FLC) for two-wheeled differential drive mobile robot trajectory tracking control. Due to the inherent complexity associated with tuning the membership functions of an FLC, this work employs a particle swarm optimization algorithm to optimize the parameters of these functions. In order to automate and reduce the number of rule bases, the genetic algorithm is also employed for this study. The effectiveness of the proposed approach is validated through MATLAB simulations involving diverse path tracking scenarios. The performance of the FLC is compared against established controllers, including minimum norm solution, closed-loop inverse kinematics, and Jacobian transpose-based controllers. The results demonstrate that the FLC offers accurate trajectory tracking with reduced root mean square error and controller effort. An experimental, hardware-based investigation is also performed for further verification of the proposed system. In addition, the simulation is conducted for various paths in the presence of noise in order to assess the proposed controller’s robustness. The proposed method is resilient against noise and disturbances, according to the simulation outcomes.

Integration of Fuzzy Logic and Neural Networks for Enhanced MPPT in PV Systems Under Partial Shading Conditions

Efficient energy collection from photovoltaic (PV) systems in environments that change is still a challenge, especially when partial shading conditions (PSC) come into play. This research shows a new method called Maximum Power Point Tracking (MPPT) that uses fuzzy logic and neural networks to make PV systems more flexible and accurate when they are exposed to PSC. Our method uses a fuzzy logic controller (FLC) that is specifically made to deal with uncertainty and imprecision. This is different from other MPPT methods that have trouble with the nonlinearity and transient dynamics of PSC. At the same time, an artificial neural network (ANN) is taught to guess where the Global Maximum Power Point (GMPP) is most likely to be by looking at patterns of changes in irradiance and temperature from the past. The fuzzy controller fine-tunes the ANN's prediction, ensuring robust and precise MPPT operation. We used MATLAB/Simulink to run a lot of simulations to make sure our proposed method would work. The results showed that combining fuzzy logic with neural networks is much better than using traditional MPPT algorithms in terms of speed, stability, and response to changing shading patterns. This innovative technique proposes a dual-layered control mechanism where the robustness of fuzzy logic and the predictive power of neural networks converge to form a resilient and efficient MPPT system, marking a significant advancement in PV technology.

Control of Solar Photovoltaic Integrated Universal Active Filter Using Artificial Intelligence Technique

This paper organizes, a different technique based on adaptive filtering is proposed for the control of three phase worldwide active power filter with a solar photovoltaic array integrated at its DC bus. Two adaptive filters along with a zero crossing detection method are used to remove the magnitude of fundamental active component of distorted load currents, which is then used in estimation of reference signal for the shunt active filter. This technique enables extraction of active component of all three phases with reduced mathematical computation. The series active filter control is based on synchronous reference frame theory and it regulates load voltage and maintains it in-phase with voltage at point of common coupling under conditions of voltage sag and swell. In this paper An artificial intelligence technique fuzzy logic controller is used. The performance of the system is evaluated on an MATLAB/SIMULINK software under various dynamic conditions such as sag and swell in voltage at point of common coupling, load unbalancing and change in solar irradiation intensity.

Fuzzy logic based improved direct torque control of DFIG-based wind turbines

This paper investigates direct torque control with a fuzzy controller (F-DTC) for a two-level voltage source inverter feeding the rotor of a doubly-fed induction generator (DFIG) based on a variable-speed wind energy conversion system (VSWECS). The wind turbine's MPPT technology is used to obtain the torque reference. The proposed fuzzy logic-based direct torque control (F-DTC) is designed to eliminate the flux and electromagnetic torque ripples that are common with existing (DTC) controls, leading to smoother operation and improved efficiency in the wind energy conversion system. The fuzzy logic controller improves switching decision-making by handling uncertainties and non-linearities, resulting in smoother and more efficient control of the torque and flux. To validate the effectiveness of the proposed F-DTC method, simulations are conducted in MATLAB/SIMULINK, and the results are compared with those of the classical DTC approach. The findings demonstrate that the F-DTC strategy significantly reduces torque and flux ripples, enhances the dynamic response, and improves overall system performance.

Application of Artificial Intelligent Techniques for Power Quality Improvement in Microgrid System

This paper focuses on modeling, control and power quality improvement of a microgrid connected system. This last was designed as a multi-converter system with Wind Turbine driven Permanent Magnet Synchronous Generator, and lithium ion Battery Storage Energy System. These sources are connected by a continuous bus to a nonlinear load through a DC /AC converter and three-phases multi-functional voltage source inverter. Where, wind systems are considered as primary power resource, and the grid is used for the effect of consumption of the overage power available from sources, when the battery has been fully charged. Multi Functional Voltage Source Inverter is used to ameliorate the performance of the proposed system, guaranteeing both reactive power and harmonic compensation. Moreover an intelligent control by fuzzy logic control algorithm are managed In order to extract the maximum power from wind turbine, to guarantee an effective storage management, this about the DC side. For the AC side a direct power control strategy with fuzzy logic algorithm have been used. The simulation of the system solution is carried out in Matlab/Simulink. The obtained simulation results show that the technique of fuzzy logic furnish the best solution in term of robustness, optimization performance, low THD and fast dynamic response.

PLC Controlled Fuzzy Logic-Based Egg Hatching Machine

In response to the increasing food demand due to the growing global population, this study has designed a fully automated incubator machine based on a Programmable Logic Controller (PLC) to enhance efficiency in the egg production sector. To align with Industry 4.0 technologies, this machine controls temperature and humidity using fuzzy logic, one of the artificial intelligence methods. The incubator is capable of meeting the specific temperature and humidity needs for the incubation processes of various types of bird eggs. It has been tested under different conditions, and its performance has been examined in detail. The analysis results show that the fuzzy logic-based temperature and humidity control systems on the PLC successfully reached the set reference values and maintained them continuously and stably after initial fluctuations. In conclusion, the design and control of an automated incubation machine with a PLC-based fuzzy logic controller were successfully accomplished.

MITIGATION OF POWER QUALITY PROBLEMS IN STEEL PLANTS USING STATIC SYNCHRONOUS COMPENSATOR

This paper focuses on mitigating various power quality issues from electric arc furnaces (EAF) in steel plants, including voltage flicker, sag, swell, and harmonics. Arc furnaces are recognized as one of the industry's most nonlinear electrically polluting loads. The proposed solution in this article to address all power quality issues caused by EAF is implementing a flexible power supply strategy known as a static synchronous compensator (STATCOM) for injecting reactive energy into the electrical grid. In this study, an experimental model of EAF is utilized based on real measurements of the variation of arc resistance and arc reactance during the melting phase. The pulse width modulation (PWM) technique is employed for current tracking feedback control to generate the required reactive current. A fuzzy logic controller (FLC) is employed for the DC bus.

Designing Fuzzy Logic Control for the BLDC Motor Based on Airfoil Bearing

Due to removing the mechanical brushes in BLDC motors, BLDC motors are widely used in many industrial applications. Therefore, they are maintenance-free and their control is easy to design, especially for high-speed applications. For example, eco-friendly vacuum cleaners prevent atopic dermatitis, allergic rhinitis, and asthma. Other applications are Micro Gas turbines or compressors with small impellers due to miniaturization, BLDC motors must rotate at very high speeds to maintain the compressor's compression ratio. Generally, airfoil bearings should be used instead of ball bearings because of the friction in high-speed motors. Unfortunately, the characteristics of airfoil bearings depend on rotational speed. In this work, a BLDC motor with an airfoil bearing is controlled by a PID controller, this work analyzed the BLDC SYSTEM to determine the PID coefficient using the feedback method. The proposed controller Fuzzy Logic is used for adaptive control. In addition, the controller of BLDC motors combines auto-tuning and self-tuning technology. The results demonstrate that the proposed method gives efficient control by reducing the settling time and maximum peek overshot. The designed controller for the airfoil-bearing BLDC motor has a good performance.

Implementation of fuzzy logic controller for positive output LUO converter

This paper describes the fuzzy logic controller (FLC) for a positive output LUO converter (POLC), which uses a voltage controller to achieve the output voltage regulation. The fourth-order POLC DC–DC converter produces positive output voltage utilizing the voltage lift technique. Due to the converter’s switching functions and its inherently time-varying nature, it exhibits a highly nonlinear dynamic response. To enhance the dynamic performance of the converter, controllers are designed using small signal model and frequency response analysis. The closed loop is used to evaluate converter performance in the presence of variations in reference voltage, source voltage and load disturbances. The power loss analysis of the converter is performed. The comparative results show that FLC gives good dynamic response for wide range of variations than the proportional–integral (PI) controller. The prototype model is built using dsPIC (30F4011) microcontroller, and the experimental results are presented to validate the simulation results.

Humanoid Robot Teleoperation Through 3-D Pose Estimation

The knowledge of how the human motions is performed helps to understand how the human body works. This paper presents a method to estimate the human limbs angles through a kinematic model depicted by Roll-Pitch-Yaw rotationmatrix and the mimic of those angles on a humanoid robot. The advantage of this model is the detailed representation of each joint movement in the coordinate axes (x, y, z). The angles estimation is made with the information provided by algorithms of artificial vision and artificial intelligence. In order to reduce the latency between the human motion capture and robot motions, a fuzzy logic controller is implemented in order to control each robot joint. The final robot limbs angles are compared with the human angles in order to obtain the final error between those measurements. This method shows a similar result on the arms posture regarding previous works.

A predictive fuzzy logic and rule-based control approach for practical real-time operation of urban stormwater storage system

Predictive real-time control (RTC) strategies are usually more effective than reactive strategies for the intelligent management of urban stormwater storage systems. However, it remains a challenge to ensure the practicality of RTC strategies that use accessible, non-idealized predictive information while improving their efficiency for successive rainfall events instead of specific phases. This study developed a predictive fuzzy logic and rule-based control (PFL-RBC) approach to address the continuous control of individual storage systems. This approach incorporates total rainfall depth forecast information with an intra-storm fuzzy logic system to optimize peak flow control and several rule-based strategies for pre-storm water detention, reuse, and release control. Computational experiments were conducted using a storage tank case study to test the proposed approach under various rainfall conditions and storage sizes. The results showed that PFL-RBC outperformed static rule-based control in infrequent design storms and realistic continuous rainfall events, reducing flood peaks and volumes by 55 %∼87 % and 7 %∼20 %, respectively, and significantly increasing water detention time and reuse volume. Meanwhile, PFL-RBC required less predictive information to achieve a 6 %∼15 % advantage in peak flow control compared to optimized model predictive control. More importantly, PFL-RBC was reliable in the face of input uncertainty, with <25 % performance loss for water quantity control when the realistic forecast error ranged from -50 % to +50 %. These findings suggest that the proposed approach has great potential to enhance the efficiency and practicality of stormwater storage operations.

Design and implementation of Type-3 intuitionistic fuzzy logic MPPT controller for PV solar system: Comparative study

The performance of a photovoltaic (PV) solar system is affected by partial shading conditions (PSC) and environmental conditions, such as solar irradiance and ambient temperature, which vary throughout the day. This results in variations in the maximum power point (MPP) on the solar PV output characteristic curve. Therefore, various classical MPP tracking (MPPT) techniques have been used to track the MPP and extract maximum power from PV systems. However, these techniques have drawbacks such as lower stability, increased oscillation around the steady state, and slower convergence to the MPP. To overcome this problem, the newly proposed interval Type-3 intuitionistic fuzzy logic (T3IFL) controller has been proposed. The T3IFL MPPT controller combines the uncertainty of Type-3 fuzzy logic (T3FL) controller with intuitionistic concepts. The T3IFL controller is more accurate and offers faster convergence to the MPP under changing climatic and steady-state conditions than classical techniques and T3FL controller. The T3IFL algorithm provides better performance with excellent MPP tracking by controlling the duty cycle of the DC-DC buck converter. Four cases studied were investigated: uniform radiation conditions, a step change in solar radiation with constant temperature, replacing the battery load with the ohmic load with constant radiation and temperature, and partial shading conditions. Experimental validation of the T3IFL was performed on a DC-DC buck converter using real-time hardware-in-the-loop (HIL). Finally, the simulation and experimental results with comparative studies verified the accuracy of the proposed method in tracking the desired value and disturbance/uncertainty attenuation with better response.

Nutrition Temperature and TDS Control System with Fuzzy Logic on Pak Choy Hydroponics (Brassica rapa subsp. chinensis)

Hydroponics is a method of cultivation that does not use soil as a medium, allowing it to be applied in limited spaces such as urban households. One of the vegetable plants that can be grown using hydroponics is pak choy (Brassica rapa subsp. chinensis). To produce healthy pak choy plants that can efficiently absorb nutrients in a hydroponic system, several factors need to be considered, such as the level of Total Dissolved Solids (TDS) in the nutrient solution, nutrient solution temperature, and air humidity in the hydroponic environment. The ideal nutrient solution temperature for hydroponic plants falls within the range of 25-27°C. In this system, a monitoring and control system will be designed to optimize the growth of pak choy plants in a Deep Flow Technique (DFT) hydroponic system. In this system, the nutrient solution temperature will be controlled with a set point of 25°C using an on/off control for a peltier device. To maintain the TDS level at a set point of 1200 ppm in the nutrient solution, fuzzy logic control will be employed, generating timer-based control signals for the nutrient pump A, nutrient pump B, and water pump. The monitoring system will be displayed on an Internet of Things (IoT) dashboard platform, such as ThingSpeak.

A new adaptive fuzzy logic control for nonlinear car active suspension systems based on the time-delay

A new adaptive fuzzy logic control for nonlinear car active suspension systems based on the time-delay

Power Quality Enhancement in High-Voltage Transmission Systems Using STATCOM by Type 1 and Type 2 Fuzzy Logic Controllers

The continuing growth of load demand leads to increased power system complexity and interconnections. During faults and abnormal operating conditions, conventional power systems change the load flow in transmission lines, which may cause voltage drops or swells, and other power quality issues. Voltage regulation is connected to reactive power compensation in system busbars, and fixed capacitor banks are usually used for this purpose, but they are not flexible and dynamic enough to meet the power quality requirements. With power electronics development, flexible alternative current transmission systems (FACTS) became a popular solution for power quality improvement and power transmission capability increment in power systems, and FACTS can be categorized into series and shunt systems. In this study, STATCOM was used to improve the voltage profile in part of Iraq’s high-voltage transmission system during abnormal operating conditions, such as sudden load addition and removal, which causes voltage dip and voltage swell. Type 1 fuzzy logic controller and type 2 fuzzy logic controller were used as the outer controller in the STATCOM; both controllers were able to regulate the voltage magnitude by reducing the voltage dip from 1.32 to 0.4%, voltage swell from 1.02 to 0.5%, and voltage THD at the connecting point did not exceed 3%, indicating that SATACOM with FLC improves the voltage profile under different operating conditions.

Bi-directional DC-DC Converter and Fuzzy Logic Controller Based Switched Reluctance Motor for EV Applications

Bi-directional DC-DC Converter and Fuzzy Logic Controller Based Switched Reluctance Motor for EV Applications

Evaluation of advanced control strategies for PMSG in wind turbines: backstepping fuzzy logic control and adaptive neural network control approaches

This study presents a comparative analysis of two advanced control strategies for Permanent Magnet Synchronous Generators (PMSGs) in wind turbines: Backstepping Fuzzy Logic Control and Adaptive Neural Network Control. The aim is to evaluate their effectiveness in handling structured and unstructured uncertainties and enhancing system performance. Backstepping Fuzzy Logic Control integrates backstepping techniques with fuzzy logic regulation to improve flexibility and robustness. This method addresses the limitations of traditional PI controllers by enhancing adaptability to parameter variations and external disturbances. In contrast, Adaptive Neural Network Control employs neural networks with dynamic compensators to address high uncertainties and nonlinearities, aiming to surpass the performance of conventional PI controllers. The study involves simulations to compare these control strategies based on several criteria: robustness in managing parameter changes and external disturbances, flexibility in adapting to dynamic conditions, and overall performance including efficiency and stability. Simulation results indicate that both strategies offer significant improvements over traditional PI controllers. Backstepping Fuzzy Logic Control demonstrates strong adaptability and robustness, while Adaptive Neural Network Control shows superior efficiency and optimality, particularly in high-uncertainty environments. This comparative analysis provides insights into the strengths and limitations of each approach, offering guidance for selecting the most suitable control strategy based on specific operational requirements. The findings contribute to advancing control methodologies in wind energy systems and suggest directions for future research and practical implementation.