Conventional Fuzzy Logic Controller Research Articles

Fuzzy Sliding Mode Active Disturbance Rejection Control of an Autonomous Underwater Vehicle-Manipulator System

In this paper, a fuzzy sliding mode active disturbance rejection control (FSMADRC) scheme is proposed for an autonomous underwater vehicle-manipulator system (AUVMS) with a two-link and three-joint manipulator. First, the AUVMS is separated into nine subsystems, and the combined effects of dynamic uncertainties, hydrodynamic force, unknown disturbances, and nonlinear coupling terms on each subsystem are lumped into a single total disturbance. Next, a linear extended state observer (LESO) is presented to estimate the total disturbance. Then, a sliding mode active disturbance rejection control (SMADRC) scheme is proposed to enhance the robustness of the control system. The stability of the SMADRC and the estimation errors of the LESO are analyzed. Because it is difficult to simultaneously adjust several parameters for a LESO-based SMADRC scheme, a fuzzy logic control (FLC) scheme is used to formulate the FSMADRC to determine the appropriate parameters adaptively for practical applications. Finally, two AUVMS tasks are illustrated to test the trajectory tracking performance of the closed-loop system and its ability to reject and attenuate the total disturbance. The simulation results show that the proposed FSMADRC scheme achieves better performance and consume less energy than conventional PID and FLC techniques.

A Fuzzy Logic and Artificial Neural Network-Based Intelligent Controller for a Vehicle-to-Grid System

With a shift toward increasing usage of electric vehicles (EVs) for mobility, the demand for power from the electric grid to recharge EVs is imminent to increase. Vehicle to grid (V2G) is an emerging technique used to integrate EVs with electric grid for meeting this demand and mitigating the impact of EV recharging on the grid. V2G use an extensive communication network to exchange data between various entities, including the grid, charging station (CSs), and the EVs. There are possibilities of errors in the data communicated between entities of the V2G system. These errors, if not addressed properly, would create unwanted disturbances in the electric grid. The V2G controller plays a significant role in the integration of EVs to the grid. It does so by deciding the right amount of power to be exchanged between them based on the inputs communicated by the grid and the CSs. Hence, this article proposes a two-layer framework intelligent controller comprising of a data integrity and correction check block in the first layer and a rule-based fuzzy logic controller (FLC) in the second layer. The proposed controller's effectiveness is validated using a simulation model, which comprises of an electricity distribution network and a communication network. Results demonstrate the intelligent controller's potential in reducing the impact of errors by deciding the right amount of power to be exchanged between the grid and EVs. A comparison between the conventional FLC and the proposed intelligent controller further corroborates the potential.

Power management of hybrid energy storage system in a standalone DC microgrid

Abstract Standalone microgrids with renewable energy sources (like solar photovoltaic and wind systems) utilize energy storage devices (ESDs) to supply uninterrupted power to their system loads. To utilize the characteristic advantages of different ESDs, they are combined to form a hybrid energy storage system (HESS). Efficient control algorithms are necessary for power sharing among the multiple ESDs in a HESS to ensure maximum utilization of all the storage devices in the microgrid. Conventional fuzzy logic controllers (FLCs) are used to decide the power output of an ESD considering state of charge (SoC) of the fuzzy controlled ESD and the overall power demand (or availability) in the microgrid. However in a standalone DC microgrid with multiple ESDs, such conventional FLC algorithms cause over or under utilization of the ESDs since the instantaneous SoC status of other ESDs are not considered in its control logic. Thus a novel adaptive FLC (AFLC) algorithm is proposed in this paper to dynamically change the controlling rules of the AFLC as per the average SoC of other ESDs of the microgrid. The performance of the proposed AFLC algorithm is compared with the conventional FLC algorithm on the basis of duration of discharge, average power delivered and utilization factor of the ESDs in a DC microgrid using real time simulation study in OPAL RT 4510 simulator. The merits of using the AFLC for power sharing among multiple ESDs are further validated through experimental study performed on a microgrid hardware setup at Indian Institute of Technology Kharagpur, India.

A modified self-tuning fuzzy logic temperature controller for metal induction heating.

This paper presents a method to build a dynamic target curve producer corresponding to the rising time setting and the ultimate target temperature as a reference for a fuzzy logic controller that is used in the metal heating process application. To achieve this goal, there are some quantization factors in a fuzzy controller that must be set according to the system situation, as well as the experience of experts that will cause the controller to have a lack of adaptivity. To solve this problem, in this paper, all the quantization factors are analyzed thoroughly, and a self-tuning module is designed to make it possible for the controller to perform real-time adjustments based on the system situation and, eventually, make it more adaptive. During the design process, a simulation comparing the control capabilities of the conventional fuzzy logic controller and the self-tuning fuzzy logic controller (STFLC) is made using a finite element analysis. Finally, experiments are carried out on the induction heating system to verify the effect of the proposed STFLC. The results show that, with the proposed self-tuning module, the control capability and adaptivity of the controller were drastically improved.

Fault mitigation through multi converter UPQC with hysteresis controller in grid connected wind system

The ability of parallel compensation for voltage and current in bus system is known as Multi Converter Unified Power Quality Conditioner (MC-UPQC). It improve the Power Quality (PQ) of the renewable energy sources. The combination of current and voltage quality is known as PQ. The PQ problems, named as imbalance, flicker, and harmonics, the electronically switched and nonlinear devices with increasing applications in distribution systems and industries have become serious concerns. In this paper, improved fuzzy-based MC-UPQC is proposed for improving the PQ in grid connected wind energy system (WES). Improved fuzzy, that is hybrid fuzzy incremental conductance controlled MC-UPQC is the novelty in this study. Fuzzy incremental conductance is introduced to search the maximum power point in wind energy conversion system. From the grid, the proposed controller joined with MC-UPQC improve the WES. At the same time, the system can recompense the current and voltage imperfections in adjacent feeders. A normal dc-link capacitor is shared by all converters in this configuration. Hence, the power transfer from one to adjacent feeders is possible. The proposed work is simulated in MATLAB/SIMULINK platform. Through the hysteresis controller, the performance analysis is carried out. Finally the results are compared with existing controller’s namely proportional integral and a conventional fuzzy logic controller. Thus, the overall performance can display the effectiveness of the proposed approach. Compared to other two existing approaches, the proposed scheme achieves very low THD values i.e. 3.53% for load 1 and 3.25% for load 2.

Performance evaluation of PI and PI-fuzzy logic controller FPGA based permanent magnet synchronous motor drive

This paper depicts an experimental validation and simulation for speed control under load and no load conditions. It delineates the detail study of a closed loop vector controlled permanent magnet synchronous motor drive. This system consists of two loops for smooth speed tracking as well as reduced torque undulation. PMSM drives facilitate better dynamic responses during both steady state and dynamic conditions by monitoring the component of torque current. The resistive loads are varied and resultant speed and torque are studied. Experimental work has been performed on PMSM drives under different conditions and results are compared with conventional PI and Fuzzy logic controller. Â

Adaptation mechanism techniques for improving a model reference adaptive speed observer in wind energy conversion systems

Rotational speed sensor is one of the most important components used in wind energy conversion system control strategies. The latter is very expensive and may be faulty due to the harsh environment working, causing by that a low efficiency operation of the system. The use of a speed estimator or an observer may be a good alternative solution for the use either in sensorless control or detecting the sensor failure or degradation (FDI and FTC), provided that the observer is robust and ensures high rotational speed estimation accuracy. This paper presents a comprehensive study to solve the optimization problem of a model reference adaptive speed (MRAS) observer in a typical wind energy generation system based on permanent magnet synchronous generator using five adaptation mechanisms: The first one is the classical MRAS observer; it is based on proportional–integral (PI) controller. The second one uses a fuzzy logic controller (FLC) with two inputs. The third one uses the single-input fuzzy logic controller which represents the simplification of the conventional FLC. The fourth one uses the sliding mode controller, and the last one uses the super-twisting algorithm. A detailed comparison between the five adaptation mechanisms is carried out. The obtained results show a good estimation stability as well as a fast speed estimation at dynamic regime for the improved adaptation mechanisms compared with the conventional PI controller.

CONVENTIONAL FUZZY LOGIC CONTROLLER FOR BALANCING TWO-WHEEL INVERTED PENDULUM

This paper presents the design and the Real-Time implementation of self-balancing Two-Wheeled Inverted Pendulum (TWIP) using state-feedback controller and a conventional fuzzy logic controller (CFLC) on Real-Time. The state-feedback controller consists of two parts PD controller and PI controller. The state-feedback controller was designed first for the nonlinear model then it was updated for the Real-Time implementation. The CFLC was designed first based on the state-feedback controller to reach the point of using pure fuzzy controller for the TWIP. The CFLC was designed with Mamdani fuzzy inference architecture if-then rules. The TWIP is balanced with CFLC with a base of 7 rules only. Different controller’s types were tested on real-Time to perform a good balance for the TWIP. Fuzzy PI, Fuzzy PD, and combination of two independent CFLC were used to achieve the balancing of the TWIP on Real-Time.

Coordinated Control of Aichi Microgrid for Efficient Power Management Using Novel Set Point Weighting Iterative Learning Controller

A novel Set Point Weighting Iterative Learning Controller (SPW-ILC) has been proposed for voltage stabilization at AC/DC bus, coordinated control among the distributed sources in the modeled hybrid microgrid (HMG) and synchronization of HMG with utility grid. The Aichi Micro grid test system located at Aichi Institute of Technology, Japan has been considered for the simulation studies and modeled in MATLAB/Simulink environment. The Aichi microgrid can be operated in autonomous mode as AC system and DC system. When it is working as DC system, the dc bus voltage is maintained stable by incorporating dedicated fuzzy logic controllers (FLC) for DC-DC converters due to the variable distributed sources. Meanwhile, the bidirectional converter also called as Interlinking Converter (IC) located between ac bus and dc bus controlled by proposed SPW-ILC converts the DC voltage into AC voltage and meets AC loads. In AC system of autonomous mode, the inverters are controlled by proposed controller to meet the ac demands. The grid connected mode of Aichi microgrid system is performed by properly controlling the IC to meet ac and dc loads. The proposed SPW-ILC reduces the voltage deviation and maintains the power balance under variable source and load conditions. The results have been compared with the conventional proportional integral (PI) controller and FLC to validate the performance of the controller. The results show that the proposed SPW-ILC has efficiently control the voltage and maintain the power balance.

Robust and Intelligent Type-2 Fuzzy Fractional-Order Controller-Based Automatic Generation Control to Enhance the Damping Performance of Multi-Machine Power Systems

ABSTRACT In terms of power generation, Automatic Generation Control (AGC) has taken care of the interchange power flow deviations in order to sufficiently alleviate both the frequency and tie-line power deviations as a perturbation occurs in the interconnected multi-area power system. To provide a high-quality electrical power, a robust and intelligent controller can effectively augment the operational function of the AGC system. The wide-ranging bandwidth, flexible memory and uniform phase contribution in the Fractional Order Proportional Integral Derivative (FOPID) frequency response have been utilized to enhance the dynamic performance of control system against the various power system uncertainties. Since type-2 Fuzzy Logic Controller can significantly overcome the drawbacks of type-1 FLC, it has been satisfactorily combined with FOPID to more abate the low- frequency oscillations. The so-called T2FLC-FOPID has been thoroughly evaluated in two distinct multi-machine power systems under different perturbations. The dynamic stability strategy is formulated as a nonlinear constrained multi-objective optimization problem. As a result, T2FLC-FOPID parameters have been optimally extracted using Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). The dynamic performance of T2FLC-FOPID has been more validated when it has been compared with the conventional PID, FLC and T1FLC-FOPID.

A Highly-Efficient Fuzzy-Based Controller With High Reduction Inputs and Membership Functions for a Grid-Connected Photovoltaic System

Most conventional Fuzzy Logic Controller (FLC) rules are based on the knowledge and experience of expert operators: given a specific input, FLCs produce the same output. However, FLCs do not perform very well when dealing with complex problems that comprise several input variables. Hence, an optimization tool is highly desirable to reduce the number of inputs and consequently maximize the controller performance, leading to easier maintenance and implementation. This paper, presents an enhanced fuzzy logic controller applied to a photovoltaic system. Specifically, both inputs and membership functions are reduced, resulting in a Highly Reduced Fuzzy Logic Controller (HRFLC), to model a 100kW gridconnected Photovoltaic Panel (PV) as part of a Maximum Power Point Tracking (MPPT) scheme. A DC to DC boost converter is included to transfer the total energy to the grid over a three-level Voltage Source Converter (VSC), which is controlled by varying its duty cycle. FLC generates control parameters to simulate different weather conditions. In this study, only one input representing the current variation (ΔI) of the FLC is used to provide an effective and accurate solution. This reduction in simulation inputs results in a novel HRFLC which simplifies the solar electric system design with output Membership Functions (MFs). Both are achieved by grouping two rules instead of using an existing state-of-the-art method with twenty-five MFs. To the best of our knowledge, this is the first FLC able to provide such rules compression. Finally, a comparison with different techniques such as Perturb and Observe (PandO) shows that HRFLC can improve the dynamic and the steady state performance of the PV system. Notably, experimental results report a steady state error of 0.119%, a transient time of 0.28s and an MPPT tracking accuracy of 0.009s.

Optimization of fuzzy logic controller parameters using modern meta-heuristic algorithm for gantry crane system (GCS)

Robust control of underactuated nonlinear systems is a challenging task using conventional methods because of uncertainties which may lead to failure especially for mechanical and robotic applications. Fuzzy logic controller (FLC) is one of the methods specifically used for designing a robust controller that is able to deal with nonlinearity and achieve satisfactory performance for control applications. FLC uses heuristic information or expert operator knowledge to create its mechanism. However, nonlinearities exist in the control systems make its rule-based FLC design a non-trivial task. Manual trial and error tuning method is common way used for tuning the parameters of FLC to achieve the desired performance of the system. Hence, an automatic tuning using optimization algorithm is necessary. Meta-heuristic optimization algorithms are applied to tune the parameters of FLC automatically. In this paper, three popular meta-heuristic algorithms are used to optimize the scaling factors of FLC, i.e. Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Differential Evolution (DE) to improve the effectiveness of FLC design. A gantry crane system, which is a nonlinear system, is used as test system for this project. The simulation of the optimization of FLC parameters by using meta-heuristics algorithms was successfully achieved. The simulation results show that the optimization techniques for FLC are effective to improve the performance of conventional FLC for gantry crane control, i.e. position control and anti-swing control. In addition, PSO performs better than CS and DE in optimization of FLC for gantry crane system.

Speed control of brushless direct current motor using a genetic algorithm–optimized fuzzy proportional integral differential controller

In this article, a genetic algorithm–based proportional integral differential–type fuzzy logic controller for speed control of brushless direct current motors is presented to improve the performance of a conventional proportional integral differential controller and a fuzzy proportional integral differential controller, which consists of a genetic algorithm–based fuzzy gain tuner and a conventional proportional integral differential controller. The tuner is used to adjust the gain parameters of the conventional proportional integral differential controller by a new fuzzy logic controller. Different from the conventional fuzzy logic controller based on expert experience, the proposed fuzzy logic controller adaptively tunes the membership functions and control rules by using an improved genetic algorithm. Moreover, the genetic algorithm utilizes a novel reproduction operator combined with the fitness value and the Euclidean distance of individuals to optimize the shape of the membership functions and the contents of the rule base. The performance of the genetic algorithm–based proportional integral differential–type fuzzy logic controller is evaluated through extensive simulations under different operating conditions such as varying set speed, constant load, and varying load conditions in terms of overshoot, undershoot, settling time, recovery time, and steady-state error. The results show that the genetic algorithm–based proportional integral differential–type fuzzy logic controller has superior performance than the conventional proportional integral differential controller, gain tuned proportional integral differential controller, conventional fuzzy proportional integral differential controller, and scaling factor tuned fuzzy proportional integral differential controller.

Improved MPPT method to increase accuracy and speed in photovoltaic systems under variable atmospheric conditions

The changes in temperature and radiation cause visible fluctuations in the output power produced by the photovoltaic (PV) panels. It is essential to keep the output voltage of the PV panel at the maximum power point (MPP) under varying temperature and radiation conditions. In this study, a maximum power point tracking (MPPT) method has been developed which is based on mainly two parts: the first part is adapting calculation block for the reference voltage point of MPPT and the second one is Fuzzy Logic Controller (FLC) block to adjust the duty cycle of PWM applied switch (Mosfet) of the DC-DC converter. In order to evaluate the robustness of the proposed method, Matlab/Simulink program has been used to compare with the traditional methods which are Perturb & Observe (P&O), Incremental Conductance (Inc. Cond.) and FLC methods under variable atmospheric conditions. When the test results are observed, it is clearly obtained that the proposed MPPT method provides an increase in the tracking capability of MPP and at the same time reduced steady state oscillations. The accuracy of the proposed method is between 99.5% and 99.9%. In addition, the time to capture MPP is 0.021 sec. It is about four times faster than P&O and five times faster than for Inc. Cond. and, furthermore, the proposed method has been compared with the conventional FLC method and it has been observed that the proposed method is faster about 28% and also its efficiency is about 1% better than FLC method.

Fuzzy-Controller-Designed-PV-Based Custom Power Device for Power Quality Enhancement

In this paper, a three-phase series hybrid active filter (SEHAF) interconnected with photovoltaic (PV) system and dc–dc boost converter is proposed to minimize sag, swell, and harmonics caused due to nonlinear power electronic loads. The SEHAF consists of a voltage source inverter (VSI) with a capacitor connected across it to provide consistency in managing and compensating the reactive power. This minimizes the sag, swell, and harmonics present in the source and load voltages. With the integration of PV, the voltage across the dc-link capacitor of VSI is controlled effectively, which helps in better compensation. Reference current generation is done using the proposed robust extended complex Kalman filter (RECKF) technique. The performance of the PV-integrated-HAF is analyzed using a synchronous reference frame with proportional-integral (PI) as well as fuzzy logic controller (FLC) and is compared with the proposed RECKF technique. The PV-integrated hybrid power system is developed using MATLAB/SIMULINK. Further, real-time digital simulation using OPAL-RT OP5142 is also carried out to support the simulation results. It is observed that the proposed control scheme provides better harmonic compensation compared to conventional PI and FLC.

An Optimized Fuzzy Logic Control Model Based on a Strategy for the Learning of Membership Functions in an Indoor Environment

The Mamdani fuzzy inference method is one of the most important fuzzy logic control (FLC) techniques and has several applications in different fields. Despite its applications, the Mamdani fuzzy inference method has some core issues which still require solutions. The most critical issue is the selection of accurate shape and boundaries of membership functions (MFs) in the universe of discourse. In this work, we introduced a methodology called learning to control (LtC) to resolve the problem. The proposed methodology consisted of two main modules, namely, a control algorithm (CA) module and a learning algorithm (LA) module. In the CA module, the Mamdani FLC method has been used, whereas, in the LA module, we have used the artificial neural network (ANN) algorithm. Inputs into the ANN were the error difference between environmental temperature and the required temperature. The output of the ANN was the MF set to the FLC. Inputs into the fuzzy logic controller (FLC) were the error difference between environmental temperature and required temperature (D), and the output was the required power for the fan actuator. The purpose of the ANN was to tune the MFs of the FLC to improve its efficiency. The proposed learning-to-control method along with the conventional fuzzy logic controller method was applied to the data to evaluate the model’s performance. The results indicate that the proposed model’s performance is far better than that of conventional fuzzy logic techniques.

Energy Management using Optimal Fuzzy Logic Control in Wireless Sensor Network

Energy is a key factor that affects the lifetime of wireless sensor network (WSN). This paper proposes an adaptive energy management model to improve the energy efficiency in WSN. Unlike existing clustering routing protocols, the overall performance indicators are introduced as the inputs of fuzzy logic control (FLC). Meanwhile, the probability adjustment value, as the out of FLC, is fed back to the network for the generation of new clusters. Since the design of membership functions (MFs) of FLC has a significant impact on system performance, a particle swarm optimization (PSO) algorithm is used to optimize MFs and its optimization goal is to reduce the number of dead nodes and increase the remaining energy level in WSN. Simulation experiments were conducted for the low energy adaptive clustering hierarchy protocol (LEACH), the conventional FLC, FLC using genetic algorithm (GA), and FLC using PSO. The results show that the proposed FLC-PSO has the best performance among the four protocols and it can be used efficiently in energy management of WSN.

Impact of Increasing Stator Resistance on Active Disturbance Rejection Control Based Sensorless Induction Motor Compared with a Conventional PI and Fuzzy Logic Control

Impact of Increasing Stator Resistance on Active Disturbance Rejection Control Based Sensorless Induction Motor Compared with a Conventional PI and Fuzzy Logic Control

A novel beta parameter based fuzzy-logic controller for photovoltaic MPPT application

In this paper, a novel beta parameter three-input one-output fuzzy-logic based maximum power point tracking (MPPT) algorithm is presented for the photovoltaic (PV) system application. The conventional fuzzy-logic controllers (FLCs) exhibit obvious limitations such as their dependence on the user's knowledge about the system and complicated rules. Furthermore, they show inherent dilemma between the rules number of FLC and the universality for various operating conditions, which is revealed and explained with details in this paper. Thus, a novel FLC is proposed by introducing a third input: an intermediate variable β. It can simplify the fuzzy rule membership functions and cover wider operating conditions. The dependence on the user's knowledge about the system is reduced. The converging speed for transients is improved and oscillations around the MPPs are completely eliminated compared with conventional MPPT methods. Typical operation conditions such as varying solar irradiation and load resistance are tested for fair comparison of various algorithms. An experimental prototype was designed and main experimental results were presented to verify the advantages of the proposed algorithm.

Hierarchical Fuzzy Control Applied to Parallel Connected UPS Inverters Using Average Current Sharing Scheme

Parallel connection of UPS inverters to enhance power rating is a widely accepted practice. Inter-modular circulating currents appear when multiple inverter modules are connected in parallel to supply variable critical load. Interfacing of modules henceforth requires an intensive design, using proper control strategy. The potentiality of human intuitive Fuzzy Logic (FL) control with imprecise system model is well known and thus can be utilised in parallel-connected UPS systems. Conventional FL controller is computational intensive, especially with higher number of input variables. This paper proposes application of Hierarchical-Fuzzy Logic control for parallel connected Multi-modular inverters system for reduced computational burden on the processor for a given switching frequency. Simulated results in MATLAB environment and experimental verification using Texas TMS320F2812 DSP are included to demonstrate feasibility of the proposed control scheme.