Conventional Fuzzy Logic Controller Research Articles

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.

A Practical performance verification of AFLC based MPPT for standalone PV power system under varying weather condi-tion

For amelioration of tracking efficiency, the Maximum power point trackers (MPPT) are very important for photovoltaic (PV) generation. For this purpose here a reformed adaptive fuzzy logic control (FLC) MPPT tracker has been presented to enhance its overall power efficiency and gives rapid transient response under changing weather conditions. For voltage regulation at load bus, the zeta buck-boost converter has been taken for its least voltage ripple. MATLAB/SIMULINK simulation environment and dSPACE DS1104 real time control board is used to test the proposed adaptive fuzzy logic controller based MPPT in variable irradiance level and ambient tempera-ture. The tracking efficiency in this presented method is analyzed in comparison with standard fuzzy logic controller (FLC) and perturb and observe (P and O) MPPT algorithms. The modified AFLC controller gives better tracking efficiency and precise response compared to conventional fuzzy logic controller and P and O MPPT algorithms. Theoretical and experimental results obtained are demonstrated for improved functioning of the system.

Path Tracking of an Autonomous Ground Vehicle With Different Payloads by Hierarchical Improved Fuzzy Dynamic Sliding-Mode Control

A hierarchically improved fuzzy dynamical sliding-model control (HIFDSMC) is presented to address the autonomous ground vehicle (AGV) path tracking problem. The proposed controller has two portions: one is the virtual desired input (VDI), and the second is the path tracking control (PTC). In addition to the equivalent control in VDI and PTC, an improved fuzzy dynamical sliding-mode control (IFDSMC) is designed to deal with the system uncertainties, e.g., different payloads. Contributions of this paper include the following four parts: 1) Based on the nominal system response, the fuzzy rules and scaling factors of the IFDSMCs in the VDI and PTC are easily chosen. In contrast, a conventional fuzzy logic control approach requires more trial-and-error tuning to obtain a satisfactory performance. 2) The proposed HIFDSMC possesses the tuning mechanism (the coefficients of two sliding surfaces, the scaling factors in indirect and direct modes, and the fine tuning in fuzzy table) such that the uncertainties are tackled without a larger computational burden. 3) The stability of the closed-loop system is verified by the Lyapunov stability with hierarchical concept. 4) Different payloads not at the mass center of the AGV (e.g., greater than 25% in the total weight of the AGV) are tackled by the IFDSMCs to obtain a satisfactory performance. Finally, the application to the assembly line of the AGV with different payloads to track the circular and piecewise straight-line paths by the proposed HIFDSMC is compared with the hierarchical fuzzy decentralized PTC.

A Polar Fuzzy Control Scheme for Hybrid Power System Using Vehicle-To-Grid Technique

A novel polar fuzzy (PF) control approach for a hybrid power system is proposed in this research. The proposed control scheme remedies the issues of system frequency and the continuity of demand supply caused by renewable sources’ uncertainties. The hybrid power system consists of a wind turbine generator (WTG), solar photovoltaics (PV), a solar thermal power generator (STPG), a diesel engine generator (DEG), an aqua-electrolyzer (AE), an ultra-capacitor (UC), a fuel-cell (FC), and a flywheel (FW). Furthermore, due to the high cost of the battery energy storage system (BESS), a new idea of vehicle-to-grid (V2G) control is applied to use the battery of the electric vehicle (EV) as equivalent to large-scale energy storage units instead of small batteries to improve the frequency stability of the system. In addition, EV customers’ convenience is taken into account. A minimal-order observer is used to estimate the supply error. Then, the area control error (ACE) signal is calculated in terms of the estimated supply error and the frequency deviation. ACE is considered in the frequency domain. Two PF approaches are utilized in the intended system. The mission of each controller is to mitigate one frequency component of ACE. The responsibility for ACE compensation is shared among all parts of the system according to their speed of response. The performance of the proposed control scheme is compared to the conventional fuzzy logic control (FLC). The effectiveness and robustness of the proposed control technique are verified by numerical simulations under various scenarios.

Dielectric relaxation process of a partially unwound helical structure in ferroelectric liquid crystals.

The fluctuations of unwound helical structure have been observed in deformed helix ferroelectric liquid crystal (DHFLC) and conventional FLC sample cells. The helix is partially unwound by strong anchoring on the substrates. In such sample cells, the helical decarlization lines are not observed in the texture under crossed polarized microscope. The dielectric spectroscopy is employed to observe the behavior of dielectric relaxation processes in these sample cells. A dielectric relaxation process is observed at a lower frequency than the Goldstone mode processes in DHFLC and FLC, which we call partially unwound helical mode (p-UHM). However, the p-UHM process is not observed in the sample cell in which the helical lines appear. The application of various amplitudes of probing ac voltages on this mode has shown the higher frequency shift, i.e., the larger the amplitude of ac voltage, the higher is the relaxation frequency of p-UHM. At sufficient amplitude of applied probing ac voltage, the p-UHM merges with the Goldstone mode process and is difficult to detect. However, the Goldstone mode relaxation frequency is almost independent of the cell geometry and sample configuration. The electro-optical behavior of the p-UHM has also been confirmed by electro-optical technique. The dielectric relaxation of UHM at a frequency lower than the Goldstone mode is interpreted as the fluctuation of partially unwound helix.

Design of Vectored Sum-Based Fuzzy Logic Control System and Its Application to Segway-Type Mobile Robot

The study of individual mobile devices has been widely conducted. In this paper, we propose the design of some fuzzy logic control systems for the control of the Segway-type mobile robot, which is a kind of inverted pendulum system. We first design two conventional fuzzy logic control systems for position and balance control of the Segway-type mobile robot. And then, we design another two fuzzy logic control systems with a single input variable for position and its balance control. We also propose a new defuzzification method called vectored sum scheme. Finally, in order to check the feasibility of the proposed systems we present some simulation examples.

Real time energy management strategy for a fast charging electric urban bus powered by hybrid energy storage system

In this paper, an innovative real time energy management strategy design approach is proposed for a fast charging electric urban bus with hybrid energy storage system composed of conventional batteries and supercapacitors. After modeling, a multi-objective optimization problem taking into account cycle life of the battery, total energy consumption and specific requirement that minimizing the use of battery is formulated. A quantifiable evaluation model is firstly derived to evaluate different kinds of strategies. Then a conventional fuzzy logic control based energy management strategy with features of intelligence and adaptability is proposed, but the simulation result shows that even after long time tuning it can not achieve the desired result with the manual set membership functions. Thereafter, an optimal energy management strategy based on dynamic programming is developed as a benchmark to see the room for improvement. Finally, an innovative model in the loop optimization approach based on genetic algorithm is proposed to optimize the membership functions of the conventional fuzzy logic based energy management strategy. Simulation results demonstrate that the overall performance of optimized fuzzy logic based energy management strategy can be improved significantly and can even approach the optimal results of dynamic programming.

A Proposed Maximum Power Point Tracking by Using Adaptive Fuzzy Logic Controller for Photovoltaic Systems

This paper presents a proposed method to search maximum power point (MPP) based on the adaptive fuzzy logic control (AFLC) which is applied to photovoltaic (PV) systems under varying temperatures and radiations. The proposed system is composed of boost converter, two fuzzy controllers and load. Whenever environmental conditions change in wide range, using only conventional fuzzy logic controller (CFLC) is not adequate and causes more errors in tracking. The proposed AFLC comprises two stages: Online and Offline tuning. The offline method by accurately setting CFLC controller parameters is applied for relatively stable atmospheric conditions. Meanwhile, the online method is considered for unstable atmospheric conditions and contains two fuzzy controllers-one primary, one secondary. The primary fuzzy controller is the CFLC, and the secondary controller is the decision-making, which due to atmospheric conditions, alters the primary fuzzy controller parameters in order to achieve a better answer compared to utilizing CFLC. Decision making controller with changing in irradiation and temperature changes gain of inputs of CFLC simultaneously that it increases rate and accuracy of tracking in comparison with using only fuzzy controller. By simulating results using CFLC and AFLC controllers, the proposed method is able to improved performance indicators with respect to CFLC.

Self-organizing fuzzy control for dissolved oxygen concentration using fuzzy neural network1

In order to improve the accuracy and adaptive ability of dissolved oxygen concentration control in the wastewater treatment process (WWTP), a self-organizing fuzzy control (SOFC) method is developed in this paper. The main feature of this control system is that the fuzzy controller can extract fuzzy rules automatically using a self-organizing fuzzy neural network (FNN), which can adjust the network structure during the process based on the growing-pruning-combining algorithm. Furthermore, to ensure the convergence of the system, a compensation controller is designed to dispel the FNN approximation error, and the parameter compensation is also considered while adjusting the network structure. Finally, simulation results, based on the international benchmark simulation model No.1 (BSM1), demonstrate that the proposed method can achieve better control performance and superior adaptive ability compared with PID, model predictive control and conventional fuzzy logic controller.

A Nature-Inspired Optimization-Based Optimum Fuzzy Logic Photovoltaic Inverter Controller Utilizing an eZdsp F28335 Board

Photovoltaic (PV) inverters essentially convert DC quantities, such as voltage and current, to AC quantities whose magnitude and frequency are controlled to obtain the desired output. Thus, the performance of an inverter depends on its controller. Therefore, an optimum fuzzy logic controller (FLC) design technique for PV inverters using a lightning search algorithm (LSA) is presented in this study. In a conventional FLC, the procedure for obtaining membership functions (MFs) is usually implemented using trial and error, which does not lead to satisfactory solutions in many cases. Therefore, this study presents a technique for obtaining MFs that avoids the exhaustive traditional trial-and-error procedure. This technique is implemented during the inverter design phase by generating adaptive MFs based on the evaluation results of the objective function formulated with LSA. The mean squared error (MSE) of the inverter output voltage is used as an objective function in this study. LSA optimizes the MFs such that the inverter provides the lowest MSE for the output voltage, and the performance of the PV inverter output is improved in terms of amplitude and frequency. First, the design procedure and accuracy of the optimum FLC are illustrated and investigated through simulations conducted in a MATLAB environment. The LSA-based FLC (LSA-FL) are compared with differential search algorithm (DSA)-based FLC (DSA-FL) and particle swarm optimization (PSO)-based FLC (PSO-FL). Finally, the robustness of the LSA-FL is further investigated with a hardware that is operated via an eZdsp F28335 control board. Simulation and experimental results show that the proposed controller can successfully obtain the desired output when different loads are connected to the system. The inverter also has a reasonably low steady-state error and fast response to reference variation.

Hybrid Fuzzy Controller Based Frequency Regulation in Restructured Power System

This paper discusses the implementation of Load Frequency Control (LFC) in restructured power system using Hybrid Fuzzy controller. The formulation of LFC in open energy market is much more challenging; hence it needs an intelligent controller to adapt the changes imposed by the dynamics of restructured bilateral contracts. Fuzzy Logic Control deals well with uncertainty and indistinctness while Particle Swarm Optimization (PSO) is a well-known optimization tool. Abovementioned techniques are combined and called as Hybrid Fuzzy to improve the dynamic performance of the system. Frequency control of restructured system has been achieved by automatic Membership Function (MF) tuned fuzzy logic controller. The parameters defining membership function has been tuned and updated from time to time using Particle Swarm Optimization (PSO). The robustness of the proposed hybrid fuzzy controller has been compared with conventional fuzzy logic controller using performance measures like overshoot and settling time following a step load perturbation. The motivation for using membership function tuning using PSO is to show the behavior of the controller for a wide range of system parameters and load changes. Error based analysis with parametric uncertainties and load changes is tested on a two-area restructured power system.

Task Space Control of an Autonomous Underwater Vehicle Manipulator System by Robust Single-Input Fuzzy Logic Control Scheme

In this paper, a robust single-input fuzzy logic control Robust Single Input Fuzzy Logic Controller (RSIFLC) scheme is proposed and applied for task-space trajectory control of an autonomous underwater vehicle manipulator system (AUVMS) employed for underwater manipulation tasks. The effectiveness of the proposed control scheme is numerically demonstrated on a planar underwater vehicle manipulator system [consisting of an underwater vehicle and a two link rotary (2R) serial planar manipulator]. The actuator and sensor dynamics of the system are also incorporated in the dynamical model of an AUVMS. The proposed control law consists of a feedforward term to exaggerate the control activity with immoderation from the known desired acceleration vector and an estimated perturbed term to compensate for the unknown effects namely external disturbances and unmodeled dynamics as a first part and a single-input fuzzy logic control as a feedback portion to enhance the overall closed-loop stability of the system as a second part. The primary objective of the proposed control scheme is to track the given end-effector task space trajectory despite of external disturbances, system uncertainties, and internal noises associated with the AUVMS. To show the efficacy of the proposed control scheme, comparison is made with conventional fuzzy logic control (CFLC), sliding mode control (SMC), and proportional–integral–derivative (PID) controllers. Simulation results confirmed that with the proposed control scheme, the AUVMS can successfully track the given desired spatial trajectory and gives better and robust control performance.

Intelligent Controller for UPQC Using Combined Neural Network

The Unified Power Quality Conditioner (UPQC) plays an important role in the constrained delivery of electrical power from the source to an isolated pool of load or from a source to the grid. The proposed system can compensate voltage sag/swell, reactive power compensation and harmonics in the linear and nonlinear loads. In this work, the off line drained data from conventional fuzzy logic controller. A novel control system with a Combined Neural Network (CNN) is used instead of the traditionally four fuzzy logic controllers. The performance of combined neural network controller compared with Proportional Integral (PI) controller and Fuzzy Logic Controller (FLC). The system performance is also verified experimentally.

A Time-Varying Strategy for Urban Traffic Network Control: A Fuzzy Logic Control Based on an Improved Black Hole algorithm

Urban traffic network (UTN) models are usually illustrated by state-charts (CH) and object-diagrams (OD). So, a state space model is usually used to calculate the half-value waiting time of vehicles in UTN with a stabled time control. In this study, a combination of the general type-2 fuzzy logic sets (GT2FLS) and the modified black hole (MBH) algorithm techniques is used in order to control the traffic signal scheduling and phase succession so as to guarantee the smooth flow of traffic with the least waiting time and average queue length. A comparison is made between the results obtained regarding the performance of the proposed model and those of the optimal type-1 fuzzy logic (OT1FL) and conventional type-1 fuzzy logic (CT1FL) controllers, which are the most recent researches in the problem at hand. The simulation results prove the successfulness and effectiveness of the proposed controller.

PSO Optimized Load Frequency Control of Two Area Power System in Deregulated Environment

The Load frequency control (LFC) of power systems is one of the challenges faced under deregulated conditions. To attain the LFC optimal tuning of the controller is needed. This article proposes a solution for the LFC of two area thermalthermal power systems under deregulated conditions. The controller used is Particle Swarm Optimization (PSO) tuned fuzzy logic controller (FLC). The regions are interrelated by means of standard AC tie-lines. The effects of bilateral agreements between distribution companies and generation companies are also considered. To enhance optimization synthesis and to minimize the fuzzy system work the membership functions of the FLC are tuned by PSO technique. The optimal tuning is obtained by optimizing the scaling gains of the FLC. The proposed controller is evaluated under various operating conditions in a deregulated two area environments. The simulation is carried out using Simulink/MATLAB. The PSO tuned controller showed better performance with respect to conventional Proportional Integral (PI) controller and non-tuned FLC controller.

Stabilizing Frequency Oscillations Of A Two Area Thermal-Thermal Power System In Deregulated Environment Using Particle Swarm Optimized Fuzzy Logic Controller

The Automatic Generation Control (AGC) of power systems is one of the challenges faced under deregulated conditions. This article proposes a solution for the AGC of two area thermal-thermal power systems under deregulated conditions. The controller used is fuzzy logic controller and it is tuned using Particle Swarm Optimization (PSO) technique. The regions are interrelated by means of standard AC tie-lines. The effects of bilateral agreements between distribution companies (DISCOs) and generation companies (GENCOs) are also considered. To enhance optimization synthesis and to minimize the fuzzy system work the membership functions of the FLC are tuned by PSO technique. The optimal tuning is obtained by optimizing the scaling gains of the FLC. The proposed controller is evaluated under various operating conditions in a deregulated two area environment. The simulation is carried out using Simulink/MATLAB. The PSO tuned controller showed better performance with respect to conventional Proportional Integral (PI) controller and non-tuned FLC controller.

Design of Simple-Structured Fuzzy Logic Systems for Segway-Type Mobile Robot

Studies on the control of the inverted pendulum type system have been widely reported. This is because it is a typical complex nonlinear system and may be a good model for verifying the performance of a proposed control system. In this paper, we propose the design of some fuzzy logic control (FLC) systems for controlling a Segway-type mobile robot, which is an inverted pendulum type system. We first derive a dynamic model of the Segway-type mobile robot and then analyze it in detail. Next, we propose the design of some FLC systems that have good performance for the control of any nonlinear system. Then, we design two conventional FLC systems for the position and balance control of the Segway-type mobile robot, and we demonstrate their usefulness through simulations. Next, we point out the possibility of simplifying the design process and reducing the computational complexity,, which results from the skew symmetric property of the fuzzy control rule tables. Finally, we design two other FLC systems for position and balance control of the Segway-type mobile robot. These systems have only one input variable in the FLC systems. Furthermore, we observe that they offer similar control performance to that of the conventional two-input FLC systems.

Evaluation of bending limit curves of aluminium alloy AA6014-T4 and dual phase steel DP600 at ambient temperature

Bending/hemming operations are extensively used in automotive industries for assembling the car body panels. Besides the process mechanics, bending operation differs from biaxial sheet metal forming operations in failure mechanism also. Hence, the limit strains that material can sustain are different for both the operations. Thus, the conventional FLC proposed by Keeler and Goodwin fails to predict formability in bending/hemming operations. This necessitates the development of bending limit curves. In this work, bending limit curves are determined experimentally for AA6014-T4 and DP600. Effect of punch radius, nature and level of pre-strain on the bending limits is also studied. Thus, BLC can be used as a post-processing criterion in finite element simulations to assess formability during bending/hemming operations.

A simplified yet effective fuzzy logic controller for chemical ship tanker

This paper proposes a simplified yet effective control scheme to design a fuzzy logic controller (FLC) for the ship tanker. The proposed method exploits the feature of signed distance method for reducing the conventional two-input FLC (CFLC) into a piecewise linear single input FLC (PWL-FLC). It has been shown that the proposed FLC has a similar structure to the CFLC, exhibiting analogous control performance. However, the main features of the proposed method are the absence of fuzzification, rule inference and defuzzification processes that are essential for CFLC implementation. The proposed PWL-FLC could be easily programmed into a low cost microcontroller using just a look-up table. To authenticate its effectiveness, the control algorithm has been implemented using the marine system simulator in Matlab/Simulink ® platform. The result signifies that both the PWL-FLC and CFLC results in identical functioning performance; however, the former requires least possible tuning effort and its execution time is in the orders of three magnitudes less than its conventional counterpart.