Fuzzy Logic Control Strategy Research Articles

Optimal control of state‐of‐charge of superconducting magnetic energy storage for wind power system

The optimal control of state-of-charge (SOC) for superconducting magnetic energy storage (SMES), which is used to smooth power fluctuations from wind turbine, is essential to improve its technical and economical performance. Without an efficient control strategy, the SMES may go to the state of over-charge or deep-discharge, which will pose a significant effect on its service life and its technical performance. In this study, combined with wind forecasting technology and real-time monitoring of SOC for the SMES, a double fuzzy logic control strategy is proposed that is applied to regulate SOC of SMES with the purpose of not only effectively smoothing the power fluctuations of wind turbine, but also preventing the SMES from occurring of the state of over-charge/deep-discharge and adjusting it to the appropriate SOC. The effectiveness of the proposed control strategy is verified by the simulation results.

Power Factor Correction Using Fuzzy Logic Control

Harmonic pollution and low power factor in power systems caused by power converters have been a great concern. To overcome these problems several converter topologies and control schemes have been proposed in recent years. This paper is proposed to study the control techniques for such converters to improve the Power Factor (PF) and reduce total harmonic distortion (THD) in the input current with output voltage regulation. A new fuzzy logic control strategy for boost PFC is presented in this paper. Here the controlling action was established through a fuzzy Logic controller. The proposed Fuzzy Logic control system is a two input one output Fuzzy Logic Controller. The proposed PFC control is derived based on Boost converter operates at continuous conduction mode and the switching frequency is much higher than the line frequency so that input voltage can be assumed as a constant during one switching cycle.

Semi-active structural fuzzy control with MR dampers subjected to near-fault ground motions having forward directivity and fling step

Semi-active control equipments are used to effectually enhance the seismic behavior of structures. Magneto-rheological (MR) dampers are semi-active devices that can be utilized to control the response of structures during seismic loads and have received voracious attention for response suppression. They supply the adaptability of active devices and stability and reliability of passive devices. This paper presents an optimal fuzzy logic control scheme for vibration mitigation of buildings using magneto-rheological dampers subjected to near-fault ground motions. Near-fault features including a directivity pulse in the fault-normal direction and a fling step in the fault-parallel direction are considered in the requisite ground motion records. The membership functions and fuzzy rules of fuzzy controller were optimized by genetic algorithm (GA). Numerical study is performed to analyze the influences of near-fault ground motions on a building that is equipped with MR dampers. Considering the uncontrolled system response as the base line, the proposed method is scrutinized by analogy with that of a conventional maximum dissipation energy (MED) controller to accentuate the effectiveness of the fuzzy logic algorithm. Results reveal that the fuzzy logic controllers can efficiently improve the structural responses and MR dampers are quite promising for reducing seismic responses during near-fault earthquakes.

Research and Simulation of Vehicle System Based on a Vehicular Waste-Heat Thermoelectric Generator

Study on the thermoelectric effect of electric on vehicle performance problems. Local fuzzy logic control strategy based on, the application of ADVISOR simulation software, The established vehicle model of the hybrid system simulation, the simulation results show that, better improve the vehicle fuel economy and emissions, make the motor work in high load area, high efficiency with low emissions. At the same time system engine, motor, transmission system and battery efficiency can be better maintained.

Metaheuristic Design and Optimization of Fuzzy-Based Gas Turbine Engine Fuel Controller Using Hybrid Invasive Weed Optimization/Particle Swarm Optimization Algorithm

This paper presents the metaheuristic design and optimization of fuzzy-based gas turbine engine (GTE) fuel flow controller by means of a hybrid invasive weed optimization/particle swarm optimization (IWO/PSO) algorithm as an innovative guided search technique. In this regard, first, a Wiener model for the GTE as a block-structured model is developed and validated against experimental data. Subsequently, because of the nonlinear nature of GTE, a fuzzy logic controller (FLC) strategy is considered for the engine fuel system. For this purpose, an initial FLC is designed and the parameters are then tuned using a hybrid IWO/PSO algorithm where the tuning process is formulated as an engineering optimization problem. The fuel consumption, engine safety, and time response are the performance indices of the defined objective function. In addition, two sets of weighting factors for objective function are considered, whereas in one of them savings in fuel consumption and in another achieving a short response time for the engine is a priority. Moreover, the optimization process is performed in two stages during which the database and the rule base of the initial FLC are tuned sequentially. The simulation results confirm that the IWO/PSO-FLC approach is effective for GTE fuel controller design, resulting in improved engine performance as well as ensuring engine protection against physical limitations.

Embedded system based on a real time fuzzy motor speed controller

This paper describes an implementation of a fuzzy logic control (FLC) system and a/the conventional proportional-integral (PI) controller for speed control of DC motor, based on field programmable gate array (FPGA) circuit. The proposed scheme is aimed to improve the tracking performance and to eliminate the load disturbance in the speed control of DC motors. The proposed fuzzy system has been applied to a permanent magnet DC motor, via a configuration of H-bridge. The fuzzy control algorithm is designed and verified with a nonlinear model, using the MATLAB® tools. Both FLC and conventional PI controller hardware are synthesized, functionally verified and implemented using Xilinx Integrated Software Environment (ISE) Version 11.1i. The real time implementation of these controllers is made on Spartan-3E FPGA starter kit (XC3S500E). The practical results showed that the proposed FLC scheme has better tracking performance than the conventional PI controller for the speed control of DC motors.

RETRACTION

This article refers to:A fuzzy logic control scheme for a solar photovoltaic system for a maximum power point tracker

DSP based fuzzy and conventional overcurrent relay controller comparisons

Fuzzy logic control uses linguistic approach to solve complicated rules and ambiguous systems. This control strategy can be used to improve the overall performance of an overcurrent relay for power system protection compared to conventional relay. It is essential for a relay to work efficiently to trip the circuit breakers in the presence of faults and at the same time proficient to coordinate well with the networks to avoid mal-operation. There are two different types of fuzzy logic control strategies proposed for the relay, the Fuzzy Logic Controller (FLC) and Fuzzy Bang-Bang Controller (FBBC). The FBBC is the same as the conventional FLC except that the defuzzification method uses largest of maxima (LOM). Comparisons between the fuzzy controllers and conventional relay are based on IEC 255-3 standard. These relays are implemented on a DSP TMS320F2812 and their performance is evaluated which is based on operation time, DSP’s execution time and grading margin. The results obtained show a significant performance improvement compared to conventional relay.

Genetic algorithm based fuzzy logic control for a magneto-rheological suspension

To overcome the limitations of conventional fuzzy logic control strategies, an adaptive fuzzy logic control based on a hybrid Taguchi genetic algorithm is proposed to control the vibration of the magneto-rheological suspension in order to promote ride comfort. A half-car model equipped with two telescopic magneto-rheological dampers is first developed. An adaptive fuzzy logic control based on a hybrid Taguchi genetic algorithm is then formulated on the basis of the developed model. The hybrid Taguchi genetic algorithm is applied to tune the linguistic variables and control rules of the fuzzy logic control. Finally, a road test is carried out to validate the proposed control scheme. For comparison purposes, a conventional fuzzy logic control is also implemented on the test car. The results show that the magneto-rheological suspension system with two control strategies can improve ride comfort, and the proposed control algorithm has better ride improvement than conventional fuzzy logic control.

Cooperative Control of Regenerative Braking and Antilock Braking for a Hybrid Electric Vehicle

A new cooperative braking control strategy (CBCS) is proposed for a parallel hybrid electric vehicle (HEV) with both a regenerative braking system and an antilock braking system (ABS) to achieve improved braking performance and energy regeneration. The braking system of the vehicle is based on a new method of HEV braking torque distribution that makes the antilock braking system work together with the regenerative braking system harmoniously. In the cooperative braking control strategy, a sliding mode controller (SMC) for ABS is designed to maintain the wheel slip within an optimal range by adjusting the hydraulic braking torque continuously; to reduce the chattering in SMC, a boundary-layer method with moderate tuning of a saturation function is also investigated; based on the wheel slip ratio, battery state of charge (SOC), and the motor speed, a fuzzy logic control strategy (FLC) is applied to adjust the regenerative braking torque dynamically. In order to evaluate the performance of the cooperative braking control strategy, the braking system model of a hybrid electric vehicle is built in MATLAB/SIMULINK. It is found from the simulation that the cooperative braking control strategy suggested in this paper provides satisfactory braking performance, passenger comfort, and high regenerative efficiency.

Optimized Fuzzy Logic Control Strategy for Parallel Hybrid Electric Vehicle Based on Genetic Algorithm

For PHEV energy management, in this paper the author proposed an EMS is that based on the optimization of fuzzy logic control strategy. Because the membership functions of FLC and fuzzy rule base were obtained by the experience of experts or by designers through the experiment analysis, they could not make the FLC get the optimization results. Therefore, the author used genetic algorithm to optimize the membership functions of the FLC to further improve the vehicle performance. Finally, simulated and analyzed by using the electric vehicle software ADVISOR, the results indicated that the proposed strategy could easily control the engine and motor, ensured the balance between battery charge and discharge and as compared with electric assist control strategy, fuel consumption and exhaust emissions have also been reduced to less than 43.84%.

Optimal Slip Ratio Based Fuzzy Control of Acceleration Slip Regulation for Four-Wheel Independent Driving Electric Vehicles

To improve the driving performance and the stability of the electric vehicle, a novel acceleration slip regulation (ASR) algorithm based on fuzzy logic control strategy is proposed for four-wheel independent driving (4WID) electric vehicles. In the algorithm, angular acceleration and slip rate based fuzzy controller of acceleration slip regulation are designed to maintain the wheel slip within the optimal range by adjusting the motor torque dynamically. In order to evaluate the performance of the algorithm, the models of the main components related to the ASR of the four-wheel independent driving electric vehicle are built in MATLAB/SIMULINK. The simulations show that the driving stability and the safety of the electric vehicle are improved for fuzzy logic control compared with the conventional PID control.

Study of the Parallel Hybrid Electric Vehicle Drive Train Fuzzy Logic Control Strategy

Vehicle power train plays an important role to develop the energy efficient and reduce fuel consumption.In this paper,the drive train fuzzy controller structure and the fuzzy logic control strategies of parallel hybrid vehicles is presented. Simulation results illustrate the potential of the proposed controller and control strategy in terms of fuel economy and in keeping the deviations of SOC at a low level.

Implementation of a SAG Grinding Expert System at Kansanshi Mine - Zambia

This paper describes the method and results of the advanced control system that was implemented on the Sulphide milling circuit. The acceptance and performance achieved may be attributed to the approach in implementing the system, as well as the configuration of the fuzzy logic control strategy.

Fuzzy Logic Control Strategy for an Extended-Range Electric Vehicle

This paper presents a Fuzzy Logic Control Strategy (FLCS) for an Extended-range Electric Vehicle (E-REV) with series structure. The control strategy design objective of the E-REV is fuel economy. Based on the State of Charge (SOC) of the battery and the desired power for driving, the power required by the vehicle is split between the engine/generator set and the battery by the FLCS. The engine can be operated consistently in a very high efficiency area and the SOC of the battery can be maintained at a reasonable level. Some standard driving cycles and two control strategies of Power Follower Control Strategy (PFCS) and FLCS were simulated with AVL-Cruise and Matlab/Simulink to analyze the vehicle performance. Some simulation results are compared and discussed: the FLCS indicates better performance in terms of fuel consumption.

Fractional order PID Based Control Strategy for the Valve Plant of Hydraulic Transformer

Aiming at the pressure control of hydraulic transformer (HT), the fractional order PID (FOPID) controller is proposed for the control of the position of the valve plate. The assignment system of the HT is carefully modeled. To optimally determine the parameters of the FOPID controller, a self-adapting differential evolution algorithm is employed. Numerical simulation is conducted to investigate the proposed control strategy. Both step response and sinusoidal command signal tracking of the servo system is assessed, and the results demonstrate that the proposed control strategy is obviously superior to the conventional PID, Fuzzy Logic Controller (FLC) and fuzzy-PID control strategy, and can afford precise tracking performances. It is suitable for the precise adjustment of the output pressure of the HT.

A Study on Fuzzy Control of Humanoid Soccer Robot EFuRIO for Vision Control System and Walking Movement

Instability is one of the major defects in humanoid robots. Recently, various methods on the stability and reliability of humanoid robots have been studied actively. We propose a new fuzzy-logic control scheme for vision systems that would enable a robot to search for and to kick a ball towards an opponent goal. In this paper, a stabilization algorithm is proposed using the balance condition of the robot, which is measured using accelerometer sensors during standing and walking, and turning movement are estimated from these data. From this information the robot selects the appropriate motion pattern effectively. In order to generate the appropriate reaction in various body of robot situations, a fuzzy algorithm is applied in finding the appropriate angle of the joint from the vision system. The performance of the proposed algorithm is verified by searching for a ball, walking, turning tap and ball kicking movement experiments using an 18-DOF humanoid robot, called EFuRIO.

Application of an SMA‐based hybrid control device to 20‐story nonlinear benchmark building

SUMMARYThis paper investigates the seismic response control of a 20‐story nonlinear benchmark building with a new recentering variable friction device (RVFD). The RVFD combines energy dissipation capabilities of a variable friction damper (VFD) with the recentering ability of shape memory alloy (SMA) wires. The VFD that is the first subcomponent of the hybrid device consists of a friction generation unit and piezoelectric actuators. The clamping force of the VFD can be adjusted according to the current level of ground motion by adjusting the voltage level of piezoelectric actuators. SMA wires that exhibit a unique hysteretic behavior and full shape recovery after experiencing large strains is the second subcomponent of the hybrid device. Numerical simulations of a seismically excited 20‐story benchmark building are conducted to evaluate the performance of the hybrid device. A continuous hysteretic model is used to capture frictional behavior of the VFD and a neuro‐fuzzy model is employed to describe highly nonlinear behavior of the SMA components of the hybrid device. A fuzzy logic controller is developed to adjust the voltage level of VFDs for favorable performance in an RVFD hybrid application. Results show that the RVFD modulated with a fuzzy logic control strategy can effectively reduce interstory drifts and permanent deformations without increasing acceleration response of the benchmark building for most cases. Copyright © 2012 John Wiley & Sons, Ltd.

Modeling and Control of a Two-link Flexible Manipulator using Fuzzy Logic and Genetic Optimization Techniques

Flexible manipulator systems exhibit many advantages over their traditional (rigid) counterparts. However, they have not been favored in production industries due to its obvious disadvantages in controlling the manipulator. This paper presents theoretical investigation into the dynamic modeling and characterization of a constrained two-link flexible manipulator, by using finite element method. The final derived model of the system is simulated to investigate the behavior of the system. A Genetic Algorithm (GA) based fuzzy logic control strategy is also developed to reduce the end-point vibration of a flexible manipulator without sacrificing its speed of response. An uncoupled fuzzy logic controller approach is employed with individual controllers at the shoulder and the elbow link utilizing hub-angle error and hub-velocity feedback. GA has been used to extract and optimize the rule base of the fuzzy logic controller. The fitness function of GA optimization process is formed by taking weighted sum of multiple objectives to trade off between system overshoot and rise time. Moreover, scaling factors of the fuzzy controller are tuned with GA to improve the performance of the controller. A significant amount of vibration reduction has been achieved with satisfactory level of overshoot, rise time and settling time and steady state error.

Optimization Strategy of Distortion Rate Control for High Power Transfer Supplying an X-Ray Tube

This paper mainly concerns a contribution in the art development of high power generator supplying an X-ray tube for medical use. A generic method is proposed to reduce thermal stresses induced in X-ray tube during successive doses. For such application resonant DC-DC converter should be required, which generate output voltage and load current strictly identical to reference signal accompanied with normalized power factor quality. Primordially an adequate structure active filter connected in parallel at load lover is used to adjust the input and out put current distortion rate. With fuzzy logic control strategy, out put characteristics in transient and steady state are integrally mastery. This robust control type permits successive and repetitive taken measurement of radiology. K e y w o r d s: X-ray tube, DC-DC resonant converter, high frequency transformer, active filter, distortion rate, robust control