Application Of Fuzzy Logic Controller Research Articles

Enhancing the Supersonic Wind Tunnel Performance Based on Plenum Temperature Control

The application of fuzzy logic controllers (FLCs) to the control of nonlinear processes, typically controlled by a human operator, is a topic of much study. In this paper, the design and application of a FLC is discussed to control the plenum chamber temperature for a blowdown supersonic wind tunnel (BSWT) with the aim of achieving the accurate and desired results. In this regard, first, a nonlinear mathematical model of special BSWT is developed in Matlab/Simulink software environment. Next, an artificially intelligent controller is designed using fuzzy logic approach. For this purpose, a proportional-derivative FLC (PD-FLC) system is developed in the Simulink toolbox to control the plenum stagnation temperature using a heater upstream of the plenum chamber. Finally, the system simulation results inside of the temperature and pressure controllers in comparison with the experimental run are presented. The results for Mach 2.5 blowdown run show the great performance of the Wind Tunnel Simulator Model and its temperature controller system.

Real-time estimation and detection of non-linearity in bio-signals using wireless brain-computer interface

In this paper, the work is mainly concentrated on removing non-linear parameters to make the physiological signals more linear and reducing the complexity of the signals. This paper discusses three different types of techniques that can be successfully utilised to remove non-linear parameters in EEG and ECG. (i) Transformation technique using Discrete Walsh-Hadamard Transform (DWHT); (ii) application of fuzzy logic control and (iii) building the Adaptive Neuro-Fuzzy Inference System (ANFIS) model for fuzzy. This work has been inspired by the need to arrive at an efficient, simple, accurate and quicker method for analysis of bio-signal.

Robust fuzzy logic stabilization with disturbance elimination.

A robust fuzzy logic controller is proposed for stabilization and disturbance rejection in nonlinear control systems of a particular type. The dynamic feedback controller is designed as a combination of a control law that compensates for nonlinear terms in a control system and a dynamic fuzzy logic controller that addresses unknown model uncertainties and an unmeasured disturbance. Since it is challenging to derive a highly accurate mathematical model, the proposed controller requires only nominal functions of a control system. In this paper, a mathematical derivation is carried out to prove that the controller is able to achieve asymptotic stability by processing state measurements. Robustness here refers to the ability of the controller to asymptotically steer the state vector towards the origin in the presence of model uncertainties and a disturbance input. Simulation results of the robust fuzzy logic controller application in a magnetic levitation system demonstrate the feasibility of the control design.

Semi-active Control of Building Structures using Variable Stiffness Device and Fuzzy Logic

Semi-active control devices, also called “Intelligent” control devices, constitute the positive aspects of both the passive and active control devices. A semi-active control strategy is similar to the active control strategy, but this control device has been shown to be more energy-efficient than active devices. A particular type of semi-active control device, the Variable Stiffness Device (VSD), consists of a hydraulic cylinder with a normally closed solenoid control valve inserted in the tube connecting the two cylinder chambers. This paper emphasizes on the application of Semi-active Fuzzy Logic Controller (SFLC) of this system for getting the best results in the reduction of the building responses under earthquake excitations. For the numerical example, a 12-story building, located in the city of Rasht, Iran, is modeled as 3-D frame and the problem is solved in state space. The results obtained from the proposed control scheme (SFLC) are compared with those obtained from the ON-OFF control method. It is found that the SFLC is highly effective in reducing the responses of the example building than the ON-OFF algorithm. In this study, the optimal values of the fuzzy rule bases, membership functions, and the location of the control device are determined.

Type-2 Fuzzy Logic Controllers Based Genetic Algorithm for the Position Control of DC Motor

Type-2 fuzzy logic systems have recently been utilized in many control processes due to their ability to model uncertainty. This research article proposes the position control of (DC) motor. The proposed algorithm of this article lies in the application of a genetic algorithm interval type-2 fuzzy logic controller (GAIT2FLC) in the design of fuzzy controller for the position control of DC Motor. The entire system has been modeled using MATLAB R11a. The performance of the proposed GAIT2FLC is compared with that of its corresponding conventional genetic algorithm type-1 FLC in terms of several performance measures such as rise time, peak overshoot, settling time, integral absolute error (IAE) and integral of time multiplied absolute error (ITAE) and in each case, the proposed scheme shows improved performance over its conventional counterpart. Extensive simulation studies are conducted to compare the response of the given system with the conventional genetic algorithm type-1 fuzzy controller to the response given with the proposed GAIT2FLC scheme.

Fuzzy logic control implementation considerations and complexity analyses

In this paper, a number of implementation and algorithmic options for fuzzy logic control applications are presented. The emphases are on the analyses of the computational load and memory requirements of all the processing stages of fuzzy logic control algorithms. Comparisons are made among commonly used techniques and recommendations are provided. The results could be used to guide the design of fuzzy logic controllers for an embedded or hardware implementation.

Design of a Tele-Control Electrical Vehicle System Using a Fuzzy Logic Control

This paper presents a fuzzy logic design of a tele-control electrical vehicle system. We showed that the application of fuzzy logic control allows the stability of tele-vehicle system in spite of communication delays between the operator and the vehicle. A robust bilateral controller design using fuzzy logic frameworks was proposed. This approach allows a convenient means to trade off robustness and stability for a pre-specified time- delay margin. Both the performance and robustness of the proposed method were demonstrated by simulation results for a constant time delay between the operator and the electrical vehicle system.

Comparative Analysis of Conventional Pid Controller and Fuzzy Controller with various Defuzzification Methods in a Three Tank Level Control System

All the real systems exhibits non-linear nature, conventional controllers are not always able to provide good and accurate results. Fuzzy Logic Control is used to obtain better response. A model for simulation is designed and all the assumptions are made before the development of the model. An attempt has been made to analyze the efficiency of a fuzzy controller over a conventional PID controller for a three tank level control system using fuzzification & defuzzification methods and their responses are compared. Analysis is done through computer simulation using Matlab/Simulink toolbox. This study shows that the application of Fuzzy Logic Controller (FLC) gives the best response with triangular membership function and centroid defuzzification method.

Load frequency control using multi-stage fuzzy logic controller for wind-micro hydro-diesel hybrid power system

This paper presents an analysis of multi-stage fuzzy logic control (FLC) application for load frequency control (LFC) of an isolated wind-micro hydro-diesel hybrid power system. Due to the sudden load changes and intermittent wind power, large frequency fluctuation problems can occur. The LFC deviates the frequency deviation and maintains dynamic performance of the system. In this paper, a multi-stage fuzzy logic proportional integral derivative (PID) controller is proposed for LFC of an isolated wind-micro hydro-diesel hybrid power system. Simulations are performed for this hybrid system with the proposed multi-stage fuzzy logic PID controller, conventional PI controller and FLC with different load disturbances and wind input disturbances. The performance of the proposed approach is verified from simulations and comparisons. Simulation results explicitly show that the performance of the proposed multi-stage fuzzy logic PID controller is superior to the conventional PI controller and FLC in terms of overshoot, settling time and steady state error against various load changes and variations of wind inputs.

Hybrid Neural-Fuzzy Controller for Motorized Robot Arm

In this study, a design methodology is introduced that blends the neural and fuzzy logic controllers in an intelligent way developing a new intelligent hybrid controller. In this design methodology, the fuzzy logic controller works in parallel with the neural controller and adjusting the output of the neural controller. The performance of our proposed controller is demonstrated on a motorized robot arm with disturbances. The simulation results shows that the new hybrid neural -fuzzy controller provides better system response in terms of transient and steady-state performance when compared to neural or fuzzy logic controller applications. The development and implementation of the proposed controller is done using the MATLAB/Simulink toolbox to illustrate the efficiency of the proposed method.

On the Application of Fuzzy Logic Control in Pneumatic Conveying Systems

On the Application of Fuzzy Logic Control in Pneumatic Conveying Systems

Towards next generation electrochemical machining controllers: A fuzzy logic control approach to ECM

Electrochemical machining (ECM) is a manufacturing process that offers a number of advantages (e.g. no mechanical stress) over its nearest competitors as certain trends in production move towards the micro scale. Maintaining optimum ECM process conditions ensures higher machining efficiency and performance. This paper presents the development of a fuzzy logic controller to add intelligence to the ECM process. An experimental ECM drilling rig, at University of Manchester, was improved through the integration of a fuzzy logic controller into the existing control system. Matlab (Fuzzy Logic Toolbox) was used to build a fuzzy logic controller system, which controls the feed rate of the tool and the flow rate of the electrolyte. The objective of the fuzzy logic controller was to improve machining performance and accuracy by controlling the ECM process variables. The results serve to introduce innovative possibilities and provide potential for future applications of fuzzy logic control (FLC) in ECM. Hybrid controllers that integrate fuzzy logic into the control system allow for “human like” decision-making intelligence to be incorporated into ECM controllers. The focus of this paper is the feasibility of FLC in ECM, but the results have the potential of being applied to EMM. As the future of ECM moves towards electrochemical micromachining (EMM), the need for process uncertainty control in this area may be met by FLC, which has advantages over conventional methods of process control.

To Study and Survey About Load Frequency Control for Stability Improvement of Power

The modern power systems with industrial and commercial loads need to operate at constant frequency with reliable power. The load frequency control of an interconnected power system is being improved over the last few years. The goals of the LFC are to maintain stability improvement of power system. Fuzzy logic controller application of load frequency control (LFC) of a single area power system by using a fuzzy logic is presented. The study has been designed for a single area interconnected power system. The comparison between a conventional Proportional and Integral (PI) controller and the proposed artificial neural networks controller is showed that the proposed controller can generate the best dynamic response for a step load change. For this application, MATLAB-Simulink software is used.

Application of fuzzy logic controller and pseudo‐attitude to the autonomous flight of an unmanned aerial vehicle

This paper presents the application of the proposed autopilot and navigation system to an unmanned aerial vehicle (UAV). The autopilot is based on fuzzy control and the navigation system is composed of a single antenna global positioning system (GPS) and gyroscopes, which implies that no other inertial navigation system (INS) is needed for this system. The autopilot consists of four fuzzy logic controllers, which control the altitude, pitch, heading, and roll respectively, and it is capable of performing waypoint navigation and trajectory following. The navigation system supplies the position and the estimated attitude of the aircraft. The estimated attitude is fused from the angular rates measured by the gyroscopes and the pseudo‐attitude derived from the GPS velocity solution, and this fusion is via the quaternion‐based extended Kalman filter (EKF). Ultimately, the feasibility and performance of the resulting autopilot and navigation system are verified through flight simulation of the SP‐80 UAV. The simulation results demonstrate the successful autonomous flight of this UAV, using the autopilot and navigation system, even under the influence of wind disturbance.

Improvement the DTC system for electric vehicles induction motors

A three-phase squirrel-cage induction motor is used as a propulsion system of an electric vehicle (EV). Two different control methods have been designed. The first is based on the conventional DTC Scheme adapted for three level inverter. The second is based on the application of fuzzy logic controller to the DTC scheme. The motor is controlled at different operating conditions using a FLC based DTC technique. In the simulation the novel proposed technique reduces the torque and current ripples. The EV dynamics are taken into account.

Energy efficient fuzzy based combined variable refrigerant volume and variable air volume air conditioning system for buildings

Energy conservative building design has triggered greater interests in developing flexible and sophisticated air conditioning systems capable of achieving enhanced energy-savings potential without sacrificing the desired thermal comfort and indoor air quality (IAQ). This research work greatly aimed at achieving enhanced energy conservation, good thermal comfort and better IAQ for space conditioning with the application of combined variable refrigerant volume (VRV) and variable air volume (VAV) air conditioning (A/C) systems. Experimental investigation on the proposed combined air conditioning system with the application of intelligent fuzzy logic controller was performed for summer and winter climatic conditions to substantiate the energy-savings capability. The proposed system experimentally analyzed under fixed ventilation, demand controlled ventilation (DCV) and combined DCV and economizer cycle (EC) ventilation techniques effectively conserved 44% and 63% of per day average energy-savings in summer and winter design conditions respectively, while compared to the conventional constant air volume (CAV) A/C system. The results of the present investigation have proved that the proposed combined air conditioning system operated under the different ventilation strategies and controlled by the intelligent fuzzy logic controller (FLC) can be considered as an efficient technology to achieve good thermal comfort, IAQ and energy conservation in the modern heating, ventilation and air conditioning (HVAC) applications.

Rancang Bangun Tacho Generator Sebagai Sensor Kecepatan Pada Praktikum Kendali Motor-Motor Listrik

The research implementing “Tachogenerator as the sensor for controller of Electric Motor Speed rotation at electric laboratories” Plant (Object controlled) is controlled by the electric motors speed rotation in AC or DC, by setting armature voltage of motor using digital PID controller and fuzzy logic from Personal Computer (PC). Although the load disturbances are changing significantly, but the motor speed rotation can be stabilized by feedback input from tachogenerator which are the focus in this research. PID controller dan fuzzy Logic controller applications are used to find the advantages the disadvatages or controll methode of motor reliability in dinamic controll (speed rotation) which cause the system more stabil. The result can be seen in maintained system responses and the system reliabilty against disturbances.

Better Quality Control: Stochastic Approaches to Optimize Properties and Performance of Plasma-Sprayed Coatings

Statistical design of experiment (SDE) methodology applied to design and performance testing of plasma-sprayed coatings follows an evolutionary path, usually starting with classic multiparameter screening designs (Plackett-Burman), and progressing through factorial (Taguchi) to limited response surface designs (Box-Behnken). Modern designs of higher dimensionality, such as central composite and D-optimal designs, will provide results with higher predictive power. Complex theoretical models relying on evolutionary algorithms, and application of artificial neuronal networks (ANNs) and fuzzy logic control (FLC) allow estimating the behavior of the complex plasma spray environment through validation either by key experiments or first-principle calculations. In this review, paper general principles of SDE will be discussed and examples be given that underscore the different powers of prediction of individual statistical designs. Basic rules of ANN and FLC will be briefly touched on, and their potential for increased reliability of coating performance through stringent quality control measures assessed. Salient features will be reviewed of studies performed to optimize thermal coating properties and processes reported in the pertinent literature between 2000 and the present.

Performance and behaviour of the microbial community of an anaerobic biogas digester using sugar beet silage as mono-substrate

The performance and the behaviour of the microbial community of an anaerobic mesophilic biogas digester fed with the sugar beet silage (SBS) were investigated in this laboratory-scale work. The biogas digester was operated using a fuzzy logic control (FLC) technique, which was developed at the Hamburg University of Applied Sciences. The SBS, which had extremely low pH of 3.3–3.4, was used as mono-input, without addition of manure. The main objective of this study was to achieve and maintain a stable and safe anaerobic conversion process, along with satisfactory process efficiency. With use of FLC, the biogas digester could be operated with hydraulic retention times (HRTs) in the range 8–25 days, and up to an organic loading rate (OLR) of 7.41 g volatile solids [VS] l−1 d−1. The average levels of the specific gas production (GP) rate (SGPR) and the volumetric GP rate (VGPR) were 0.55 l g [VS]−1 d−1 and 3 l l−1 d−1, respectively, with 66% of methane (CH4) content in digester biogas. Despite the SBS having an extremely low pH, low amounts of nutrients and buffering capacity, instability problems were not encountered during the experimental period of 156 days, as indicated by the neutral range of the digester pH and the high OLR applied. A daily addition of 1 M KHCO3 to the digester also provided adequate buffering capacity. The application of the FLC also exhibited a positive effect on the methanogenic population. After starting the FLC, the methanogenic population doubled from 4.80 × 108 to 1.19 × 109 cells ml−1 at the end of the investigation period, whereas the number of the total bacteria remained constant around 2.27–2.30 × 1010 cells ml−1. The FLC application appeared to have a positive effect on the number of the fluorescent methanogens. However, no significant effect on the morphology was found.

ECOLOGICAL SYSTEMS CONTROL BY FUZZY LOGIC CONTROLLER

The application of fuzzy logic control in the long-term management of an ecological system is proposed. The ecological systems are subject to unpredictable but bounded disturbances due to changes in climatic conditions, disease, migrating species, etc. By applying the fuzzy logic controller, the biomasses of exploited and continually disturbed ecological systems will converge in a comparatively short time and keep the biomasses within a small neighborhood of the unique nontrivial optimal equilibrium state of the undisturbed exploited ecosystem. Demonstration through examples reveals that the accumulated yield with fuzzy logic control is better than that with state feedback control or no control.