Top PDF Type-2 fuzzy logic control in computer games

Type-2 fuzzy logic control in computer games

Type-2 fuzzy logic control in computer games

Summary. In this chapter, we will present the novel applications of the Interval Type-2 (IT2) Fuzzy Logic Controllers (FLCs) into the research area of computer games. In this context, we will handle two popular computer games called Flappy Bird and Lunar Lander. From a control engineering point of view, the game Flappy Bird can be seen as a classical obstacle avoidance while Lunar Lander as a position control problem. Both games inherent high level of uncertainties and randomness which are the main challenges of the game for a player. Thus, these two games can be seen as challenging testbeds for benchmarking IT2-FLCs as they provide dynamic and competitive elements that are similar to real- world control engineering problems. As the game player can be considered as the main controller in a feedback loop, we will construct an intelligent control systems composed of three main subsystems: reference generator, the main controller, and game dynamics. In this chapter, we will design and then employ an IT2-FLC as the main controller in a feedback loop such that to have a satisfactory game performance while be able to handle the various uncertainties of the games. In this context, we will briefly present the general structure and the design methods of two IT2-FLCs which are the Single Input and the Double Input IT2-FLCs. We will show that an IT2 fuzzy control structure is capable to handle the uncertainties caused by the nature of the games by presenting both simulations and real-time game results in comparison with its Type-1 and conventional counterparts. We believe that the presented design methodology and results will provide a bridge for a wider deployment of Type-2 fuzzy logic in the area of the computer games.
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Comparative Study of a Fuzzy Logic Based Controller and a Neuro-Fuzzy logic Based Controller for Computer Fan

Comparative Study of a Fuzzy Logic Based Controller and a Neuro-Fuzzy logic Based Controller for Computer Fan

Incessant packing, malfunction, and irritating noise generated by computer due to load impact and temperature conditions has made the control of computer fan an important issue to improve and resolve [3]. Computer fan comes in various types, such as; (i) two-wire type, (ii) three-wire type, and (iii) four-wire type. The two-wire type is the oldest, giving full speed rotation once the computer system is switched on. The three-wire type is very common and has “tacho” used mainly to sense fan speed [1]. The most modern computer fan comes with four-wire with a pulse width modulation (PWM) signal in terms of revolution per minute. A four-wire computer fan, in addition to speed control, with the use of “tacho”, senses the speed simultaneously making it ideal for a feedback system unlike the three-wire system. The drawback with PWM fans is that, if the duty cycle is below a threshold value, the fan either stops operation or run at a constant/stable low speed [2, 4]. Flexibility in terms of operational conditions gives room for improvement.
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Type 2 Fuzzy Logic Controllers Based Genetic Algorithm for the Position Control of DC Motor

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

Genetic operators such as crossover and mutation are ap- plied to the parents in order to produce a new generation of candidate solutions. As a result of this evolutionary cycle of selection, crossover and mutation, more and more suitable solutions to the optimization problem emerge with- in the population. Increasingly, GA is used to facilitate FLSs design [9]. However, most of the works discuss type-1 FLC design. This paper focuses on genetic algo- rithm of type-2 FLCs. There are two very different ap- proaches for selecting the parameters of a type-2 FLS [4]. Type-2 FLCs designed via the partially dependent ap- proach are able to outperform the corresponding type-1 FLCs [9], The type-2 FLC has a larger number of de- grees of freedom because the fuzzy set is more complex. The additional mathematical dimension provided by the type-2 fuzzy set enables a type-2 FLS to produce more complex input-output map without the need to increase the resolution. To address this issue, a comparative study involving type-2 and type-1 FLCs with similar number of degrees of freedom is performed. The totally independent approach is adopted so that the type-2 FLC evolved using GA has maximum design flexibility.
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Development of Interval Type-2 Fuzzy Logic Controller for Polymer Extruder Melt Temperature Control

Development of Interval Type-2 Fuzzy Logic Controller for Polymer Extruder Melt Temperature Control

A main objective of controlling melt temperature is to develop a thermal control framework based on temperature profile measurements, which manipulates screw speed and individual set temperatures together to reduce undesirable melt temperature variations while maintain the required average temperature levels. Obviously, this type of controller will have to handle the complex nonlinear behaviors of the process. This study uses a model-based control approach and hence the performance of the controller depends on the accuracy of models. In this type of work, use of a control technique like interval type-2 fuzzy logic (IT2FLC) may be advantageous as it does not require fully accurate models. Another major advantage is that interval type2 fuzzy logic (IT2FLC) controller can handle process nonlinearities with a set of linguistic IF-THEN rules which do not require exact numerical boundaries. Due to these and its other advantages, interval type2 fuzzy logic (IT2FLC) was selected as the control technique for this study.
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A Pd-Type Fuzzy Logic Control Approach For Vibration Control Of A Single-Link Flexible Manipulator

A Pd-Type Fuzzy Logic Control Approach For Vibration Control Of A Single-Link Flexible Manipulator

Many control techniques have been reported in literature for the vibration and deflection control in these systems, literature survey shows that most of the control technique developed for the control of flexible structures has lots of accuracy and precision limitations. Two different approaches have been applied in literature for the control of the flexible robot arms these are; i) linear control approach and ii) nonlinear control approach. Linear controllers such as H-infinity [1], linear quadratic regulator (LQG)[2], conventional PID control[3] and integral resonant control (IRC)[4,5], have been applied in the control of FMS. Flexible manipulator is quite difficult to be accurately controlled by linear control approach due to their nonlinear dynamic structure of the system. Nonlinear control approach such as: Adaptive control technique [6], fuzzy logic control technique [7] and observer-based fuzzy- control [8] have also been applied in the control of FMS.
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Type-2 Fuzzy Logic Approach To Increase The Accuracy Of Software Development Effort Estimation

Type-2 Fuzzy Logic Approach To Increase The Accuracy Of Software Development Effort Estimation

Developing effort prediction models of software projects has led to purposeful studies in the past in order to estimate software development effort [2][4][5]. Generally, development effort estimation, as one of the three major challenges in computer sciences, is a major activity in software project planning [4][6]. Software development effort estimation can be considered as a subset of software estimates [7][2]. In the existing models of effort estimation, reducing relative error is considered as the most important goal, and it is attempted to reduce the amount of error as much as possible. Due to the uncertainty and sophisticated non-linear properties of software projects, high and reliable accuracy cannot be achieved by mere focusing on estimation model criterion. In addition, models based on single- objective optimization are not able to manage software projects, and the results of this type of estimators are greatly different from one database to another. As a result, due to the high level of complexity, it is impossible to generalize the accuracy of the existing effort estimators for various software projects [8][9]. Without a proper estimate of the required cost, the project manager cannot determine how much time, work force, and many other sources s/he needs in order to undertake the project. In case of misdiagnosis, the project will be doomed to certain failure. Surveys conducted indicate that most software
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IS-MRAS With On-Line Adaptation Parameters Based on Type-2 Fuzzy LOGIC for Sensorless Control of IM

IS-MRAS With On-Line Adaptation Parameters Based on Type-2 Fuzzy LOGIC for Sensorless Control of IM

F. Zidani received her B.Sc., M.Sc., and Ph.D. all in Electrical Engineering, from the University of Batna, Algeria in 1993, 1996, and 2003, respectively. After graduation, she joined the University of Batna, Algeria, where she is a Full Professor in the Electrical Engineering Department. She is the Head of Team: Control and Diagnosis of Electrical Drives, Laboratory of Electromagnetic Induction and Propulsion Systems. Her current area of research includes advanced control techniques; diagnosis of electric machines and drive, robust control. She is reviewer of many IEEE proceedings and IEEE journals.
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A Study on Performance of Fuzzy Logic Type 2 PSS

A Study on Performance of Fuzzy Logic Type 2 PSS

A FLC is a kind of a state variable controller governed by a family of rule and a fuzzy type 2 inference mechanism. The FLC algorithm can be implementation-using heuristic strategies, defined by linguistically describe statements. The fuzzy type 2 logic control algorithm reflects the mechanism of control implemented by people, without using a mathematical model the controlled object, and without an analytical description of the control algorithm. The main FLC processes are fuzzifier, knowledge base, the inference engine and defuzzifier as in Fig. 2.
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A Study on Performance of Fuzzy Logic Type 2 PSS and Fuzzy type 2 Model Reference Learning PSS

A Study on Performance of Fuzzy Logic Type 2 PSS and Fuzzy type 2 Model Reference Learning PSS

20 which leads to more power consumption and an increase in the complexity of the designs (Tarek and Kaamran, 2010). For overcoming the disadvantages of the conventional topologies, the single stage divide- by- 2/3 is presented. The TSPC divide- by- 2/3 cell which consists of only 10 transistors is shown in Fig.1. This architecture is designed for the VCO input source in the range of WLAN frequencies. The control signals which activate transistors Q 1 and Q 2 can change the division ratio from 2 to 3, respectively.
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Application of Hyper-Fuzzy Logic Type -2 in Field Oriented Control of Induction Motor with Broken Bars

Application of Hyper-Fuzzy Logic Type -2 in Field Oriented Control of Induction Motor with Broken Bars

International organization of Scientific Research 5 | P a g e nonlinear control and the IM motor mathematical model is also non-linear and complex. The FLCT2 controller performed better performance with respect to rise time and steady state error, we perform a simulation of a first broken rotor bar at t = 2s increasing resistance 11fois the resistance of the bar, the second adjacent broken rotor bar at t = 3 s. In bars break during we note that the speed remains constant insensitive broken the bars, demonstrating the robustness of the order by fuzzy logic type-2. There is a small fracture strain of the bar. In fact, it is on (Figure 4b) which cancels the loads instructions FLCT2 effects perturbations applied at time t = 0.8s, so also we see in this figure that the electromagnetic torque following these instructions without causing overflows considered moments and with less vibration. We also note the increase in the amplitude modulation of the stator current during the second broken rotor bar. The control input (u) has chattering in IFOC, but is free of chattering in FLCT-2. The field orientation is obvious, as the d-axis stator current and rotor flux remain constant. The speed response is shown in Figure.5.a. It can be seen clearly that the FLCT-2 provides a minimum response time and robust speed response compared to the traditional PI controller.
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Performance Improvement in Fault Diagnosis using Fuzzy Logic Type-2 Classifier

Performance Improvement in Fault Diagnosis using Fuzzy Logic Type-2 Classifier

A lot of research work on type-2 fuzzy sets has been carried out since the latter part of the 1990’s by Prof. Jerry Mendel and his students on type-2 fuzzy sets and systems. Since then, more and more researchers around the world are writing articles about type-2 fuzzy sets and systems. It has been extensively used in the past few years in fuzzy logic control, fuzzy logic signal processing, rule-based classification, etc. [1-2]

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Application of Type-1 and Type-2 Fuzzy Logic Controller for The Real  Swarm Robot

Application of Type-1 and Type-2 Fuzzy Logic Controller for The Real Swarm Robot

The use of the fuzzy logic method in the navigation task has been analyzed by a lot of previous studies. In overcoming the obstacle avoidance and stabilization of the position of mobile robot wheel problem Faisal et al [32] has designed sensor-based fuzzy sensor wireless for mobile robot navigation tasks between static and moving barriers. While in the paper [33], it can be seen that there were design and implemen- tation of the fuzzy hybrid architecture for intelligent navigation systems and mobile robot control in avoiding obstacles in static and dynamic environments. Just as in the case of robot football, fuzzy logic is very important, applied to individual robot be- haviors and actions, especially for obstacle avoidance and achieving targets [34][35]. In research [8], Algabri et al. have designed two fuzzy logic behaviors for mobile robot navigation i.e., behavior to achieve targets and avoid obstacles with different scenarios. However, it is important to pay attention to the development of this archi- tecture, that is for the same path-planning problem.
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Direct Field-Oriented Control Using Fuzzy Logic Type -2 for Induction Motor with Broken Rotor Bars

Direct Field-Oriented Control Using Fuzzy Logic Type -2 for Induction Motor with Broken Rotor Bars

In the direct rotor field-oriented vector-controlled induction motor drives (DRFOC) the fault symptoms can be observed as characteristic frequencies of stator current components, rotor flux magnitude, control voltages and decoupling signals. So the monitoring of these signals can be useful from the diagnostic point of view. In this paper an analysis of a DRFOC induction motor drive with a faulty rotor is presented with respect to direct rotor speed measurement as well as a speed sensorless operation. The rotor flux and speed are reconstructed by an estimator the complete scheme of direct vector control rotor flux oriented is the following using fuzzy logic type-2 [7, 11]:
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Adaptive Fuzzy Type-2 in Control of 2-DOF Helicopter

Adaptive Fuzzy Type-2 in Control of 2-DOF Helicopter

Although the difficulties in both understanding and design of the type-2 logic systems comparing to other controllers, the first stays still as a preferred research area in the recent years, due to its robustness through the uncertainties and disturbances. In this sense, PID controller, which is highly sensitive to perturbations and uncertainties, has a drawback and it may cause a performance degradation. In the meanwhile, applied on the same class of systems as described previously, the PID and fuzzy control have higher tracking errors, especially when disturbances arise. In this study, the proposed designed controllers successfully designed several controllers for trajectory tracking control of TRMS model on MATLAB/Simulink, among these designed controllers, an interval type-2 fuzzy logic system is presented. According to the results, type-2 fuzzy logic controller produce better results than the PID and fuzzy type-1 controller in terms of tracking precision in the presence of the disturbances.
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An Interval Type-2 Fuzzy Logic Approach for Instrument Fault Detection and Diagnosis

An Interval Type-2 Fuzzy Logic Approach for Instrument Fault Detection and Diagnosis

I N the last three decades process control and automation area had a tremendous improvement due to advances on computational tools. Many of regulatory control actions that were performed by human operators are now performed automatically with aid of computers. Nonetheless, in a pro- cess with hundreds of variables, instruments and actuators it is impossible that a person or a group can manage every and any alarm triggered by an abnormal event. Therefore the Fault Detection and Diagnosis (FDD) field had received extensive attention. According to [1], the current challenge for control engineers is the automation of Abnormal Event Management (AEM) using intelligent control systems. Inside this field, Instrument Fault Detection and Diagnosis is a potential tool to prevent process performance degradation, false alarms, missing actions, process shutdown and even safety problems. A well-known strategy related to this pro- blem is preventive maintenance. In that, periodical tests and calibration are made in instruments. This is a cumbersome task where instruments are dismantled, cleaned, reassembled and calibrated. Even so, this is not a guarantee that faults will not occur [2]. This paper presents an Interval Type- 2 Fuzzy Logic (IT2FL) classifier to detect and diagnose temperature sensor faults in an alternative compressor, named Sales Gas Compressor (SGC), operating in a Gas Processing Unit (GPU).
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Interval Type-2 Fuzzy Logic And Ga Techniques: A Review

Interval Type-2 Fuzzy Logic And Ga Techniques: A Review

where I is the total number of start position, K is the number of step simulation for each start position, ω(k), and v(k) are the rotational speed and the faulty speed at k, respectively, and c is constant for health check of IM, 0 if there is no fault and 1 if there is fault. This function is minimized in order to achieve the condition than the motor run by avoiding fault, higher speed, and mostly reliable speed. After that, three operators of GA are carried out, namely recombination, crossover and mutation, with fixed crossover probability rate (Pc) and probability mutation rate (Pm), that are 0.7, and 0.7/parameter numbers, respectively. The number of new generation is adjusted by Generation Gap constant (GGAP), which is 0.9. The procedure is repeated until the termination condition is reached. It has been presented Interval Type2Fuzzy Logic Controller (IT2FLC) where the fuzzy knowledge based, i.e. membership functions and rule bases, are tuned by Genetic Algorithm (GAs), known as Genetic Fuzzy System (GIT2FS), to generate individual command action. The model is designed in order to detect faults in IM. The best fitness knowledge base is obtained by learning the RB in advance and then tuning the MF after. Besides that, the motor has improved its performance, for instance it can generate motor control for individual fault.
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Interval Type-2 Fuzzy Logic Controller for DC-DC Converter

Interval Type-2 Fuzzy Logic Controller for DC-DC Converter

ABSTRACT: In this paper an Interval Type2 Fuzzy Logic (IT2FL) controller is proposed for the control of DC-DC converters to attain a good output voltage regulation and dynamic response. The buck and boost type DC-DC converters are considered for the implementation of the IT2FL controller. To study the effects on the system performance, the conventional PI and type-1 fuzzy controller are designed and compared with interval type-2 fuzzy controller. Design of PI control is based on the frequency response of the DC-DC converter. Design of IT2FL controller is based on heuristic knowledge of converter behavior and tuning requires some expertise to minimize unproductive trail and errors. The setting time, the overshoot and the steady state error of the converter are used as the performance criteria for the evaluation of the controller performance. From the comparison, it is inferred that IT2FL controller will give better result than other controllers.
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Structural Damage Control with Interval Type-2 Fuzzy Logic Controller

Structural Damage Control with Interval Type-2 Fuzzy Logic Controller

Incremental Dynamic Analysis (IDA) is a parametric analysis method that is used to evaluate the performance of structures under the earthquake loads and has attracted the attention of the researchers. As shown in Figure 3, in this method, a structure is under the influence of various earthquakes of varying intensity, and the results of the analysis are presented as a curve. The curves are the response of each structure to earthquakes with different seismicity. In these graphs, indicators such as displacement, velocity, storey drift, acceleration of the structure, etc. can be considered as a response of the structure. By studying the obtained diagrams, a comprehensive assessment of the structure’s behavior can be made, under the influence of far field and near field earthquakes with different intensities. Thus, knowing the behavior of the structure, it is possible to think about some ways in order to control its behavior. The specific information of IDA curves can justify using this method, despite its time- consuming process and its difficulty. Bertero [29], for the first time in his research, referred to the concept of incremental analysis. Then, many researchers have used this method in their research; some of these researchers are Luco, Nassar, Psycharis, Mehanny, Matteis et al. [30-34]. Vamvatsikos has also carried out extensive research on the IDA method, and has been evaluating the capacity and reliability of structures under various earthquakes. Their research is a complete reference to the method of production, summarization and interpretation of IDA graphs [35]. The FEMA 2000 report also uses this method as a method for determining the final failure capacity of the structure [36].
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Type-2 Fuzzy Logic Control of a Doubly-Fed Induction Machine (DFIM)

Type-2 Fuzzy Logic Control of a Doubly-Fed Induction Machine (DFIM)

In the area of control of electrical machinery, research is increasingly oriented towards the application of modern control techniques. These techniques involved dizzily with the evolution of computers and power electronics. This enables leading manufacturers to high performance processes. These techniques are linear control [1], sliding mode control [1] [4], feedback linearization, adaptive control, and fuzzy control [9], this last Fuzzy Logic Controller (FLC) usually give better results for non-linear systems with variable parameters, the DFIM is an ideal candidate for testing the performance of fuzzy logic controllers [10]. The present work concerns "The IT2FLC of the speed in a vector-control of DFIM.
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Photovoltaic Power Control Using Fuzzy Logic and Fuzzy Logic Type 2 MPPT Algorithms and Buck Converter

Photovoltaic Power Control Using Fuzzy Logic and Fuzzy Logic Type 2 MPPT Algorithms and Buck Converter

To test the performance of the three algorithms in different climatic conditions, non-uniform irradiation is applied to the input of the PV array. The irradiation takes 880W/m 2 at the beginning of the simulations. After t=3.2 s, it rapidly decreases to 310W/m 2 and returns to the initial state. Fig. 17, Fig. 18, and Fig. 19 show the voltage, current, and power generated from the PV array with the three algorithms (FLT2, FL, InC) in the case of variable irradiation. After the disturbance, the FLT2 controller shows a better performance in term of response time, stability and generated power.
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