A Two-Wheeled Vehicle Navigation System Based on a Fuzzy Logic Controller

The paper deals with a two-wheeled vehicle,namely ESG-2 (Extended Segway-like Generation- 2) navigation control system using a fuzzy logic controller. The vehicle employs two wheels left and right independently which are controlled independently using a fuzzy logic controller respectively. The controllers deal with a compact and implementable application for the normal using with a person (human with 60kg weight in average) loaded on the vehicle. A modified infrared-based range sensor system is applied to the vehicle as a tilt sensor and it is incorporated with an accelerometer to control its response in case of the dynamics disturbances. The fuzzy controller runs in tilt-mode while a reference tilt using a potentiometer (as steer system) is taken into account for navigating the vehicle. From the simulation using MATLAB @ and experiments it is obvious that the prototype of ESG-2 is quite challenging to be developed in the future

The Advantages of Fuzzy Control for Heat Pumps Systems

Application of fuzzy control has been observed in various engineering fields due to its ability to handle uncertainties and non-linearities. In this study, the advantages of using fuzzy control in heat pump systems are being investigated. Specifically, the performance of a heat pump system with a conventional proportional-integral-derivative (PID) controller is being compared to that of a heat pump system with a fuzzy logic controller. It has been demonstrated by the results that the fuzzy control-based heat pump system offers better performance in terms of energy efficiency, temperature control, and overall system stability. This study contributes to the understanding of the potential benefits of fuzzy control-based heat pump systems and provides a foundation for further research in this area

Adaptive fuzzy system for 3-D vision

An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller

Mathematical Modeling and Fuzzy Adaptive PID Control of Erection Mechanism

This paper describes an application of fuzzy adaptive PID controller to erection mechanism. Mathematical model of erection mechanism was derived. Erection mechanism is driven by electro-hydraulic actuator system which is difficult to control due to its nonlinearity and complexity. Therefore fuzzy adaptive PID controller was applied to control the system. Simulation was performed in Simulink software and experiment was accomplished on laboratory equipment. Simulation and experiment results of erection angle controlled by fuzzy logic, PID and fuzzy adaptive PID controllers were respectively obtained. The results show that fuzzy adaptive PID controller can effectively achieve the best performance for erection mechanism in comparison with fuzzy logic and PID controllers

The field oriented control of a permanent magnet synchrounous motor (PMSM) by using fuzzy logic

This project presents the comprehensive performance analysis on the principle of operation, design considerations and control algorithms of the field oriented control (FOC) for a permanent magnet synchronous motor (PMSM) drive system of Fuzzy Logic Controller (FLC) and proportional-integral PI for speed control in closed loop operation. To perform speed control of typical PMSM drives, PI controllers and FOC method are classically used. PI Controller controller suffers from the drawback that for its proper performance, the limits of the controller gains and the rate at which they would change have to be appropriately chosen. Fuzzy based gain scheduling of PI controller has been proposed in which uses in order to overcome the PI speed controller problem. The simulation results show that the proposed FLC speed controller produce significant improvement control performance compare to the PI controller. FLC speed controller produced a better performance than PI speed controller where the overshoot is totally removed and the settling time faster than PI speed controller in achieving desired output speed. The fuzzy algorithm is based on human intuition and experience and can be regarded as a set of heuristic decision rules. It is possible to obtain very good performance in the presence of varying load conditions changes of mechanical parameters and inaccuracy in the process modelling. Research and application of fuzzy logic are developing very rapidly, with promising impacts on electric drives and power electronics in future. Keywords: FOC, PMSM, FLC, PI and for Speed Control

Pengembangan Sistem Mikro Kontroler sebagai Fuzzy Logic Controller

Generally microcontroller, and also microprocessor, work on the basis of boolean logic, which only knows true or false conditions. However, in real life. what often accurs is the presence of gradation in each rise of levels, and this makes microcontroller very limited in its application. Fuzzy logic will give a membership vlue to observed element so that it can meet the gradation intended. Thefuzzy logic system is the development of a control system based on microcontrnller by appliying fuzzy logic. This system comprises the hardware of microcontroller system and the assembly program software together with the fuzzy system software which is implemented on a microcontroller through a computer. Fuzzy system indesigned in the order of playing analytic methodology and input-output partitions, defining the input-output communicating surface button. Composing the rules for the definied control surface, and doing controller system design includes thefuzzyfication, rule evaluation and defuzzyficztion processes. Key words : microcomroller system, fuzzy logic controlle

Integrating Remote Sensing Data into Fuzzy Control System for Variable Rate Irrigation Estimates

Variable rate irrigation (VRI) is the capacity to vary the depth of water application in a field spatially. Developing precise management zones is necessary to efficient variable rate irrigation technologies. Intelligent fuzzy inference system based on precision irrigation knowledge, i.e., a system capable of creating prescriptive maps to control the rotation speed of the central pivot. Based on the VRI-prescribed map created by the intelligent system of decision-making, the pivot can increase or decrease its speed, reaching the desired depth of application in a certain irrigation zone. Therefore, this strategy of speed control is more realistic compared to traditional methods. Results indicate that data from the edaphoclimatic variables, when well fitted to the fuzzy logic, can solve uncertainties and non-linearities of an irrigation system and establish a control model for high-precision irrigation. Because remote sensing provides quick measurements and easy access to crop information for large irrigation areas, images will be used as inputs. The developed fuzzy system for pivot control is original and innovative. Furthermore, the artificial intelligent systems can be applied widely in agricultural areas, so the results were favorable to the continuity of studies on precision irrigation and application of the fuzzy logic in precision agriculture

A model-based fuzzy control of an induction motor

The paper presents a method of obtaining a simplified fuzzy model of an induction motor from measured data, without the necessity of preliminary knowledge of its internal structure and parameters. With the aim of avoiding a heuristic search for linguistic control rules, the paper presents one of the possibilities of the application of this method for an inverse fuzzy model based control. The proposed simplified fuzzy model of an induction motor was applied in the control of the desired torque of the drive with induction motor. Obtained results were first verified by simulation in programme Matlab and finally experimentally validated by measurements on an IC inverter–induction motor system. Simulation results and experimental measurements confirmed the correctness of the proposed fuzzy modelling and control method and its applicability also to other nonlinear dynamic systems

Prototype of Lighting Intensity Administration in Work Room With Sound Control and Fuzzy Logic Control

The use of indoor and outdoor lighting systems that are still passive makes the use of electrical energy less efficient and classified as wasteful. From the problems that occur in the system that is running, the researchers need to develop a more dynamic system by utilizing the Tsukamoto FLC method and Arduino using voice control to adjust the intensity of the light in the room. After implementing as well as testing the system that has been made, namely the Prototype of Light Intensity Regulator in the Work Room With Voice Control and Fuzzy Logic Control using NodeMCU ESP8266, it is concluded that each component can function according to its function which can be controlled and monitored from the application, implements fuzzy logic control on nodemcu with time and activity input variables obtained from the android application while the room light intensity variable is obtained from the light sensor or LDR. The results of the fuzzy process will adjust the light which is controlled by nodemcu. With the fuzzy logic control in this system, it can adjust the light to the room conditions, making it much more efficient