Autosoft Journal

Online Manuscript Access


SEARCH DATABASE




PAPERS TO SHOW






Listing 91 manuscripts matching the search of "Fuzzy logic"


A Hybrid Modular Context-Aware Services Adaptation For A Smart Living Room

by Moeiz Miraoui, Sherif El-etriby, Chakib Tadj, Abdulbasit Zaid Abid
Abstract

Smart spaces have attracted considerable amount of interest over the past few years. The introduction of sensor networks, powerful electronics and communication infrastructures have helped a lot in the realization of smart homes. The main objective of smart homes is the automation of tasks that might be complex or tedious for inhabitants by distracting them from concentrating on setting and configuring home appliances. Such automation could improve comfort, energy savings, security, and tremendous benefits for elderly persons living alone or persons with disabilities. Context awareness is a key enabling feature for development of smart homes. It allows the automation task to be done pro-actively according to the inhabitant’s current context and in an unobtrusive and seamlessly manner. Although there are several works conducted for the development of smart homes with various technologies, in most cases, robust. However, the context-awareness aspect of services adaptation was not based on clear steps for context elements extraction (resp. clear definition of context). In this paper, we use the divide and conquer approach to master the complexity of automation task by proposing a hybrid modular system for context-aware services adaptation in a smart living room. We propose to use for the context-aware adaptation three techniques of machine learning, namely Naïve Bayes, fuzzy logic and case-based reasoning techniques according to their convenience.

Volume: 24, Issue: 2

Cost-Efficient Environmentally-Friendly Control of Micro-Grids Using Intelligent Decision-Making for Storage Energy Management

by Y. S. MANJILI, Rolando Vega, Prof Mo Jamshidi
Abstract

A smart decision-making framework based on genetic algorithms (GA) and fuzzy logic is proposed for control and energy management of micro-grids. Objectives are to meet the demand profile, minimize electricity consumption cost, and to modify air pollution under a dynamic electricity pricing policy. The energy demand in the micro-grid network is provided by distributed renewable energy generation (coupling solar and wind), battery storage and balancing power from the electric utility. The fuzzy intelligent approach allows the calculation of the energy exchange rate of the micro-grid storage unit as a function of time. Such exchange rate (or decision-making capability) is based on (1) the electrical energy price per kilowatt-hour (kWh), (2) local demand (load), (3) electricity generation rate of renewable resources (supply), and (4) air pollution measure, all of which are sampled at predefined rates. Then, a cost function is defined as the net dollar amount corresponding to electricity flow between micro-grid and the utility grid. To define the cost function one must consider the cost incurred by the owner of the micro-grid associated to its distribution losses, in addition to its demand and supply costs, in such a way that a positive cost translates to owner losses and a negative cost is a gain. Six likely scenarios were defined to consider different micro-grid configurations accounting for the conditions seen in micro-grids today and also the conditions to be seen in the future. GA is implemented as a heuristic (DNA-based) search algorithm to determine the sub-optimal settings of the fuzzy controller. The aforementioned net cost (which includes pricing, demand and supply measures) and air pollution measures are then compared in every scenario with the objective to identify best-practices for energy control and management of micro-grids. Performance of the proposed GA-fuzzy intelligent approach is illustrated by numerical examples, and the capabilities and flexibility of the proposed framework as a tool for solving intermittent multi-objective function problems are presented in detail. Micro-grid owners looking into adopting a smart decision-making tool for energy storage management may see an ROI between 5 and 10.

Volume: 19, Issue: 4

Fuzzy Switching Control Of The Complete Oil Cycle Of Shiraz Solar Power Plant

by M. J. Zeini J., M. Eghtesad, M. Yaghoubi
Abstract

In this paper, fuzzy switching control of the complete oil cycle of Shiraz solar power plant is presented. So far, in the literature, only modeling and control of the collectors’ field of a solaz power plant, which is a part of the complete oil cycle, have been studied. According to various environrnental conditions, input disturbances and design constraints in some components, changes of the oil cycle working loops become necessary which cause switching in the continuous system. To control such a complex system, two controllers aze required: 1) a continuous controller to hold outlet oil temperature of the collectors’ field at its desired level and 2) a switching (discrete) controller to determine the suitable loop to be activated. In this paper, a new approach for decision making on switching actions among various loops of an oil cycle is presented. Fuzzy switching is used to reasonably reduce chattering phenomena which may be caused by conditional switching. For the complete controller structure of the system, a combination of continuous and switching controllers is used: Fuzzy Logic Fuzzy Switching.Simulation results show that the applied control system can manage the oil cycle in different situations especially in the presence of large step disturbances (moving clouds) and white noise. By applying such a control system, performance of the oil cycle is improved and a more uniform power generation during a day is achieved.

Volume: 15, Issue: 3

Modeling of the Inductance Variation and Control of the Switched Reluctance Motor Based on Fuzzy Logic

by Omer Bay, Cetin Elmas
Abstract

Switched Reluctance Motors (SRNs) aze increasingly popular machines in electrical drives, whose performances are directly related to their operating conditions. Their dynamic characteristics vary as conditions change. Recently, several methods of the modeling of the magnetic saturation of SRNs have been proposed as analytical functions. However, the SRM system is nonlinear and cannot be adequately described by such models. Fuzzy Logic (FL) is known that might overcome this problem. This paper introduces an attempt to use fuzzy logic to model of the inductance variation of the switched reluctance motor and to control the switched reluctance motor using fuzzy model. Fuzzy based modelling does not require an accurate mathematical model which is very difficult to obtain from a switched reluctance motor because of its inherit nonlinearities. The modelling method in this paper differs significantly from previous modelling methods. An application of fuzzy sets to the SRM drive control was also applied to the speed loop, replacing the conventional PI controller. Fuzzy logic controller (FLC) was optimised by using neural network. Simulation results were verified through experimental results and fuzzy logic model was proven to be reasonably accurate. The results of applying the fuzzy logic controller to SRM were compared to those obtained by the application of a conventional PI system. Compared to a PI control the fuzzy logic control provided a better response in terms of accuracy, and insensitivity to changes in operating conditions.

Volume: 10, Issue: 3

Design of a Fuzzy Logic Based Robotic Admittance Controller

by Sameer M. Prabhu, Devendra P. Garg
Abstract

A fuzzy logic based admittance control approach is proposed for the control of robotic end-effector forces occurring during a typical automated robotic manufacturing task. The proposed admittance control approach provides the necessary nonlinear control actions required in a typical automated robotic manufacturing task, and at the same time enables the incorporation of existing knowledge obtained from the process operator, in the design of the controller. This can significantly reduce the controller development time. The robotic deburring task is used as an example of a typical manufacturing task, although the technique can be easily extended to other manufacturing tasks. Automated robotic deburring offers an attractive alternative to manual deburring in terms of reduced costs and improved quality of the finished parts. The approaches proposed for control of deburring using conventional control techniques require an accurate process model and hence are not ideally suited for control under conditions of uncertainty in the available information about the process. Fuzzy logic control techniques offer an alternative to conventional control methods used for the deburring task. A fuzzy logic rule base is designed, using the knowledge obtained from the deburring operator, to control the deburring robot in the presence of uncertainties in the burr size and location information. The fuzzy logic controller issues corrections to the nominal robot trajectory based on the burrs encountered. The corrected robot trajectory is then input to a robot positional controller. Simulation results are presented to demonstrate the effectiveness of the proposed fuzzy logic based admittance control scheme in controlling the automated robotic deburring operation.

Volume: 4, Issue: 2

The Implementation of a Relational Fuzzy Model Based Control System on an Industrial Drying Process

by H. Bremner, B. Poslethwaite
Abstract

A fuzzy logic control system for difficult non-linear processes has been developed. This controller is structured in a very similar manner to the standard Internal Model Control design, differing only from the conventional system in that it uses a fuzzy model. The relational fuzzy model used is different from the more common Rule based or expert system, in that it uses no formal rules. A relational model of the process to be controlled is constructed by firstly assuming that all the process inputs and outputs are related. Then by using gathered process input/output data the strengths of these relationships are found and the relational fuzzy model is constructed. The relational fuzzy model-based control system has previously been tested extensively on simulations and lab-scale experiments. The control problem investigated is the control of moisture content in the outlet product from a dryer. The dryer is used to process material left over from a fermentation/brewing process. The spent grain from the fermentation process is dried, pelletised and turned into animal feed. The material is dried by contact with a hot lsquodiscrsquo arrangement, with steam in the lsquodiscsrsquo and also in the jacket of the dryer. The control of the dryer is very difficult by conventional means because of two main factors. The first is that the process has significant dead-time and the second is that the process is highly non-linear.

Volume: 4, Issue: 1

Experimental Implementation of An Adaptive Fuzzy Logic Controller for Process Control

by Jun Lu, Gordon Lee, Warren Jasper
Abstract

Complex industrial processes such as batch chemical reactors, steelmaking, dyeing processes and metal forming offer challenges in control due to the uncertainty and complexity of the processes. Model-based control methods generally require some knowledge of system structure and possibly some bounds on the uncertainty of the system parameters. Such methods as robust control and adaptive control belong to this category. In a case where system identification is not feasible, knowledge-based methods offer an alternative solution to control. One such method, fuzzy logic control (FLC), may be used to simulate the decision making process of an experienced expert. Usually, the control decisions of an expert can be expressed linguistically as a set of heuristic decision rules. The rules are used to build rulebases for FLC; further, algorithms are used to convert the results of the rules to quantitative outputs.This article presents an adaptive multi-input, multi-output (MIMO) fuzzy logic control method which can be applied to unknown or partially unknown systems. This method will leam about the system as it controls the process. While several fuzzy logic control schemes have been developed, including adaptive algorithms, many of these techniques can only handle single input/single output systems. The MIMO method developed here employs optimization techniques with FLC to provide the control inputs. Further, verification of the adaptive FLC method is shown through actual experimental results of a batch direct dyeing process.

Volume: 1, Issue: 3

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




CONTACT INFORMATION


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048
EMAIL: tsiepress@gmail.com
WEB: http://www.wacong.org/tsi/