Software agent system controls comfort settings in the house

2015 ◽  
Author(s):  
Algirdas Sokas
Keyword(s):  
Fuzzy System ◽  
Fuzzy Controller ◽  
Software Agent ◽  
Range Analysis ◽  
Intelligent Building ◽  
Agent System ◽  
Fuzzy Variables ◽  
Temperature And Humidity ◽  
Rule Set ◽  
Collaboration Diagram

A building can be called intelligent when it has the means for automatic control of all systems for life activities. Intelligent environments are able to support ever-changing environmental needs by automatically and dynamically adjusting their key parameters without explicit human intervention. An intelligent building can be defined as one that is able to acquire and apply knowledge about its inhabitants and their surroundings in order to adapt to the inhabitants and meet the goals of comfort and efficiency. Agents are software programs designed to act autonomously and adaptively to achieve goals defined by their human developers. These systems make use of a knowledge base and algorithms to carry out their responsibilities. This article analyses software agent system in the building environment. How does the agent control temperature and humidity in the house, how does it make decisions? The creation tasks of software agent system are solved with the help of Agent Unified Modelling Language. The collaboration diagram describes a particular situation and is useful to present objective range analysis results. Temperature and humidity measurement and access control appliances can interact with each other with defined functions. Fuzzy controller ensures the comfort situation in the room. Fuzzy logic rules in line with the method of choice are very important during system design. Study the conventional fuzzy control, which is also known as the creator of the first Mamdani fuzzy system. Logical description of the decision engine IF - THEN a rule set of fuzzy expert system to provide connections between the fuzzy variables in order to obtain the changes that occur in the input sensor. The computer program of fuzzy system is analysed. Obtained results are discussed and conclusions are made.

Provide A Method Based on Genetic Algorithm to Optimize the Fuzzy Logic Controller for the Inverted Pendulum

2021 ◽  
Author(s):  
Shahrooz Alimoradpour ◽  
Mahnaz Rafie ◽  
Bahareh Ahmadzadeh
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy System ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Base System ◽  
Control Rules ◽  
Dynamics And Control

Abstract One of the classic systems in dynamics and control is the inverted pendulum, which is known as one of the topics in control engineering due to its properties such as nonlinearity and inherent instability. Different approaches are available to facilitate and automate the design of fuzzy control rules and their associated membership functions. Recently, different approaches have been developed to find the optimal fuzzy rule base system using genetic algorithm. The purpose of the proposed method is to set fuzzy rules and their membership function and the length of the learning process based on the use of a genetic algorithm. The results of the proposed method show that applying the integration of a genetic algorithm along with Mamdani fuzzy system can provide a suitable fuzzy controller to solve the problem of inverse pendulum control. The proposed method shows higher equilibrium speed and equilibrium quality compared to static fuzzy controllers without optimization. Using a fuzzy system in a dynamic inverted pendulum environment has better results compared to definite systems, and in addition, the optimization of the control parameters increases the quality of this model even beyond the simple case.

Software Metrics for Computing Quality of Software Agents

2020 ◽  
Vol 17 (5) ◽  
pp. 2035-2038
Author(s):  
E. Ajith Jubilson ◽  
Ravi Sankar Sangam
Keyword(s):  
Software Metrics ◽  
Online Shopping ◽  
Software Agents ◽  
Evaluation Process ◽  
Building Blocks ◽  
Software Agent ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

Metrics are the essential building blocks for any evaluation process. They establish specific goals for improvement. Multi agent system (MAS) is complex in nature, due to the increase in complexity of developing a multi agent system, the existing metrics are less sufficient for evaluating the quality of an MAS. This is due to the fact that agent react in an unpredictable manner. Existing metrics for measuring MAS quality fails to addresses potential communication, initiative behaviour and learn-ability. In this work we have proposed additional metrics for measuring the software agent. A software agent for online shopping system is developed and the metrics values are obtained from it and the quality of the multi agent system is analysed.

Fuzzy logic control to improve the performance of the direct power control based DFIG

2013 ◽  
Vol 33 (1/2) ◽  
pp. 254-272 ◽  
Author(s):  
Mohammad Verij Kazemi ◽  
Morteza Moradi ◽  
Reza Verij Kazemi
Keyword(s):  
Fuzzy Logic ◽  
Power Control ◽  
Fuzzy System ◽  
Fuzzy Controller ◽  
New Method ◽  
Direct Power ◽  
Direct Power Control ◽  
Simple Method ◽  
Content Type ◽  
Sector Rotation

Purpose – A direct power control (DPC) of the doubly-fed induction generator (DFIG) is presented. A new method, which is based on the rotation of the space sector, clockwise or vice versa, is proposed to improve the performance of the switching table. Then, it is combined with a fuzzy system to have advantages of both rotation sector and fuzzy controller. The paper aims to discuss these issues. Design/methodology/approach – In this paper, a new DPC of the DFIG is presented. To improve the performance of the switching table, a new method is proposed. The method is based on the rotation of the space sector, clockwise or vice versa. The excellence of the proposed method is proven. Then, it is shown that the performance of the system can be enhanced by using a fuzzy logic controller. The rotation method is combined with a fuzzy system. Findings – Simulation shows that although sector rotation and fuzzy controller can improve the performance of the DFIG, a combination of both demonstrates a smoother response in order that reactive and active power ripples and THD of the injected current decrease in different speeds. Also, it is demonstrated that the proposed method is robust against parameters variations. However, a hardware experiment should be performed to be practically verified. Originality/value – A sector rotation is proposed and its effect on the performance of the DFIG is considered. A simple method to write rules table is presented and the performance of sector rotation and fuzzy controller on the DFIG is analysed.

scholarly journals Multi-agent system for computer science and engineering education

2017 ◽  
Vol 58 ◽  
Author(s):  
Jaroslav Meleško ◽  
Eugenijus Kurilovas ◽  
Irina Krikun
Keyword(s):  
Learning Styles ◽  
Intelligent Agents ◽  
Educational Data Mining ◽  
Software Agent ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Agent Systems ◽  
Multi Agent ◽  
Identification Software

The paper aims to analyse application trends of intelligent multi-agent systems to personalise learning. First of all, systematic literature review was performed. Based on the systematic review analysis, the main trends on applying multi-agent systems to personalise learning were identified. Second, main requirements and components for an educational multi-agent system were formulated. Third, based on these components a model of intelligent personalized system is proposed. The system employs five intelligent agents: (1) learning styles identification software agent, (2) learner profile creation software agent, (3) pedagogical suitability software agent, (4) optimal learning units/scenarios creation software agent, and (5) learning analytics/educational data mining software agent.

scholarly journals SYNTHESIS OF A FUZZY TWO-LINKED REGULATOR FOR FREQUENCY ELECTRIC DRIVES OF A GRAIN MACHINE WITH A FOCUS OF THE GRAIN FLOW

2021 ◽  
Vol 3 (55) ◽  
pp. 16-23
Author(s):  
E. Burdilna ◽  
◽  
І. Konokh ◽  
S. Serhiienko
Keyword(s):  
Control System ◽  
Fuzzy System ◽  
Fuzzy Controller ◽  
Practical Application ◽  
Electric Drives ◽  
Frequency Converters ◽  
Grain Flow ◽  
Microprocessor Control System ◽  
Working Bodies ◽  
Definition Of

Purpose. A control system for grain overloading during post-harvest processing with grain throwers has been proposed, the use of which will reduce damage and grain loss. An automated electromechanical system is presented that can be installed on agricultural machinery without additional modernization of the working bodies. Methodology. As a result of the analysis of control systems, a system with elements of fuzzy logic was selected, which will clearly define the necessary control actions and take into account additional parameters that can affect the overload process. A functional diagram of a microprocessor control system for an electric drive using a fuzzy system is proposed. The result of the system operation will be the definition of the control task for the frequency converters of the drive motors of the grain thrower mechanisms. Originality. Based on the simulation of grain flight trajectories, the dependences of the flight range on the conveyor speed and additional blowing were formed, which were used to determine the settings of the fuzzy controller. Results. Fuzzy system settings are performed: a set of input and output variables is defined; the number and location of terms are selected; a set of production rules for the system is formulated. As a result of experimental modeling, graphs of the formation of mastering influences by a fuzzy controller under the condition of the appearance and disappearance of the headwind for different given distances of movement were obtained. Practical value. Practical application of the offered system will allow to reduce material losses from injury of the overloaded grain and to increase efficiency of work of the grain thrower. Figures 8, references 21.

Global non-quadratic Lyapunov-based stabilization of T–S fuzzy systems: A descriptor approach

2020 ◽  
Vol 26 (19-20) ◽  
pp. 1765-1778
Author(s):  
Navid Vafamand
Keyword(s):  
Stability Analysis ◽  
Lyapunov Function ◽  
Global Stability ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Closed Loop ◽  
Fuzzy Controller ◽  
Performance Criteria ◽  
Membership Functions ◽  
Quadratic Lyapunov Function

This article studies the problem of global stability of the Takagi–Sugeno fuzzy systems based on a novel descriptor-based non-quadratic Lyapunov function. A modified non-quadratic Lyapunov function, which comprises an integral term of the membership functions, and a modified non-parallel distributed controller constructed by constant delayed premise variables are considered that assure the global stability of the closed-loop T–S fuzzy system. The special structure of the used non-quadratic Lyapunov function results in time-delayed terms of the membership functions, instead of appearing their time derivatives, which is the well-known issue of the common non-quadratic Lyapunov functions in the literature. Also, the memory fuzzy controller is chosen such that the artificial constant delay-dependent stability analysis conditions for a non-delayed closed-loop T–S fuzzy system are formulated in terms of linear matrix inequalities. To further reduce the conservatives, some slack matrices are introduced by deploying the descriptor representation and decoupling lemmas. Moreover, the design of the robust fuzzy controller is studied through the [Formula: see text] performance criteria. The main advantages of the proposed approach are its small conservatives and the global stability analysis, which distinguish it from the state-of-the-art methods. To show the merits of the proposed approach, comparison results are provided, and two numerical case studies, namely, flexible joint robot and two-link joint robot are considered.

A Fuzzy Controller for Tentacle Cooperative Robots

2001 ◽  
Author(s):  
Mircea Ivanescu ◽  
Nicu Bizdoaca ◽  
Dorian Cojocaru ◽  
Nirvana Popescu ◽  
Decebal Popescu
Keyword(s):  
Control Strategy ◽  
Fuzzy System ◽  
Fuzzy Controller ◽  
Control Algorithms ◽  
Control Parameters ◽  
Software Simulation ◽  
Contact Points ◽  
Simulation Results ◽  
Hard Contact ◽  
The Stability

Abstract A fuzzy system and the control algorithms are proposed to solve the control multi-chain robotic system formed by tentacle manipulators grasping a commune object with hard contact points. The control system contains two parts: the first component is a conventional controller, which implements a control strategy based on the Lyapunov stability, and the second is an adaptive fuzzy controller which adjusts the control parameters by the output of the first level controller. The stability and robustness is investigated and the fuzzy rules are established The fuzzy controller was developed using Matlab and Simulink software. Simulation results are presented and discussed.

Sign in / Sign up

Export Citation Format

Share Document