Automated Light Controller Using Fuzzy Logic

This study describes the implementation of fuzzy logic in designing fuzzy automated light controller. The fuzzy controller controls the number of lamps lighted up based on the number of people inside the room. Its main objective is to demonstrate how fuzzy logic can minimize the number of lamps used and therefore reduce the electricity consumption. In this study, fuzzy logic controller has been implemented and tested to predict the behaviour of the controller under different light conditions by monitoring the membership function parameters. In a conventional light controller, the lamps change according to user’s specification. The light will remain on if the user forgets to switch off the light. Even if an automated light controller exist, at most the system can only be controlled as on and off without being able to adapt with dynamic inputs. Fuzzy logic offers a better method than conventional control methods, especially in the case of counting the number of people and how much the light intensity is needed. In this study, fuzzy logic has the ability to make decision as to how much the light intensity is needed by controlling the number of lamps in the room according to the number of people who have entered or left the room. On the other hand, the conventional light controller does not have the ability to solve this kind of issues. It would be more practical to let more lamps "on" if the light intensity needed is very bright. A conventional method controller for this decision is difficult to find while fuzzy logic controller simplifies the task. This study has achieved its objective, which is to design a fuzzy logic system integrated with hardware circuit of automated light controller using fuzzy logic to control light intensity in a room. In this study, tests cases have illustrated that fuzzy logic control method could be a suitable alternative method to conventional control methods that could save electricity consumption and offers ease of use to human being

Automated Light Intensity Controller Using Fuzzy Logic

This study describes the implementation of fuzzy logic in designing fuzzy automated light controller. The fuzzy controller controls the number of lamps lighted up based on the number of people inside the room. Its main objective is to demonstrate how fuzzy logic can minimize the number of lamps used and therefore reduce the electricity consumption. In this study, fuzzy logic controller has been implemented and tested to predict the behaviour of the controller under different light conditions by monitoring the membership function parameters. In a conventional light controller, the lamps change according to user's specification. The light will remain on if the user forgets to switch off the light. Even if an automated light controller exist, at most the system can only be controlled as on and off without being able to adapt with dynamic inputs. Fuzzy logic offers a better method than conventional control methods, especially in the case of counting the number of people and how much the light intensity is needed. In this study, fuzzy logic has the ability to make decision as to how much the light intensity is needed by controlling the number of lamps in the room according to the number of people who have entered or left the room. On the other hand, the conventional light controller does not have the ability to solve this kind of issues. It would be more practical to let more lamps "on" if the light intensity needed is very bright. A conventional method controller for this decision is difficult to find while fuzzy logic controller simplifies the task. This study has achieved its objective, which is to design a fuzzy logic system integrated with hardware circuit of automated light controller using fuzzy logic to control light intensity in a room. In this study, tests cases have illustrated that fuzzy logic control method could be a suitable alternative method to conventional control methods that could save electricity consumption and offers ease of use to human being

Hardware/software codesign methodology for fuzzy controller implementation

This paper describes a HW/SW codesign methodology for the implementation of fuzzy controllers on a platform composed by a general-purpose microcontroller and specific processing elements implemented on FPGAs or ASICs. The different phases of the methodology, as well as the CAD tools used in each design stage, are presented, with emphasis on the fuzzy system development environment Xfuzzy. Also included is a practical application of the described methodology for the development of a fuzzy controller for a dosage system

Neuro-Fuzzy Computing System with the Capacity of Implementation on Memristor-Crossbar and Optimization-Free Hardware Training

In this paper, first we present a new explanation for the relation between logical circuits and artificial neural networks, logical circuits and fuzzy logic, and artificial neural networks and fuzzy inference systems. Then, based on these results, we propose a new neuro-fuzzy computing system which can effectively be implemented on the memristor-crossbar structure. One important feature of the proposed system is that its hardware can directly be trained using the Hebbian learning rule and without the need to any optimization. The system also has a very good capability to deal with huge number of input-out training data without facing problems like overtraining.Comment: 16 pages, 11 images, submitted to IEEE Trans. on Fuzzy system

On-line multiobjective automatic control system generation by evolutionary algorithms

Evolutionary algorithms are applied to the on- line generation of servo-motor control systems. In this paper, the evolving population of controllers is evaluated at run-time via hardware in the loop, rather than on a simulated model. Disturbances are also introduced at run-time in order to pro- duce robust performance. Multiobjective optimisation of both PI and Fuzzy Logic controllers is considered. Finally an on-line implementation of Genetic Programming is presented based around the Simulink standard blockset. The on-line designed controllers are shown to be robust to both system noise and ex- ternal disturbances while still demonstrating excellent steady- state and dvnamic characteristics

Memristor Crossbar-based Hardware Implementation of IDS Method

Ink Drop Spread (IDS) is the engine of Active Learning Method (ALM), which is the methodology of soft computing. IDS, as a pattern-based processing unit, extracts useful information from a system subjected to modeling. In spite of its excellent potential in solving problems such as classification and modeling compared to other soft computing tools, finding its simple and fast hardware implementation is still a challenge. This paper describes a new hardware implementation of IDS method based on the memristor crossbar structure. In addition of simplicity, being completely real-time, having low latency and the ability to continue working after the occurrence of power breakdown are some of the advantages of our proposed circuit.Comment: 16 pages, 13 figures, Submitted to IEEE Transaction on Fuzzy System