scholarly journals Communication system improvement with control performance based on link quality in wireless sensor actuator networks

2021 ◽  
Vol 11 (6) ◽  
pp. 5089
Author(s):  
Nada N. Tawfeeq ◽  
Sawsan D. Mahmood
Keyword(s):  
Fuzzy Inference ◽  
Automatic Gain Control ◽  
Gain Control ◽  
Adaptive Controller ◽  
Link Quality ◽  
Wireless Sensor ◽  
Control Performance ◽  
Validation Data ◽  
Inference System ◽  
Sensor Actuator

<span lang="EN-US">New communication and networking paradigms started with wireless sensor actuator networks (WSANs) to introduce new applications. One of these is the automatic gain control system (AGC). It will enable a high degree of the decentralized and mobile control. In this study, neural networks (NN) with fuzzy logic (one of the techniques of artificial intelligence (AI)) is used to enhance the control performance depending on the link quality. The NN and fuzzy inference system (FIS) with Mamdani’s method used to build a model reference, adaptive controller, for recompensing for delay time packets losses, and improving the reliability of WSAN. Between 88.62% and 99.99%, validation data is obtained for the medium and high conditions of operation with the proposed algorithm. Experimental and simulation results show a promising approach.</span>

scholarly journals A Heating Controller Designing Based on Living Space Heating Dynamic’s Model Approach in a Smart Building

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 998
Author(s):  
Roozbeh Sadeghian Broujeny ◽  
Kurosh Madani ◽  
Abdennasser Chebira ◽  
Veronique Amarger ◽  
Laurent Hurtard
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Control Strategies ◽  
Time Series Prediction ◽  
Adaptive Controller ◽  
Inference System ◽  
Diverse Range ◽  
Smart Building ◽  
Living Space ◽  
Soft Computing Techniques

Most already advanced developed heating control systems remain either in a prototype state (because of their relatively complex implementation requirements) or require very specific technologies not implementable in most existing buildings. On the other hand, the above-mentioned analysis has also pointed out that most smart building energy management systems deploy quite very basic heating control strategies limited to quite simplistic predesigned use-case scenarios. In the present paper, we propose a heating control strategy taking advantage of the overall identification of the living space by taking advantage of the consideration of the living space users’ presence as additional thermal sources. To handle this, an adaptive controller for the operation of heating transmitters on the basis of soft computing techniques by taking into account the diverse range of occupants in the heating chain is introduced. The strategy of the controller is constructed on a basis of the modeling heating dynamics of living spaces by considering occupants as an additional heating source. The proposed approach for modeling the heating dynamics of living spaces is on the basis of time series prediction by a multilayer perceptron neural network, and the controlling strategy regarding the heating controller takes advantage of a Fuzzy Inference System with the Takagi-Sugeno model. The proposed approach has been implemented for facing the dynamic heating conduct of a real five-floor building’s living spaces located at Senart Campus of University Paris-Est Créteil, taking into account the occupants of spaces in the control chain. The obtained results assessing the efficiency and adaptive functionality of the investigated fuzzy controller designed model-based approach are reported and discussed.

scholarly journals A NEURAL FUZZY INFERENCE BASED ADAPTIVE CONTROLLER FOR NONHOLONOMIC ROBOTS

2011 ◽  
pp. 56-65
Author(s):  
Ting Wang ◽  
Fabien Gautero ◽  
Christophe Sabourin ◽  
Kurosh Madani
Keyword(s):  
Control Strategy ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Adaptive Controller ◽  
Reference Trajectory ◽  
Inference System ◽  
Nonholonomic Robots ◽  
Real Robot ◽  
Neuro Controller ◽  
Neural Fuzzy

In this paper, we propose a control strategy for a nonholonomic robot which is based on an Adaptive Neural Fuzzy Inference System. The neuro-controller makes it possible the robot track a desired reference trajectory. After a short reminder about Adaptive Neural Fuzzy Inference System, we describe the control strategy which is used on our virtual nonholonomic robot. And finally, we give the simulations’ results where the robot have to pass into a narrow path as well as the first validation results concerning the implementation of the proposed concepts on real robot.

ROBUST DIRECT ADAPTIVE CONTROLLER FOR A CLASS OF NONLINEAR SYSTEMS BASED ON NEURAL NETWORKS AND FUZZY LOGIC SYSTEMS

2007 ◽  
Vol 16 (03) ◽  
pp. 553-560 ◽  
Author(s):  
MOHAMED CHEMACHEMA ◽  
KHALED BELARBI
Keyword(s):  
Nonlinear Systems ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Past History ◽  
Adaptive Controller ◽  
Lyapunov Theory ◽  
Error Estimator ◽  
Fuzzy Logic Systems ◽  
Inference System ◽  
Control Error

In this paper a direct adaptive control algorithm based on a neural network NN as controller and a fuzzy inference system FIS as control error estimator is presented for a class of SISO uncertain nonlinear systems. The weights adaptation laws are based on the control error. A fuzzy inference system is used to provide an estimate of this error based on past history of the system behavior. The stability of the closed loop is studied using Lyapunov theory. Simulation results demonstrate the effectiveness of the proposed approach.

scholarly journals Increasing Network Lifetime In Cluster Based Wireless Sensor Networks Via Fuzzy Logic

2021 ◽  
Author(s):  
Rouzbeh Behrouz
Keyword(s):  
Wireless Sensor Networks ◽  
Fuzzy Logic ◽  
Sensor Networks ◽  
Large Scale ◽  
Fuzzy Inference ◽  
Critical Issue ◽  
Base Station ◽  
Wireless Sensor ◽  
Inference System ◽  
Efficient Operation

Energy efficient operation is a critical issue that has to be addressed with large-scale wireless sensor networks deployments. Cluster-based protocols are developed to tackle this problem and Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the best-known protocols of this type. However, certain aspects of LEACH offer room for improvement. One such aspect is the arrangement of wireless sensor network with the fixed base station location. In this thesis we purpose Fuzzy Logic for Mobile Base Station (FLMBS) protocol that is based on LEACH but uses a Fuzzy Inference System driven approach to adjust the location of the base station. FLMBS produces reasonable improvement over LEACH in a network area greater than 1000 x 1000 m

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