Intelligent Control Technique for Reduction of Converter Generated EMI in DG Environment

2021 ◽  
pp. 111-129
Author(s):  
Ritesh Tirole ◽  
R R Joshi ◽  
Vinod Kumar Yadav ◽  
Jai Kumar Maherchandani ◽  
Shripati Vyas
Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2617
Author(s):  
Catalin Dumitrescu ◽  
Petrica Ciotirnae ◽  
Constantin Vizitiu

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.


2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Yassine Rabhi ◽  
Makrem Mrabet ◽  
Farhat Fnaiech

A new control system of a hand gesture-controlled wheelchair (EWC) is proposed. This smart control device is suitable for a large number of patients who cannot manipulate a standard joystick wheelchair. The movement control system uses a camera fixed on the wheelchair. The patient’s hand movements are recognized using a visual recognition algorithm and artificial intelligence software; the derived corresponding signals are thus used to control the EWC in real time. One of the main features of this control technique is that it allows the patient to drive the wheelchair with a variable speed similar to that of a standard joystick. The designed device “hand gesture-controlled wheelchair” is performed at low cost and has been tested on real patients and exhibits good results. Before testing the proposed control device, we have created a three-dimensional environment simulator to test its performances with extreme security. These tests were performed on real patients with diverse hand pathologies in Mohamed Kassab National Institute of Orthopedics, Physical and Functional Rehabilitation Hospital of Tunis, and the validity of this intelligent control system had been proved.


2018 ◽  
Vol 11 (6) ◽  
pp. 94-106
Author(s):  
Ibrahim Moukhtar ◽  
◽  
Adel A. Elbaset ◽  
Adel Z. Dein ◽  
Yasunori Mitani ◽  
...  

2014 ◽  
Vol 678 ◽  
pp. 406-409
Author(s):  
Xiao Rong Fu

An adaptive neuro-fuzzy controller of nonlinear systems is presented based on data fusion method. It reduces the input dimension of the controller using data fusion technique and simplifies the fuzzy controller’s design. The fuzzy controller was designed with self-learning of neural networks. The simulation results show that the performance of the system is superior to that using conventional fuzzy controller. It is rewarding for the research on combination of data fusion method and intelligent control technique of nonlinear systems.


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