Closed Loop Control of Diode Clamped Multilevel Inverter Using Fuzzy Logic Controller

2019 ◽  
pp. 787-794
Author(s):  
K. Muralikumar ◽  
Ponnambalam Pathipooranam
Keyword(s):  
Fuzzy Logic ◽  
Closed Loop ◽  
Fuzzy Logic Controller ◽  
Multilevel Inverter ◽  
Closed Loop Control ◽  
Loop Control

A Novel Approach to GSA, GA and Wavelet Transform to Design Fuzzy Logic Controller for 1ϕ Multilevel Inverter

2016 ◽  
Vol 7 (4) ◽  
pp. 1200
Author(s):  
Varsha Singh ◽  
S. Gupta ◽  
S. Pattnaik ◽  
Aarti Goyal
Keyword(s):  
Fuzzy Logic ◽  
Wavelet Transform ◽  
Output Voltage ◽  
Closed Loop ◽  
Fuzzy Logic Controller ◽  
Multilevel Inverter ◽  
Closed Loop Control ◽  
Voltage Waveform ◽  
Loop Control ◽  
Novel Approach

<p>This paper proposes a novel approach for obtaining a closed loop control scheme based on Fuzzy Logic Controller to regulate the output voltage waveform of multilevel inverter. Fuzzy Logic Controller is used to guide and control the inverter to synthesize a stepped output voltage waveform with reduced harmonics. In this paper, three different intelligent soft-computing methods are used to design a fuzzy system to be used as a closed loop control system for regulating the inverter output. Gravitational Search Algorithm and Genetic Algorithm are used as optimization methods to evaluate switching angles for different combination of input voltages applied to MLI. Wavelet Transform is used as synthesizing technique to shape stepped output waveform of inverter using orthogonal wavelet sets. The proposed FLC controlled method is carried out for a wider range of input dc voltages by considering ±10% variations in nominal voltage value. A 7-level inverter is used to validate the results of proposed control methods. The three proposed methods are then compared in terms of various parameters like computational time, switching angles and THD to justify the performance and system flexibility. Finally, hardware based results are also obtained to verify the viability of the proposed method.</p>

A Control Strategy for Intelligent Stamp Forming Tooling

2011 ◽  
Vol 133 (6) ◽  
Author(s):  
William J. Emblom ◽  
Klaus J. Weinmann
Keyword(s):  
Fuzzy Logic ◽  
Control Systems ◽  
Control Strategy ◽  
Pid Controller ◽  
Closed Loop ◽  
Closed Loop Control ◽  
Linear Quadratic ◽  
Blank Holder ◽  
Loop Control ◽  
Stamp Forming

This paper describes the development and implementation of closed-loop control for oval stamp forming tooling using MATLAB®’s SIMULINK® and the dSPACE®CONTROLDESK®. A traditional PID controller was used for the blank holder pressure and an advanced controller utilizing fuzzy logic combining a linear quadratic gauss controller and a bang–bang controller was used to control draw bead position. The draw beads were used to control local forces near the draw beads. The blank holder pressures were used to control both wrinkling and local forces during forming. It was shown that a complex, advanced controller could be modeled using MATLAB’s SIMULINK and implemented in DSPACE CONTROLDESK. The resulting control systems for blank holder pressures and draw beads were used to control simultaneously local punch forces and wrinkling during the forming operation thereby resulting in a complex control strategy that could be used to improve the robustness of the stamp forming processes.

scholarly journals Sensor-less Brushed DC Motor Speed Control with Intelligent Controllers

2021 ◽  
Vol 20 ◽  
pp. 140-148
Author(s):  
Amir Salmaninejad ◽  
Rene V. Mayorga
Keyword(s):  
Closed Loop ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Mechanical Load ◽  
Dc Motor ◽  
Closed Loop Control ◽  
Motor Speed ◽  
Motor Shaft ◽  
Loop Control ◽  
Intelligent Controllers

A Direct Current (DC) Motor is usually supposed to be operated at a desired speed even if the load on the shaft is exposed to changes. One of its applications is in automatic door controllers like elevator automatic door drivers. Initially, to achieve this aim, a closed loop control can be applied. The speed feedback is usually prepared by a sensor (encoder or tachometer) coupled to the motor shaft. Most of these sensors do not always perform well, especially in elevator systems, where high levels of noise, physical tensions of the mobile car, and maintenance technicians walking on the car, make this environment too noisy. This Paper presents a new approach for precise closed loop control of the DC motor speed without a feedback sensor, while the output load is variable. The speed here is estimated by the Back EMF (BEMF) voltage obtained from the armature current. First, it is shown that a PID controller cannot control this process alone, and then intelligent controllers, Fuzzy Logic Controller (FLC) and Adaptive Neuro Fuzzy Inference Systems (ANFIS), assisting PID are applied to control this process. Finally, these controllers’ performance subjected to a variable mechanical load on the motor shaft are compared.

scholarly journals Control of Rotary Cranes Using Fuzzy Logic

2001 ◽  
Author(s):  
Amjed A. Al-mousa ◽  
Ali H. Nayfeh ◽  
Pushkin Kachroo
Keyword(s):  
Fuzzy Logic ◽  
Closed Loop ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Operating Conditions ◽  
Building Construction ◽  
Loop Control ◽  
Industrial Structures ◽  
Inference Engines

Abstract Rotary cranes (tower cranes) are common industrial structures that are used in building construction, factories, and harbors. These cranes are usually operated manually. With the size of these cranes becoming larger and the motion expected to be faster, the process of controlling them became difficult without using automatic control methods. In general, the movement of cranes has no prescribed path. Cranes have to be run under different operating conditions, which makes closed-loop control preferable. In this work a fuzzy logic controller is introduced with the idea of split-horizon; that is, fuzzy inference engines (FIE) are used for tracking the position and others are used for damping the load oscillations. The controller consists of two independent controllers: radial and rotational. Each of these controllers has two fuzzy inference engines (FTEs). Computer simulations are used to verify the performance of the controller. Three simulation cases are introduced: radial, compound, and damping. The results from the simulations show that the fuzzy controller is capable of keeping the load-oscillation angles small throughout the maneuvers while completing them in a relatively reasonable time.

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