A Novel Speed Control for DC Motors: Sliding Mode Control, Fuzzy Inference System, Neural Networks and Genetic Algorithms

2012 ◽  
Author(s):  
Paul Cepeda ◽  
Pedro Ponce ◽  
Arturo Molina
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Sliding Mode Control ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Sliding Mode ◽  
Speed Control ◽  
Inference System ◽  
Mode Control ◽  
Dc Motors

Ride comfort analysis of passenger body biodynamics in active quarter car model using adaptive neuro-fuzzy inference system based super twisting sliding mode control

2019 ◽  
Vol 25 (12) ◽  
pp. 1866-1882 ◽  
Author(s):  
Devdutt Singh
Keyword(s):  
Sliding Mode Control ◽  
Human Body ◽  
Ride Comfort ◽  
Fuzzy Inference ◽  
Sliding Mode ◽  
Inference System ◽  
Quarter Car Model ◽  
Car Model ◽  
Mode Control ◽  
Quarter Car

In this paper, a four degrees of freedom biodynamic human body model is used for ride comfort analysis, which is coupled with a three degrees of freedom quarter car model. The random road profile is generated in a simulation environment using the ISO 8608:2016 standard. In order to suppress the adverse effects of road induced vibrations on the human body, a super-twisting sliding mode control (STSMC) and adaptive neuro-fuzzy inference system (ANFIS) based super-twisting sliding mode control (ASTSMC) strategy is used in the main suspension of the active quarter car model. The ride comfort response of the human body segments is compared for passive and active suspension systems using the ISO 2631-1:1997 standard. Based on the simulation results in time and frequency domain related to acceleration and displacement response for head and neck, upper torso, viscera and lower torso, it is shown that the ride comfort provided by the ASTSMC controller is much improved compared to the STSMC and passive control method. It can be finalized from the present research work that active suspension with the ASTSMC control strategy can successfully reduce the adverse effects of road induced vibrations on human body health and safety.

scholarly journals Basic Tutorial on Sliding Mode Control in Speed Control of DC-motor

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Hari Maghfiroh ◽  
Augustinus Sujono ◽  
Musyaffa' Ahmad ◽  
Chico Hermanu Brillianto Apribowo
Keyword(s):  
Sliding Mode Control ◽  
High Performance ◽  
Sliding Mode ◽  
Speed Control ◽  
Dc Motor ◽  
Step Response ◽  
Signal Tracking ◽  
Mode Control ◽  
Dc Motors ◽  
Overshoot And Undershoot

<p class="Abstract"><em>One technology to support production speed is electric motors with high performance, efficiency, dynamic speed and good speed responses. DC motors are one type of electric motor which is used in the industry. Sliding Mode Control (SMC) is the robust non-linear control. The basic theory regarding SMC is presented. The SMC design which is implemented is the speed control of the DC motor is analyzed. The controller is implemented in simulation using MATLAB / Simulink environment. The step response and signal tracking test unit are carried out. The results show that SMC has a better performance compare to PID which is faster settling time and no overshoot and undershoot. </em></p><p class="Abstract"> </p>

scholarly journals ANFIS-sliding mode control of a DFIG supplied by a two-level SVPWM technique for wind energy conversion system

2020 ◽  
Vol 9 (1) ◽  
pp. 36
Author(s):  
Habib Benbouhenni
Keyword(s):  
Sliding Mode Control ◽  
Pulse Width Modulation ◽  
Reactive Power ◽  
Fuzzy Inference ◽  
Sliding Mode ◽  
Inference System ◽  
Wind Energy Conversion ◽  
Mode Control ◽  
Neuro Fuzzy ◽  
Doubly Fed

A modified adaptive neuro-fuzzy inference system sliding mode control (ANFIS-SMC) by using two-level space vector pulse width modulation (SVPWM) for doubly fed induction generator (DFIG) is proposed in this article. ANFIS-SMC with SVPWM strategy improves the basic SMC performances, which features low stator active and reactive power and also minimize the total distortion harmonic (THD) of stator current. The computer simulation results, in Matlab, demonstrate the effectiveness of the proposed control strategy which improves the performance of the DFIG.

Sliding Mode Control of a Wastewater Plant with Neural Networks and Genetic Algorithms

2007 ◽  
pp. 120-129
Author(s):  
Miguel A. Jaramillo-Morán ◽  
Juan C. Peguero-Chamizo ◽  
Enrique Martínez de Salazar ◽  
Montserrat García del Valle
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Mode Control

Combined Sliding Mode Control with a Feedback Linearization for Speed Control of Induction Motor

2011 ◽  
Vol 7 (1) ◽  
pp. 19-24
Author(s):  
Aamir Hashim Obeid Ahmed ◽  
Martino O. Ajangnay ◽  
Shamboul A. Mohamed ◽  
Matthew W. Dunnigan
Keyword(s):  
Sliding Mode Control ◽  
Induction Motor ◽  
Feedback Linearization ◽  
Sliding Mode ◽  
Speed Control ◽  
Mode Control
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