Sliding Mode with Neural Network Regulator for DFIG Using Two-Level NPWM Strategy
author
Abstract:
This article presents a sliding mode control (SMC) with artificial neural network (ANN) regulator for the doubly fed induction generator (DFIG) using two-level neural pulse width modulation (NPWM) technique. The proposed control scheme of the DFIG-based wind turbine system (WTS) combines the advantages of SMC control and ANN regulator. The reaching conditions, robustness and stability of the system with the proposed control are guaranteed. The SMC method which is insensitive to uncertainties, including parameter variations and external disturbances in the whole control process. Finally, the SMC control with neural network regulator (NSMC) is used to control the stator reactive and a stator active power of a DFIG supplied by the NPWM strategy and confirms the validity of the proposed approach. Results of simulations containing tests of robustness and tracking tests are presented.
similar resources
Direct Power Control With NSTSM Algorithm for DFIG Using SVPWM Technique
The paper presents a super-twisting sliding mode (STSM) regulator with neural networks (NN) of direct power command (DPC) for controlling the active/reactive power of a doubly-fed induction generator (DFIG) using a two-level space vector pulse width modulation (2L-SVPWM). Traditional DPC strategy with proportional-integral (PI) controllers (DPC-PI) has significantly more active/reactive power r...
full textNeural Network MLP with Sliding Mode Controller for Robotic Manipulator
In this paper, MLP network with sliding-mode controller and neural control is designed to the joint position control of two-link robot manipulators for periodic motion and predefined trajectory tracking control. Because the nonlinear nature robot, first the nonlinear and robust method sliding mode as a control theory for the system is chosen. Chattering phenomenon is always a problem of sliding...
full textHybrid Adaptive Neural Network AUV controller design with Sliding Mode Robust Term
This work addresses an autonomous underwater vehicle (AUV) for applying nonlinear control which is capable of disturbance rejection via intelligent estimation of uncertainties. Adaptive radial basis function neural network (RBF NN) controller is proposed to approximate unknown nonlinear dynamics. The problem of designing an adaptive RBF NN controller was augmented with sliding mode robust term ...
full textDynamic Sliding Mode Control of Nonlinear Systems Using Neural Networks
Dynamic sliding mode control (DSMC) of nonlinear systems using neural networks is proposed. In DSMC the chattering is removed due to the integrator which is placed before the input control signal of the plant. However, in DSMC the augmented system is one dimension bigger than the actual system i.e. the states number of augmented system is more than the actual system and then to control of such ...
full textNeural Network Global Sliding Mode PID Control for Robot Manipulators
This paper presents a neural network global PID-sliding mode control method for the tracking control of robot manipulators with bounded uncertainties. A certain sliding mode controller with PID sliding function is developed. In this controller, the switching gain is tuned by a RBF neural network on the reachable condition of sliding mode. Thus, the effect of chattering can be alleviated. Moreov...
full textSliding Mode Control Using Neural Networks
Variable structure control with sliding mode, which is commonly known as sliding mode control (SMC), is a nonlinear control strategy that is well known for its robust characteristics (Utkin, 1977). The main feature of SMC is that it can switch the control law very fast to drive the system states from any initial state onto a user-specified sliding surface, and to maintain the states on the surf...
full textMy Resources
Journal title
volume 15 issue 3
pages 411- 419
publication date 2019-09
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023