Synchronization of Chaotic Gyro Systems via Sliding Mode Control with Cooperative Weights Neural Network

2013 ◽  
Vol 427-429 ◽  
pp. 1101-1104
Author(s):  
Yan Qiu Che ◽  
Ting Ting Yang ◽  
Xiao Qin Li ◽  
Rui Xue Li
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Closed Loop ◽  
Control Method ◽  
Sliding Mode ◽  
Closed Loop System ◽  
External Disturbances ◽  
Mode Control ◽  
Nonlinear Uncertainties ◽  
Stability Method

In this paper, a sliding mode control (SMC) with a cooperative weights neural network (CWNN) is proposed to realize the synchronization of two chaotic Gyro systems with nonlinear uncertainties and external disturbances. By the Lyapunov stability method, the overall closed-loop system is shown to be stable and chaos synchronizationis obtained. The simulation results demonstrate the effectiveness of the proposed control method.

Continuous terminal sliding mode control with extended state observer for PMSM speed regulation system

2016 ◽  
Vol 39 (8) ◽  
pp. 1195-1204 ◽  
Author(s):  
Huiming Wang ◽  
Shihua Li ◽  
Qixun Lan ◽  
Zhenhua Zhao ◽  
Xingpeng Zhou
Keyword(s):  
Sliding Mode Control ◽  
Disturbance Rejection ◽  
Closed Loop ◽  
Control Method ◽  
Sliding Mode ◽  
Terminal Sliding Mode Control ◽  
Closed Loop System ◽  
Terminal Sliding Mode ◽  
Mode Control ◽  
Speed Regulation

In this paper, we discuss the speed regulation problem of permanent magnet synchronous motor (PMSM) servo systems. Firstly, a continuous terminal sliding mode control (CTSMC) method is introduced for speed loops to eliminate the chattering phenomenon while still ensuring a strong disturbance rejection ability for the closed-loop system. However, in the presence of strong disturbances, the CTSMC law still needs to select high gain which may result in large steady-state speed fluctuations for the PMSM control system. To this end, an extended state observer (ESO)-based continuous terminal sliding mode control method is proposed. The ESO is employed to estimate system disturbances and the estimation is employed by the speed controller as a feed-forward compensation for disturbances. Compared to the conventional sliding mode control method, the proposed composite sliding control method obtains a faster convergence and better tracking performance. Also, by feed-forward compensating system disturbances and tuning down the gain of the CTSMC law, the fluctuation of steady-state speed of the closed-loop system is reduced while the disturbance rejection capability of the PMSM system is still maintained. Simulation and experimental results are provided to demonstrate the superior properties of the proposed control method.

Sliding Mode Control of a 2D Torsional MEMS Micromirror with Sidewall Electrodes

2013 ◽  
Vol 3 (1) ◽  
pp. 16-26
Author(s):  
Hui Chen ◽  
Manu Pallapa ◽  
Weijie Sun ◽  
Zhendong Sun ◽  
John T. W. Yeow
Keyword(s):  
Sliding Mode Control ◽  
Closed Loop ◽  
Sliding Mode ◽  
Loop System ◽  
Closed Loop System ◽  
Parameter Uncertainties ◽  
External Disturbances ◽  
Mode Control ◽  
Control Scheme ◽  
The Stability

This paper presents a sliding mode control scheme to improve the positioning performance of a 2-Degree-of-freedom (DOF) torsional MEMS micromirror with sidewall electrodes. The stability of closed-loop system is proved by Lyapunov stability theorem under the existence of bounded parameter uncertainties and external disturbances. Furthermore, the performance of the closed-loop system is illustrated by experimental and simulation results which verify that the feasibility and effectiveness of the proposed scheme. The results demonstrated that the torsional MEMS micromirror with the proposed sliding mode controller has a good transient response and tracking performance.

The Sliding Mode Variable Structure Control Method for Brushless DC Motors

2014 ◽  
Vol 596 ◽  
pp. 584-589
Author(s):  
Xi Jie Yin ◽  
Jian Guo Xu
Keyword(s):  
Sliding Mode Control ◽  
Control Method ◽  
Sliding Mode ◽  
Variable Structure ◽  
External Disturbances ◽  
Brushless Dc Motors ◽  
Structure Control ◽  
Brushless Dc ◽  
Mode Control ◽  
Sliding Mode Variable Structure

The sliding mode variable structure control method for brushless DC motors with uncertain external disturbances and unknown loads is studied. A neural sliding mode control scheme is proposed for reducing chattering of sliding mode control. A global sliding mode manifold is designed in this approach, which guarantees that the system states can be on the sliding mode manifold at initial time and the system robustness is increased. A radial basis function neural network (RBFNN) is applied to learn the maximum of unknown loads and external disturbances. Based on the neural networks, the switching control parameters of sliding mode control can be adaptively adjusted with uncertain external disturbances and unknown loads. Therefore, the chattering of the sliding mode controller is reduced. Simulation results proved that this control scheme is valid.

scholarly journals Fractional-Order Sliding Mode Control of Overhead Cranes   

2020 ◽  
Vol 31 (1) ◽  
pp. 68-76
Keyword(s):  
Sliding Mode Control ◽  
Fractional Order ◽  
Closed Loop ◽  
Control Structure ◽  
Adaptive System ◽  
Sliding Mode ◽  
Lyapunov Theory ◽  
Closed Loop System ◽  
Control Approach ◽  
Mode Control

We constitute a control system for overhead crane with simultaneous motion of trolley and payload hoist to destinations and suppression of payload swing. Controller core made by sliding mode control (SMC) assures the robustness. This control structure is inflexible since using fixed gains. For overcoming this weakness, we integrate variable fractional-order derivative into SMC that leads to an adaptive system with adjustable parameters. We use Mittag–Leffler stability, an enhanced version of Lyapunov theory, to analyze the convergence of closed-loop system. Applying the controller to a practical crane shows the efficiency of proposed control approach. The controller works well and keeps the output responses consistent despite the large variation of crane parameters.

On adaptive sliding mode control for improved quadrotor tracking

2017 ◽  
Vol 24 (14) ◽  
pp. 3219-3230 ◽  
Author(s):  
Sudhir Nadda ◽  
A Swarup
Keyword(s):  
Sliding Mode Control ◽  
Control Method ◽  
Sliding Mode ◽  
Lyapunov Stability Theory ◽  
Adaptive Method ◽  
Adaptation Strategy ◽  
Adaptive Sliding Mode Control ◽  
Parameter Uncertainties ◽  
External Disturbances ◽  
Mode Control

The tracking control of a quadrotor has been considered in this paper. The application of sliding mode control provides robustness against parametric uncertainties, but it requires knowledge of the upper bounds of uncertainties. An adaptation strategy has been proposed to implement sliding mode control, which does not require the upper bound of the uncertainties. The adaptive control law is derived on the basis of Lyapunov stability theory, which guarantees the tracking performance. The adaptation can be tuned faster by proper tuning, and convergence with good tracking can be achieved. The proposed adaptive method has improved robustness and provided simpler implementation. Through an illustrative simulation example, the performance of the proposed control method is presented and also compared with classical sliding mode control from the literature. It is demonstrated that the performance of quadrotor altitude tracking and convergence has been considerably improved while maintaining stability, even in presence of external disturbances and parameter uncertainties.

scholarly journals Robust Sliding Control of SEIR Epidemic Models

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Asier Ibeas ◽  
Manuel de la Sen ◽  
Santiago Alonso-Quesada
Keyword(s):  
Sliding Mode Control ◽  
Closed Loop ◽  
Sliding Mode ◽  
A Priori ◽  
Vaccination Strategy ◽  
Loop System ◽  
Theoretic Approach ◽  
Closed Loop System ◽  
Sliding Control ◽  
Mode Control

This paper is aimed at designing a robust vaccination strategy capable of eradicating an infectious disease from a population regardless of the potential uncertainty in the parameters defining the disease. For this purpose, a control theoretic approach based on a sliding-mode control law is used. Initially, the controller is designed assuming certain knowledge of an upper-bound of the uncertainty signal. Afterwards, this condition is removed while an adaptive sliding control system is designed. The closed-loop properties are proved mathematically in the nonadaptive and adaptive cases. Furthermore, the usual sign function appearing in the sliding-mode control is substituted by the saturation function in order to prevent chattering. In addition, the properties achieved by the closed-loop system under this variation are also stated and proved analytically. The closed-loop system is able to attain the control objective regardless of the parametric uncertainties of the model and the lack ofa prioriknowledge on the system.

scholarly journals A Modified RBF Neuro-Sliding Mode Control Technique for a Grid Connected PMSG Based Variable Speed Wind Energy Conversion System

2018 ◽  
Vol 2018 ◽  
pp. 1-19
Author(s):  
Rostand Marc Douanla ◽  
Godpromesse Kenné ◽  
François Béceau Pelap ◽  
Armel Simo Fotso
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Control Method ◽  
Sliding Mode ◽  
Control Technique ◽  
Nonlinear Controller ◽  
Parameter Uncertainties ◽  
Power Extraction ◽  
Mode Control ◽  
The Neural Network

A modified control scheme based on the combination of online trained neural network and sliding mode techniques is proposed to enhance maximum power extraction for a grid connected permanent magnet synchronous generator (PMSG) wind turbine system. The proposed control method does not need the knowledge of the uncertainty bounds nor the exact model of the nonlinear system. Since the neural network is trained online, the time to estimate good weights can affect the dynamic performance of the process during the startup phase. Therefore an appropriate way to smoothly and explicitly accelerate the neural network rate of convergence during the startup phase is proposed. Furthermore, a flexible grid side voltage source converter control structure which can handle both grid connected and standalone modes based on conventional proportional integral (PI) control method is presented. Simulations are done in Matlab/Simulink environment to verify the effectiveness and assess the performance of the proposed controller. The results analysis shows the superiority of the proposed RBF neuro-sliding mode controller compared to a nonlinear controller based on sliding mode control method when the system undergoes parameter uncertainties.

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