A new adaptive sliding mode controller based on the RBF neural network for an electro-hydraulic servo system

This paper investigates a sliding mode controller based on quantum particle swarm optimization algorithm (QPSO) to solve the nonlinearity of electro-hydraulic servo systems, external disturbance problems, and jitter of sliding mode controller. The electro-hydraulic servo system state space equations are established, constructing the sliding surface according to the tracking error and obtaining the output of the sliding mode controller. The ITAE metric is used as an adaptation function of the QPSO algorithm to evaluate the parameters in the sliding mode controller, which has good engineering utility and parameter selectivity. The QPSO algorithm is used to increase the randomicity of the search and to expand the search space, which can effectively prevent falling into a local optimum solution. Finally, a comparative simulation is presented to illustrate global search performance of QPSO algorithm and the effectiveness and applicability of the proposed control method.

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Fault Detection and Isolation for Hydraulic Servo System Based on Adaptive Threshold and SOM Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.764-765.691 ◽

2015 ◽

Vol 764-765 ◽

pp. 691-697

Author(s):

Yu Jie Cheng ◽

Chen Lu ◽

Li Mei Wang ◽

Hong Mei Liu

Keyword(s):

Neural Network ◽

Fault Detection ◽

Rbf Neural Network ◽

Servo System ◽

Adaptive Threshold ◽

Residual Error ◽

Error Signal ◽

Hydraulic Servo ◽

Som Neural Network ◽

Hydraulic Servo System

A fault detection and diagnosis method for the hydraulic servo system based on adaptive threshold and self-organizing map (SOM) neural network is proposed in this study. The nonlinear, time-varying, fluid-solid coupling properties of the hydraulic servo system are considered. Fault detection is realized based on a two-stage radial basis function (RBF) neural network model. The first-stage RBF neural network is adopted as a fault observer for the hydraulic servo system; the residual error signal is generated by comparing the estimated observer output with the actual measurements. To overcome the drawback of false alarms when the traditional fixed fault threshold is used, an adaptive threshold producer is established by the second-stage RBF neural network. Fault occurrence is detected by comparing the residual error signal with the adaptive threshold. When a system fault is detected, the SOM neural network is employed to implement fault classification and isolation by analyzing the features of the residual error signal. Three types of common faults are simulated to verify the performance and effectiveness of the proposed method. Experimental results demonstrate that the proposed method based on adaptive threshold and SOM neural network is effective in detecting and isolating the failure of the hydraulic servo system.

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A Neural Network Based Adaptive Sliding Mode Controller for Pitch Angle Control of a Wind Turbine

2020 11th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC) ◽

10.1109/pedstc49159.2020.9088373 ◽

2020 ◽

Author(s):

Hossein Dastres ◽

Ali Mohammadi ◽

Mohammadreza Shamekhi

Keyword(s):

Neural Network ◽

Wind Turbine ◽

Pitch Angle ◽

Sliding Mode ◽

Sliding Mode Controller ◽

Adaptive Sliding Mode ◽

Adaptive Sliding Mode Controller

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Friction Compensation Control of Electromechanical Actuator Based on Neural Network Adaptive Sliding Mode

Sensors ◽

10.3390/s21041508 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1508

Author(s):

Wei Ruan ◽

Quanlin Dong ◽

Xiaoyue Zhang ◽

Zhibing Li

Keyword(s):

Neural Network ◽

Sliding Mode ◽

Dynamic Performance ◽

External Disturbance ◽

Sliding Mode Controller ◽

Electromechanical Actuator ◽

Adaptive Sliding Mode ◽

Adaptive Sliding Mode Controller ◽

Radial Basis ◽

Actuator System

In this paper, a radial basis neural network adaptive sliding mode controller (RBF−NN ASMC) for nonlinear electromechanical actuator systems is proposed. The radial basis function neural network (RBF−NN) control algorithm is used to compensate for the friction disturbance torque in the electromechanical actuator system. An adaptive law was used to adjust the weights of the neural network to achieve real−time compensation of friction. The sliding mode controller is designed to suppress the model uncertainty and external disturbance effects of the electromechanical actuator system. The stability of the RBF−NN ASMC is analyzed by Lyapunov’s stability theory, and the effectiveness of this method is verified by simulation. The results show that the control strategy not only has a better compensation effect on friction but also has better anti−interference ability, which makes the electromechanical actuator system have better steady−state and dynamic performance.

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Gain adaptive sliding mode controller based on interval type-II fuzzy neural network designed for attitude control for micro aircraft vehicle

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-03-2012-0010 ◽

2014 ◽

Vol 7 (2) ◽

pp. 209-226 ◽

Cited By ~ 4

Author(s):

Xiangjian Chen ◽

Di Li ◽

Zhijun Xu ◽

Yue Bai

Keyword(s):

Neural Network ◽

Fuzzy Neural Network ◽

Sliding Mode ◽

Sliding Mode Controller ◽

Type Ii ◽

Content Type ◽

Adaptive Sliding Mode ◽

Adaptive Sliding Mode Controller ◽

Fuzzy Neural ◽

Interval Type

Purpose – Micro aerial vehicle is nonlinear plant; it is difficult to obtain stable control for MAV attitude due to uncertainties. The purpose of this paper is to propose one robust stable control strategy for MAV to accommodate system uncertainties, variations, and external disturbances. Design/methodology/approach – First, by employing interval type-II fuzzy neural network (ITIIFNN) to approximate the nonlinearity function and uncertainty functions in the attitude angle dynamic model of micro aircraft vehicle (MAV). Then, the Lyapunov stability theorem is used to testify the asymptotic stability of the closed-loop system, the parameters of the ITIIFNN and gain of sliding mode control can be tuned on-line by adaptive laws based on Lyapunov synthesis approach, and the Lyapunov stability theorem has been used to testify the asymptotic stability of the closed-loop system. Findings – The validity of the proposed control method has been verified through real-time experiments. The experimental results show that the performance of interval type-II fuzzy neural network based gain adaptive sliding mode controller (GASMC-ITIIFNN) is significantly improved compared with conventional adaptive sliding mode controller (CASMC), type-I fuzzy neural network based sliding mode controller (GASMC-TIFNN). Practical implications – This approach has been used in one MAV, the controller works well, and which could guarantee the MAV control system with good performances under uncertainties, variations, and external disturbances. Originality/value – The main original contributions of this paper are: the proposed control scheme makes full use of the nominal model of the MAV attitude control model; the overall closed-loop control system is globally stable demonstrated by Lyapunov stable theory; the tracking error can be asymptotically attenuated to a desired small level around zero by appropriate chosen parameters and learning rates; and the MAV attitude control system based on GASMC-ITIIFNN controller can achieve favourable tracking performance than GASMC-TIFNN and CASMC.

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