scholarly journals Adaptive Neural Control for Hysteresis Motor Driving Servo System with Bouc-Wen Model

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xuehui Gao
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Neural Control ◽  
Servo System ◽  
High Gain ◽  
Tracking Errors ◽  
Vector Error ◽  
Canonical State ◽  
Adaptive Control Strategy ◽  
High Order Neural Network

An adaptive high-order neural network (HONN) control strategy is proposed for a hysteresis motor driving servo system with the Bouc-Wen model. To simplify control design, the model is rewritten as a canonical state space form firstly through coordinate transformation. Then, a high-gain state observer (HGSO) is proposed to estimate the unknown transformed state. Afterward, a filter for the tracking errors is adopted which converts the vector error e into a scalar error s. Finally, an adaptive HONN controller is presented, and a Lyapunov function candidate guarantees that all the closed-loop signals are uniformly ultimately bounded (UUB). Simulations verified the effectiveness of the proposed neural network adaptive control strategy for the hysteresis servo motor system.

scholarly journals Lookup Table and Neural Network Hybrid Strategy for Wind Turbine Pitch Control

2021 ◽  
Vol 13 (6) ◽  
pp. 3235
Author(s):  
J. Enrique Sierra-García ◽  
Matilde Santos
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Control Strategy ◽  
Neural Control ◽  
Mapping Function ◽  
Energy System ◽  
Lookup Table ◽  
Hybrid Strategy ◽  
Pitch Control ◽  
The Neural Network

Wind energy plays a key role in the sustainability of the worldwide energy system. It is forecasted to be the main source of energy supply by 2050. However, for this prediction to become reality, there are still technological challenges to be addressed. One of them is the control of the wind turbine in order to improve its energy efficiency. In this work, a new hybrid pitch-control strategy is proposed that combines a lookup table and a neural network. The table and the RBF neural network complement each other. The neural network learns to compensate for the errors in the mapping function implemented by the lookup table, and in turn, the table facilitates the learning of the neural network. This synergy of techniques provides better results than if the techniques were applied individually. Furthermore, it is shown how the neural network is able to control the pitch even if the lookup table is poorly designed. The operation of the proposed control strategy is compared with the neural control without the table, with a PID regulator, and with the combination of the PID and the lookup table. In all cases, the proposed hybrid control strategy achieves better results in terms of output power error.

Control Method for Flexible Joints in Manipulator Based on BP Neural Network Tuning PI Controller

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3146
Author(s):  
Hexu Yang ◽  
Xiaopeng Li ◽  
Jinchi Xu ◽  
Dongyang Shang ◽  
Xingchao Qu
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Servo System ◽  
Pole Assignment ◽  
Pi Controller ◽  
Friction Torque ◽  
System Model ◽  
Angle Error ◽  
Assignment Method

With the development of robot technology, integrated joints with small volume and convenient installation have been widely used. Based on the double inertia system, an integrated joint motor servo system model considering gear angle error and friction interference is established, and a joint control strategy based on BP neural network and pole assignment method is designed to suppress the vibration of the system. Firstly, the dynamic equation of a planetary gear system is derived based on the Lagrange method, and the gear vibration of angular displacement is calculated. Secondly, the vibration displacement of the sun gear is introduced into the motor servo system in the form of the gear angle error, and the double inertia system model including angle error and friction torque is established. Then, the PI controller parameters are determined by pole assignment method, and the PI parameters are adjusted in real time based on the BP neural network, which effectively suppresses the vibration of the system. Finally, the effects of friction torque, pole damping coefficient and control strategy on the system response and the effectiveness of vibration suppression are analyzed.

Study on Decreasing Vibration of High-Speed Train Semi-Active Suspension System

2011 ◽  
Vol 230-232 ◽  
pp. 1104-1109
Author(s):  
Zhen Ping Fan ◽  
Heng Zeng ◽  
Jian Wei Yang ◽  
Jie Li
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
High Speed ◽  
Driving Safety ◽  
High Speed Train ◽  
Proportional Valve ◽  
Joint Simulation ◽  
The Neural Network ◽  
Adaptive Control Strategy ◽  
Active Damper

Lateral semi-active damper is designed by author based on the electro-hydraulic proportional valve, from the perspective angle of improving vehicle comfort; its purpose is to ensure vehicle driving safety. At the same time, the neural network adaptive control strategy is used for joint simulation of semi-active damper. The results show that lateral semi-active damper with the train body has significantly improved compared to the traditional passive lateral damper acceleration.

Electro-Hydraulic Servo Control Based on CMAC Neural Network for Excavating Robot

2010 ◽  
Vol 34-35 ◽  
pp. 825-830
Author(s):  
Qun Liang Dai ◽  
Hong Liang Dai ◽  
Xiao Hai Qu
Keyword(s):  
Neural Network ◽  
Dynamic Model ◽  
Strong Coupling ◽  
Intelligent Control ◽  
Control Strategy ◽  
Servo System ◽  
Servo Control ◽  
Cmac Neural Network ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

In this paper the electric-hydraulic servo system for excavating robot is analysed. The kinematic and the dynamic model of working equipment are established. Aim at the electric-hydraulic servo system of the feature with many variables, strong coupling and non-linear, the CMAC neural network was presented combined with popular PD algorithm, which could realize intelligent control for the working equipment of excavating robot. The result of simulation show that control strategy features higher precision and robustness.

Experimental Evaluation of a High-Gain Control for Compressor Surge Suppression

2001 ◽  
Author(s):  
Franco Blanchini ◽  
Pietro Giannattasio ◽  
Diego Micheli ◽  
Piero Pinamonti
Keyword(s):  
Control Strategy ◽  
Control Strategies ◽  
Gain Control ◽  
High Gain ◽  
Control Device ◽  
Guarantee System ◽  
Operation Speed ◽  
Compressor Surge ◽  
Adaptive Control Strategy ◽  
Proportional Controller

The present paper considers the suppression of surge instability in compression systems by means of active control strategies based on a high-gain approach. A proper sensor-actuator pair and a proportional controller are selected which, in theory, guarantee system stabilization in any operating condition for a sufficiently high value of the gain. Furthermore, an adaptive control strategy is introduced which allows the system to automatically detect a suitable value of the gain needed for stabilization, without requiring any knowledge of the compressor and plant characteristics. The control device is employed to suppress surge in an industrial compression system based on a four-stage centrifugal blower. An extensive experimental investigation has been performed in order to test the control effectiveness in various operating points on the stalled branch of the compressor characteristic and at different compressor speeds. On one hand the experimental results confirm the good performance of the proposed control strategy, on the other they show some inherent difficulties in stabilizing the system at high compressor speeds due to the measurement disturbances and to the limited operation speed of the actuator.

scholarly journals RBF Neural Network Sliding Mode Control Method Based on Backstepping for an Electro-hydraulic Actuator

2020 ◽  
Vol 66 (12) ◽  
pp. 697-708
Author(s):  
Wending Li ◽  
Guanglin Shi ◽  
Chun Zhao ◽  
Hongyu Liu ◽  
Junyong Fu
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Control Strategy ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Servo System ◽  
Control Law ◽  
Hydraulic Actuator ◽  
Mode Control ◽  
Non Linear System

Aiming at the interference problem and the difficulty of model parameter determination caused by the nonlinearity of the valve-controlled hydraulic cylinder position servo system, this study proposes a radial basis function (RBF) neural network sliding mode control strategy based on a backstepping strategy for the electro-hydraulic actuator. First, the non-linear system model of the third-order position electro-hydraulic control servo system is established on the basis of the principle analysis. Second, the model function RBF adaptive law and backstepping control law are designed according to Lyapunov’s stability theorem to solve the problem of external load disturbance and modelling uncertainty, combined with sliding mode control strategy and virtual control law. Finally, simulation and experiment on MATLAB Simulink and semi-physical experimental platform are accomplished to show the effectiveness of the proposed method. Moreover, results show that the designed controller has high tracking accuracy to the given signal.

Experimental Evaluation of a High-Gain Control for Compressor Surge Suppression

2002 ◽  
Vol 124 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Franco Blanchini ◽  
Pietro Giannattasio ◽  
Diego Micheli ◽  
Piero Pinamonti
Keyword(s):  
Control Strategy ◽  
Control Strategies ◽  
Gain Control ◽  
High Gain ◽  
Control Device ◽  
Guarantee System ◽  
Operation Speed ◽  
Compressor Surge ◽  
Adaptive Control Strategy ◽  
Proportional Controller

The present paper considers the suppression of surge instability in compression systems by means of active control strategies based on a high-gain approach. A proper sensor-actuator pair and a proportional controller are selected that, in theory, guarantee system stabilization in any operating condition for a sufficiently high value of the gain. Furthermore, an adaptive control strategy is introduced that allows the system automatically to detect a suitable value of the gain needed for stabilization, without requiring any knowledge of the compressor and plant characteristics. The control device is employed to suppress surge in an industrial compression system based on a four-stage centrifugal blower. An extensive experimental investigation has been performed in order to test the control effectiveness in various operating points on the stalled branch of the compressor characteristic and at different compressor speeds. On one hand, the experimental results confirm the good performance of the proposed control strategy; on the other, they show some inherent difficulties in stabilizing the system at high compressor speeds due to the measurement disturbances and to the limited operation speed of the actuator.

Research on Electro-Hydraulic Force Servo System Based on Neural Network and Fuzzy Intelligent Control Strategy

2014 ◽  
Vol 11 (4) ◽  
pp. 1205-1210 ◽  
Author(s):  
Jianying Li ◽  
Yanwei Wang ◽  
Xiaojing Wang ◽  
Junpeng Shao ◽  
Tianye Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Control ◽  
Control Strategy ◽  
Servo System ◽  
Hydraulic Force

Study of Electro-Hydraulic Force Servo System Based on Flow Press Servo Valve and Neural Network Intelligent Control Strategy

2013 ◽  
Vol 427-429 ◽  
pp. 1167-1170
Author(s):  
Jian Ying Li ◽  
Tian Ye Yang ◽  
Yan Wei Wang ◽  
Zhi Yong Mao ◽  
Li Gang Wu
Keyword(s):  
Neural Network ◽  
Intelligent Control ◽  
Control Strategy ◽  
Servo System ◽  
Mathematic Model ◽  
Load Torque ◽  
Total Load ◽  
Servo Valve ◽  
Hydraulic Force ◽  
The Neural Network

The flow press servo valve has very special work principle, and it now is widely used in the electro-hydraulic force servo system as main and kernel part. According to its special work principle, electromagnetic basic theory and the flow continuity equation, the mechanics analysis of every main part of the flow press servo valve was finished; the author built the mathematic model of the flow press sever valve. During the process of building model, the author took into account the load torque on the torque motor keeper and the total load torque of torque motor. On the other hand, the neural network intelligent control strategy was used in the force servo system to improve the whole system performance. The mathematic model of the electro-hydraulic force servo system controlled by the flow press servo valve with the neural network control strategy was built. The simulation curves and the experiment curves were accord compared by system simulation and experiment results, so we can know also that the mathematic model of the flow press sever valve and the electro-hydraulic force servo system controlled by this kind of valve with the neural network intelligent control strategy are correct.

scholarly journals Application of Nonlinear Adaptive Control in Temperature of Chinese Solar Greenhouses

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1582
Author(s):  
Yonggang Wang ◽  
Yujin Lu ◽  
Ruimin Xiao
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Radial Basis Function ◽  
Control Strategy ◽  
Basis Function ◽  
Adaptive Controller ◽  
Strong Nonlinearity ◽  
Nonlinear Adaptive Control ◽  
Radial Basis ◽  
Adaptive Control Strategy

The system of a greenhouse is required to ensure a suitable environment for crops growth. In China, the Chinese solar greenhouse plays a crucial role in maintaining a proper microclimate environment. However, the greenhouse system is described with complex dynamic characteristics, such as multi-disturbance, parameter uncertainty, and strong nonlinearity. It is difficult for the conventional control method to deal with the above problems. To address these problems, a dynamic model of Chinese solar greenhouses was developed based on energy conservation laws, and a nonlinear adaptive control strategy combined with a Radial Basis Function neural network was presented to deal with temperature control. In this approach, nonlinear adaptive controller parameters were determined through the generalized minimum variance laws, while unmodeled dynamics were estimated by a Radial Basis Function neural network. The control strategy consisted of a linear adaptive controller, a neural network nonlinear adaptive controller, and a switching mechanism. The research results show that the mean errors were 0.8460 and 0.2967, corresponding to a conventional PID method and the presented nonlinear adaptive scheme, respectively. The standard errors of the conventional PID method and the nonlinear adaptive control strategy were 1.8480 and 1.3342, respectively. The experimental results fully prove that the presented control scheme achieves better control performance, which meets the actual requirements.

Sign in / Sign up

Export Citation Format

Share Document