scholarly journals Decoupling Control for Three Degrees of Freedom Servo System Based on Neural Network

2013 ◽  
Vol 765-767 ◽  
pp. 1840-1843
Author(s):  
Zhen Bi Li ◽  
Bai Ting Zhao ◽  
Yuan Yuan Jiang
Keyword(s):  
Neural Network ◽  
Degrees Of Freedom ◽  
Inertial Navigation System ◽  
Servo System ◽  
Good Control ◽  
Decoupling Control ◽  
Simulating Experiment ◽  
Control Precision ◽  
The Neural Network ◽  
Three Degrees Of Freedom

Three degrees of freedom servo system (TDFSS) is one of the key equipments of inertial testing, such as evaluation of inertial navigation system and test of inertial components. It is a kind of servo system with some non-linearity and uncertainty. This thesis takes advantage of the characteristic of Neural network in approaching non-linear function, applies the Neural network on the three-axis simulator, provides a method for the TDFSS. Simulating experiment has been used to verify the advantage of the scheme and achieved completely decoupling control. The scheme gives good control precision, and it is simply structured and easily implemented.Introduction

Adaline neural network-based adaptive inverse control for an electro-hydraulic servo system

2011 ◽  
Vol 17 (13) ◽  
pp. 2007-2014 ◽  
Author(s):  
Jianjun Yao ◽  
Xiancheng Wang ◽  
Shenghai Hu ◽  
Wei Fu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Servo System ◽  
Inverse Model ◽  
Lms Algorithm ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
The Neural Network ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

Based on adaptive inverse control theory, combined with neural network, neural network adaptive inverse controller is developed and applied to an electro-hydraulic servo system. The system inverse model identifier is constructed by neural network. The task is accomplished by generating a tracking error between the input command signal and the system response. The weights of the neural network are updated by the error signal in such a way that the error is minimized in the sense of mean square using (LMS) algorithm and the neural network is close to the system inverse model. The above steps make the gain of the serial connection system close to unity, realizing waveform replication function in real-time. To enhance its convergence and robustness, the normalized LMS algorithm is applied. Simulation in which nonlinear dead-zone is considered and experimental results demonstrate that the proposed control scheme is capable of tracking desired signals with high accuracy and it has good real-time performance.

Neural network inverse system decoupling control strategy of BLIM considering stator current dynamics

2018 ◽  
Vol 41 (3) ◽  
pp. 621-630 ◽  
Author(s):  
Wenshao Bu ◽  
Fangzhou He ◽  
Ziyuan Li ◽  
Haitao Zhang ◽  
Jingzhuo Shi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Strategy ◽  
Displacement Component ◽  
Inverse System ◽  
Decoupling Control ◽  
Motor Speed ◽  
State Equations ◽  
Stator Current ◽  
The Neural Network

The bearingless induction motor (BLIM) is a multi-variable, non-linear, strong coupling system. To achieve higher performance control, a novel neural network inverse system decoupling control strategy considering stator current dynamics is proposed. Taking the stator current dynamics of the torque windings into account, the state equations of the BLIM system is established first. Then, the inverse system model of the BLIM is identified by a three-layer neural network; by means of the neural network inverse system method, the BLIM system is decoupled into four independent second-order linear subsystems, include a rotor flux subsystem, a motor speed subsystem and two radial displacement component subsystems. On this basis, the neural network inverse decoupling control system is constructed, the simulation verification and analyses are performed. From the simulation results, it is clear that when the proposed decoupling control strategy is adopted, not only can the dynamic decoupling control between relevant variables be achieved, but the control system has a stronger anti-load disturbance ability, smaller overshoot and better tracking performance.

Neural Network Inverse Based Omnidirectional Rotation Decoupling Control to the Electrodynamic Reaction Sphere

2019 ◽  
Author(s):  
Zhouyu Huai ◽  
Ming Zhang ◽  
Yu Zhu ◽  
Anlin Chen ◽  
Xin Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Attitude Control ◽  
Inverse Dynamics ◽  
Back Propagation ◽  
Good Control ◽  
Original System ◽  
Decoupling Control ◽  
Field Analysis ◽  
Element Analysis ◽  
Control Scheme

Abstract The electrodynamic reaction sphere is a novel actuator for the spacecraft attitude control subsystem. This paper proposes a neural network inverse based decoupling control scheme to actualize the omnidirectional rotation of the electrodynamic reaction sphere which has strong multivariable nonlinear coupling features due to the induction-based drive. And an integrated electromagnetic torque model of the reaction sphere is firstly derived from the electromagnetic field analysis and modified with the finite element analysis method. Then based on the integrated torque model, a back propagation feedforward neural network is constructed and trained to approach the inverse dynamics which transforms the original system into a pseudo-linear system. Furthermore, an additional PI controller is introduced to achieve good control performance against the unmodelled dynamics. Finally, the effectiveness of the proposed method is validated by simulations.

Study on Glass Furance Temperature Dual Control Based on Neural Networks Decoupling Control under WINCC

2013 ◽  
Vol 341-342 ◽  
pp. 856-860
Author(s):  
Hao Ming Yang ◽  
Lan Qing Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Algorithm ◽  
Decoupling Control ◽  
Dual Control ◽  
Script Language ◽  
Learning Speed ◽  
The Neural Network ◽  
Levenberg Marquardt ◽  
Experiment Control

Experiment control platform for the neural network decoupling control is constructed for the glass furnace taking heavy oil as fuel. By dual control, the improving Levenberg-Marquardt learning algorithm is discussed in order to improve the learning speed and to satisfy the real control. The neural network decoupling real control based on C-Script language and PLC S7-400 hard system under WINCC is realized with satisfying control results.

Solution to the Forward and Inverse Kinematic Equations Using Multilayer Back-Propagation Neural Network

1994 ◽  
Author(s):  
Phani K. Nagarjuna ◽  
Athamaram H. Soni
Keyword(s):  
Neural Network ◽  
Degrees Of Freedom ◽  
Robot Manipulator ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Inverse Kinematic ◽  
Robot Arm ◽  
Kinematic Equation ◽  
Neural Network Approach ◽  
The Neural Network

Abstract The problem of inverse kinematics in Robotics, is a nonlinear mapping from a given cartesian coordinates to the desirable joint coordinates of the robot arm. It is found that an appropriately designed neural network can be trained to learn the non-linearity of the Inverse Kinematic Equation (IKE). We present an approach for solving the Forward Kinematic Equation (FKE) and the IKE by means of a Multi Layer Back-Propagation Neural Network (Rumelhart et al., 1986). The neural network approach is applied to a Two Degrees-of-Freedom (DOF) robot manipulator and the results are compared with those obtained using the analytical solution. The results obtained from the simulation of the neural network indicate a fairly accurate learning of the FKE and IKE by the Multi Layer Back-Propagation Neural Network.

scholarly journals A Neural Network Based Sliding Mode Control for Tracking Performance with Parameters Variation of a 3-DOF Manipulator

2019 ◽  
Vol 9 (10) ◽  
pp. 2023 ◽  
Author(s):  
Hoai-Vu-Anh Truong ◽  
Duc-Thien Tran ◽  
Kyoung Kwan Ahn
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Degrees Of Freedom ◽  
Sliding Mode ◽  
External Disturbance ◽  
Robotic Manipulator ◽  
Tracking Performance ◽  
Mode Control ◽  
Tracking Motion ◽  
Three Degrees Of Freedom

The manipulator, in most cases, works in unstructured and changeable conditions. With large external variations, the demand for stability and robustness must be ensured. This paper proposes a neural network sliding mode control (NNSMC) to cope with uncertainties and improve the behavior of the robotic manipulator in the presence of an external disturbance. The proposed method is applied to the three degrees of freedom (DOF) manipulator. Some comparisons between the proposed and the conventional algorithms are given in both simulation and experiments to prove that the designed control can achieve higher accuracy in tracking motion.

Decoupling Control of the Gas Collectors’ Pressure System Based on Neural Network Inverse System

2014 ◽  
Vol 530-531 ◽  
pp. 985-989
Author(s):  
Fan Wei Meng ◽  
Qing Tian ◽  
Bin Xu
Keyword(s):  
Neural Network ◽  
Composite System ◽  
Nonlinear Approximation ◽  
Original System ◽  
Inverse System ◽  
Decoupling Control ◽  
Pressure System ◽  
Approximate Inverse ◽  
The Neural Network ◽  
The Mathematical Model

For collectors’ pressure system strong interference, coupled, nonlinear, multi-parameter and other characteristics, based on the inverse system decoupling principle, the reversibility of the mathematical model of the gas collectors’ pressure system is analyzed. BP neural network which has strong nonlinear approximation ability is applied, to approximate inverse system of gas collectors’ pressure system. Neural network inverse system with the original system composes of the pseudo linear decoupling composite system. The neural network inverse decoupling control of gas collectors’ pressure system is implemented. The simulation results show that this method realizes decoupling, has a certain application.

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