A New Adaptive Intelligent Control Method Based On Neural Networks

2005 ◽  
Author(s):  
Ming-wang Zhoo
Keyword(s):  
Neural Networks ◽  
Intelligent Control ◽  
Control Method

Study on the Intelligent Control of Springback in Stretch Bending Process Based on Neural Networks

2006 ◽  
Vol 532-533 ◽  
pp. 604-607
Author(s):  
Yong Jun Wang ◽  
Jun Biao Wang ◽  
Sheng Min Wei ◽  
Jiang Jun Jiang
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Friction Coefficient ◽  
Intelligent Control ◽  
Process Parameters ◽  
Control Method ◽  
Friction Condition ◽  
Whole Process ◽  
Stretch Bending ◽  
Bending Process

In extrusion stretch bending process, there are many factors which affect springback of the workpiece such as mechanical properties of the material, friction condition and process parameters. The springback of same batch of extrusion is different at same forming parameters because of the variation of the mechanical properties of the material and the friction condition. A method of intelligent control of springback in stretch bending process is proposed by using ANN(artificial neural networks). The online identification model of the mechanical properties of the material and friction coefficient and the online prediction control model of springback of workpiece in stretch bending process are established by using ANN ,which are trained by the data of analysis calculation. It realizes the intelligent control on springback of stretch bending to online identify the material properties and friction coefficient and predict springback and adjust process parameters dynamically through the whole process of stretch bending. The results from the experiment state that the intelligent control method can suit the variation of mechanical properties of material and friction condition and improve the geometry precision.

scholarly journals Task-space regulation of rigid-link electrically-driven robots with uncertain kinematics using neural networks

2021 ◽  
Vol 54 (1-2) ◽  
pp. 102-115
Author(s):  
Wenhui Si ◽  
Lingyan Zhao ◽  
Jianping Wei ◽  
Zhiguang Guan
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Control Method ◽  
Joint Space ◽  
Robot Kinematics ◽  
Rbf Neural Networks ◽  
Task Space ◽  
Actuator Dynamics ◽  
Rigid Link ◽  
On Line

Extensive research efforts have been made to address the motion control of rigid-link electrically-driven (RLED) robots in literature. However, most existing results were designed in joint space and need to be converted to task space as more and more control tasks are defined in their operational space. In this work, the direct task-space regulation of RLED robots with uncertain kinematics is studied by using neural networks (NN) technique. Radial basis function (RBF) neural networks are used to estimate complicated and calibration heavy robot kinematics and dynamics. The NN weights are updated on-line through two adaptation laws without the necessity of off-line training. Compared with most existing NN-based robot control results, the novelty of the proposed method lies in that asymptotic stability of the overall system can be achieved instead of just uniformly ultimately bounded (UUB) stability. Moreover, the proposed control method can tolerate not only the actuator dynamics uncertainty but also the uncertainty in robot kinematics by adopting an adaptive Jacobian matrix. The asymptotic stability of the overall system is proven rigorously through Lyapunov analysis. Numerical studies have been carried out to verify efficiency of the proposed method.

Decoupling Control for Bearingless Synchronous Reluctance Motor Based on Neural Networks Inverse

2012 ◽  
Vol 150 ◽  
pp. 30-35
Author(s):  
Ze Bin Yang ◽  
Huang Qiu Zhu ◽  
Xiao Dong Sun ◽  
Tao Zhang
Keyword(s):  
Neural Networks ◽  
Control Method ◽  
Decoupling Control ◽  
Performance Simulation ◽  
Inverse Control ◽  
Linear Control Theory ◽  
Synchronous Reluctance Motor ◽  
Reluctance Motor ◽  
Static Performance ◽  
Simulation And Experiment

A novel decoupling control method based on neural networks inverse system is presented in this paper for a bearingless synchronous reluctance motor (BSRM) possessing the characteristics of multi-input-multi-output, nonlinearity, and strong coupling. The dynamic mathematical models are built, which are verified to be invertible. A controller based on neural network inverse is designed, which decouples the original nonlinear system to two linear position subsystems and an angular velocity subsystem. Furthermore, the linear control theory is applied to closed-loop synthesis to meet the desired performance. Simulation and experiment results show that the presented neural networks inverse control strategy can realize the dynamic decoupling of BSRM, and that the control system has fine dynamic and static performance.

Robust Control Method Based on Machine Learning for Boiler Combustion System

2014 ◽  
Vol 685 ◽  
pp. 368-372 ◽  
Author(s):  
Hao Zhang ◽  
Ya Jie Zhang ◽  
Yan Gu Zhang
Keyword(s):  
Robust Control ◽  
Intelligent Control ◽  
Pid Control ◽  
Control Algorithm ◽  
Control Method ◽  
Support Vector ◽  
Control Performance ◽  
Fuzzy Pid Control ◽  
On Line ◽  
Control Output

In this study, we presented a boiler combustion robust control method under load changes based on the least squares support vector machine, PID parameters are on-line adjusted and identified by LSSVM, optimum control output is obtained. The simulation result shows control performance of the intelligent control algorithm is superior to traditional control algorithm and fuzzy PID control algorithm, the study provides a new control method for strong non-linear boiler combustion control system.

MODELING OF NEURAL PREDICTIVE CONTROL OF IRRIGATION MACHINES

2021 ◽  
pp. 14-22
Author(s):  
G. N. KAMYSHOVA ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Predictive Control ◽  
Intelligent Control ◽  
Machine Learning Algorithms ◽  
Intelligent Control Systems ◽  
Artificial Neural ◽  
The One ◽  
Neural Predictive Control

The purpose of the study is to develop new scientific approaches to improve the efficiency of irrigation machines. Modern digital technologies allow the collection of data, their analysis and operational management of equipment and technological processes, often in real time. All this allows, on the one hand, applying new approaches to modeling technical systems and processes (the so-called “data-driven models”), on the other hand, it requires the development of fundamentally new models, which will be based on the methods of artificial intelligence (artificial neural networks, fuzzy logic, machine learning algorithms and etc.).The analysis of the tracks and the actual speeds of the irrigation machines in real time showed their significant deviations in the range from the specified speed, which leads to a deterioration in the irrigation parameters. We have developed an irrigation machine’s control model based on predictive control approaches and the theory of artificial neural networks. Application of the model makes it possible to implement control algorithms with predicting the response of the irrigation machine to the control signal. A diagram of an algorithm for constructing predictive control, a structure of a neuroregulator and tools for its synthesis using modern software are proposed. The versatility of the model makes it possible to use it both to improve the efficiency of management of existing irrigation machines and to develop new ones with integrated intelligent control systems.

Combination of Neural Networks and State Vector Feedback Adaptive Control (SVFAC) Technique to Control the Gas Turbine Combustor

2006 ◽  
Author(s):  
Mahmood Lahroodi ◽  
A. A. Mozafari
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Control System ◽  
Gas Turbine ◽  
Control Method ◽  
Control Performance ◽  
Gas Turbine Combustor ◽  
Model Network ◽  
Neural Network Controller ◽  
Nonlinear Plants

Neural networks have been applied very successfully in the identification and control of dynamic systems. When designing a control system to ensure the safe and automatic operation of the gas turbine combustor, it is necessary to be able to predict temperature and pressure levels and outlet flow rate throughout the gas turbine combustor to use them for selection of control parameters. This paper describes a nonlinear SVFAC controller scheme for gas turbine combustor. In order to achieve the satisfied control performance, we have to consider the affection of nonlinear factors contained in controller. The neural network controller learns to produce the input selected by the optimization process. The controller is adaptively trained to force the plant output to track a reference output. Proposed Adaptive control system configuration uses two neural networks: a controller network and a model network. The model network is used to predict the effect of controller changes on plant output, which allows the updating of controller parameters. This paper presents the new adaptive SFVC controller using neural networks with compensation for nonlinear plants. The control performance of designed controller is compared with inverse control method and results have shown that the proposed method has good performance for nonlinear plants such as gas turbine combustor.

Sign in / Sign up

Export Citation Format

Share Document