scholarly journals The neural network-based control system of direct current motor driver

2019 ◽  
Vol 9 (2) ◽  
pp. 1445 ◽  
Author(s):  
Trong-Thang Nguyen
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Direct Current ◽  
Response Speed ◽  
Complex Model ◽  
Network Control ◽  
Adaptive Control System ◽  
Direct Current Motor ◽  
The Neural Network

<span lang="EN-US">This article aims to propose an adaptive control system for the direct current motor driver based on the neural network. The control system consists of two neural networks: the first neural network is used to estimate the speed of the direct current motor and the second neural network is used as a controller. The plant in this research includes motor and the driver circuit so it is a complex model. It is difficult to determine the exact parameters of the plant so it is difficult to build the controller. To solve the above difficulties, the author proposes an adaptive control system based on the neural network to control the plant reach the high quality in the case of unknowing the parameters of the plant. The results are that the control quality of the system is very good, the response speed always follows the desired speed and the transition time is small. The simulation results of the neural network control system are shown and compared with that of a PID controller to demonstrate the advantages of the proposed method.</span>

Research of Model-Free Adaptive Control System Based on Neural Network

2014 ◽  
Vol 608-609 ◽  
pp. 484-488
Author(s):  
Ze Min Liu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Nonlinear Problems ◽  
Adaptive Control System ◽  
Industrial Control ◽  
Bp Network ◽  
Model Free ◽  
The Neural Network ◽  
Positive Effect

With the development of industry, the control system is more and more complex. For the nonlinear problems which can’t be solved by the traditional linear control system used now, it uses the model-free adaptive control system based on the neural network to effectively solve them. In this paper, it firstly makes a detailed analysis on the neural network, describing the neuron, the BP network and the training of neural network; then talks about the model-free adaptive control system, analyzing the structure, characteristics and algorithm of the system; and finally gives the core code of the model-free adaptive control system of the neural network. This paper provides positive effect to the industrial control staff and artificial intelligence researchers.

scholarly journals Design and Speed-Adaptive Control of a Powered Geared Five-Bar Prosthetic Knee Using BP Neural Network Gait Recognition

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4662 ◽  
Author(s):  
Yuanxi Sun ◽  
Rui Huang ◽  
Jia Zheng ◽  
Dianbiao Dong ◽  
Xiaohong Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Bp Neural Network ◽  
Network Control ◽  
Attitude Measurement ◽  
Neural Network Control ◽  
Variable Speed ◽  
Prosthetic Knee ◽  
The Neural Network

To improve the multi-speed adaptability of the powered prosthetic knee, this paper presented a speed-adaptive neural network control based on a powered geared five-bar (GFB) prosthetic knee. The GFB prosthetic knee is actuated via a cylindrical cam-based nonlinear series elastic actuator that can provide the desired actuation for level-ground walking, and its attitude measurement is realized by two inertial sensors and one load cell on the prosthetic knee. To improve the performance of the control system, the motor control and the attitude measurement of the GFB prosthetic knee are run in parallel. The BP neural network uses input data from only the GFB prosthetic knee, and is trained by natural and artificially modified various gait patterns of different able-bodied subjects. To realize the speed-adaptive control, the prosthetic knee speed and gait cycle percentage are identified by the Gaussian mixture model-based gait classifier. Specific knee motion control instructions are generated by matching the neural network predicted gait percentage with the ideal walking gait. Habitual and variable speed level-ground walking experiments are conducted via an able-bodied subject, and the experimental results show that the neural network control system can handle both self-selected walking and variable speed walking with high adaptability.

Study on Intelligent Self-Adaptive Control System

2014 ◽  
Vol 539 ◽  
pp. 620-624
Author(s):  
Ze Min Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Control System ◽  
Pid Controller ◽  
Closed Loop ◽  
Rapid Development ◽  
Adaptive Control System ◽  
Positive Role ◽  
The Neural Network ◽  
Control Of Parameters

With the rapid development of China's industry, the use of the control system has become more and more extensive. However, with the complicating of the production system, the traditional control system has been unable to meet the needs of the current industry. Effectively bring the genetic algorithm of the neural network into the control system can solve this problem. Here, it firstly describes the neural network, genetic algorithm principle, operation procedures and the characteristics; secondly, analyzes the principle and lack of conventional PID controller; finally, effectively combines genetic algorithm and controller together, forming a closed loop, strengthening the control of parameters, and giving a code description of the genetic algorithm. This paper plays a certain positive role for industrial engineers and programmers.

scholarly journals DEVELOPMENT AND RESEARCH OF A NEURAL NETWORK AUTOTUNING UNIT FOR A PID CONTROLLER OF THE ENERGY FACILITIES AUTOMATED PROCESS CONTROL SYSTEM

2021 ◽  
pp. 71-83
Author(s):  
I.A. Shcherbatov ◽  
◽  
V.A. Artushin ◽  
A.N. Dolgushev ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Process Control ◽  
Pid Controller ◽  
Process Control System ◽  
Adaptive Control System ◽  
Automated Process Control System ◽  
Automated Process

An adaptive control system based on a neural network autotuning unit has been developed. A method for training a neural network for an autotuning block has been examined. A comparison between a control system with a PID-controller and a control system with a PID controller and an autotuning unit has been made.

scholarly journals The Neural Network-Combined Optimal Control System of Induction Motor

2019 ◽  
Vol 9 (4) ◽  
pp. 2513
Author(s):  
Thang Nguyen Trong
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Control System ◽  
Induction Motor ◽  
Control Method ◽  
Response Speed ◽  
The State ◽  
Linear Quadratic ◽  
Neuron Network ◽  
The Neural Network

<span lang="EN-US">This research aims to propose the optimal control method combined with the neuron network for an induction motor. In the proposed system, the induction motor is a nonlinear object which is controlled at each working point. At these working-points, the state equation of the induction motor is linear, so it is possible to apply the linear quadratic regular algorithm for the induction motor. Therefore, the parameters of the state feedback controller are the functions. The output-input relationships of these functions are set through the neural network. The numerical simulation results show that the quality of the control system of the induction motor is very high: The response speed always follows the desired speed with the short transition time and the small overshoot. Furthermore, the system is robust in the case of changing the load torque, and the parameters of the induction motor are incorrectly defined</span>

Hierarchical Adaptive Control System of a Manipulator Based on the Synthesis of a Neural Network of Fuzzy Inference and an Iterative Refinement Algorithm

2019 ◽  
pp. 18-31
Author(s):  
P.E. Ganin ◽  
A.I. Kobrin
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Fuzzy Inference ◽  
Experimental Studies ◽  
Initial Approximation ◽  
Iterative Refinement ◽  
Adaptive Control System ◽  
Inference System ◽  
Industrial Manipulator

The main aim of this work is to develop an adaptive control system for a kinematically redundant multilink industrial manipulator. Proposed solution allows to construct a unified real-time control system with the ability to control the accuracy of calculations. In order to achieve the required accuracy of the calculations and the performance of the control system, we propose an algorithm that is based on the so-called hybrid method for finding the solution of the inverse kinematics (IK) problem, including the adaptive neural network and fuzzy inference system with subsequent iterative refinement of numerical solution by the Newton --- Raphson method. The influence of the training sample size on the quality of the obtained initial approximation for the neural network part of the algorithm is described in the paper. The results of experimental studies of the developed hybrid algorithm are presented in comparison with the iterative and neural network methods for three-, five- and eight-link manipulator structures. Paper presents the main steps of the control system synthesis for kinematically redundant industrial manipulator, including the description for developed algorithms for finding an IK solution of multilink structures. The structure of a multi-level hierarchical manipulator control system, based on a programmable logic controller and electric stepping motors with the possibility of integration into the production system at various levels, is presented.

Mobile Operations Performed by Mobile Manipulators on Irregular Terrain - Torque Compensation Using Neural Networks for Disturbance Torques Produced by Irregular Terrain -

1998 ◽  
Vol 10 (5) ◽  
pp. 377-386 ◽  
Author(s):  
Mamoru Minami ◽  
◽  
Masatoshi Hatano ◽  
Toshiyuki Asakura ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Adaptive Control ◽  
Control System ◽  
Horizontal Plane ◽  
Mobile Manipulator ◽  
Mobile Manipulators ◽  
Adaptive Control System ◽  
Control Performance ◽  
Irregular Terrain

In the present study, we propose a control system for mobile operations of mobile manipulators traveling on irregular terrain. Irregularities exist even in structures such as man-made floors of factories and buildings. Since the hand of a mobile manipulator is often required to operate precisely while traveling on irregular terrain and it is subject to disturbance torques caused by traveling on terrain, a method for decreasing control errors caused by disturbances due to terrain must be considered. In the present paper, an adaptive control system including a compensator that uses a neural network, i.e., a neuro adaptive control system, is proposed. In addition, we discuss the control performance of the proposed control system, and show that the control system can decrease control errors occurring on irregular terrain to the levels of errors that occur while traveling on a horizontal plane.

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