scholarly journals A New Method of Applying Data Engine Technology to Realize Neural Network Control

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 97
Author(s):  
Song Zheng ◽  
Chao Bi ◽  
Yilin Song
Keyword(s):  
Neural Network ◽  
Control System ◽  
Real Time ◽  
Control Algorithm ◽  
Network Control ◽  
Neural Network Control ◽  
Hybrid Controller ◽  
Advanced Control ◽  
Real Time Database ◽  
And Control

This paper presents a novel diagonal recurrent neural network hybrid controller based on the shared memory of real-time database structure. The controller uses Data Engine (DE) technology, through the establishment of a unified and standardized software architecture and real-time database in different control stations, effectively solves many problems caused by technical standard, communication protocol, and programming language in actual industrial application: the advanced control algorithm and control system co-debugging difficulties, algorithm implementation and update inefficiency, and high development and operation and maintenance costs effectively fill the current technical gap. More importantly, the control algorithm development uses a unified visual graphics configuration programming environment, effectively solving the problem of integrated control of heterogeneous devices; and has the advantages of intuitive configuration and transparent data processing process, reducing the difficulty of the advanced control algorithms debugging in engineering applications. In this paper, the application of a neural network hybrid controller based on DE in motor speed measurement and control system shows that the system has excellent control characteristics and anti-disturbance ability, and provides an integrated method for neural network control algorithm in a practical industrial control system, which is the major contribution of this article.

Promising designs multipurpose cultivator to slopes with automatic corrected biometric control system lateral stability

2015 ◽  
Vol 5 (2) ◽  
pp. 158-165
Author(s):  
Туровский ◽  
Yaroslav Turovskiy ◽  
Кургалин ◽  
Sergey Kurgalin ◽  
Лысыч ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Control System ◽  
Real Time ◽  
Network Control ◽  
Lateral Stability ◽  
Neural Network Control ◽  
Network Control System ◽  
Unit Operator

The authors propose the concept of a control system based on the creation of avated biometric corrected neural network control system implemented on the basis tillage machines and tractors unit capable to function effectively within the system.For this approach, it is planned to develop a software system that provides for tracking the behavior of machine and tractor unit operator in real time. The resulting data will be fed to the input of artificial neural networks of different topologies.

The Electro-Hydraulic Proportional Control and Simulation of the Radial Piston Pump Based on Proportional-Integral-Derivative Neural Network

2010 ◽  
Vol 139-141 ◽  
pp. 1749-1752
Author(s):  
Lan Li ◽  
Jiang Ye ◽  
Xue Fei Zheng
Keyword(s):  
Neural Network ◽  
Control System ◽  
Pid Control ◽  
Control Algorithm ◽  
Network Control ◽  
Piston Pump ◽  
System Response ◽  
Neural Network Control ◽  
Proportional Control ◽  
Pid Neural Network

In this paper a new control method has been studied in which PID control system was integrated into the neural network. It could overcome some disadvantages such as neural network’s slow rate of convergence and PID’s difficulty in application of multivariate nonlinear systems. A controller of the Electro-hydraulic proportional control stroking mechanism for radial piston pump was designed based on the PID neural network control algorithm. The system responses of system variable control signal of system track were achieved by computer simulation. It was found by PIDNN that the control system could reach steady state in a shorter time, compared with PID control system response time by 65% to 80%. The simulation results showed that the controller for the Electro-hydraulic proportional Radial Piston Pump based PID neural network control algorithm would have a good controlling performance.

scholarly journals Adaptive Neural Network Control of Zero-Speed Vessel Fin Stabilizer Based on Command Filter

2022 ◽  
Vol 12 (2) ◽  
pp. 754
Author(s):  
Ziteng Sun ◽  
Chao Chen ◽  
Guibing Zhu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Method ◽  
Large Angle ◽  
Network Control ◽  
Neural Network Control ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Command Filter ◽  
Fin Stabilizer

This paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy based on a command filter for the problem of large-angle rolling motion caused by adverse sea conditions when a vessel is at low speed down to zero. In order to avoid the adverse effects of the high-frequency part of the marine environment on the vessel rolling control system, a command filter is introduced in the design of the controller and a command filter backstepping control method is designed. An auxiliary dynamic system (ADS) is constructed to correct the feedback error caused by input saturation. Considering that the system has unknown internal parameters and unmodeled dynamics, and is affected by unknown disturbances from the outside, the neural network technology and nonlinear disturbance observer are fused in the proposed design, which not only combines the advantages of the two but also overcomes the limitations of the single technique itself. Through Lyapunov theoretical analysis, the stability of the control system is proved. Finally, the simulation results also verify the effectiveness of the control method.

Backstepping sliding mode neural network control system for hypersonic vehicle

2019 ◽  
Vol 27 (11) ◽  
pp. 2392-2401
Author(s):  
刘 蓉 LIU Rong ◽  
黄大庆 HUANG Da-qing ◽  
姜定国 JIANG Ding-guo
Keyword(s):  
Neural Network ◽  
Control System ◽  
Sliding Mode ◽  
Hypersonic Vehicle ◽  
Network Control ◽  
Neural Network Control ◽  
Network Control System
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