scholarly journals Using a Neural Network to Minimize Pressure Spikes for Binary-coded Digital Flow Control Units

2020 ◽  
Author(s):  
Essam Elsaed ◽  
Mohamed Abdelaziz ◽  
Nabil A. Mahmoud
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Flow Control ◽  
Control Unit ◽  
Pulse Number ◽  
Tracking Accuracy ◽  
Neural Network Controller ◽  
Control Units ◽  
Unique Method ◽  
Fluid Power Systems

A unique method of improving energy efficiency in fluid power systems is called digital flow control. In this paper, binary coding control is utilized. Although this scheme is characterized by a small package size and low energy consumption, it is influenced by higher pressure peaks and larger transient uncertainty than are other coding schemes, e.g., Fibonacci coding and pulse number modulation, consequently resulting in poor tracking accuracy. This issue can be solved by introducing a delay in the signal opening/ closing of the previous or subsequent valve, thus providing sufficient time for state alteration and valve processes. In a metering-in velocity control circuit, a feedforward neural network controller was used to create artificial delays according to the pressure difference over the digital flow control unit (DFCU) valves. The delayed signal samples fed to the controller were acquired through the genetic algorithm method, and the analysis was performed with MATLAB software.

scholarly journals High Precision Adaptive Robust Neural Network Control of a Servo Pneumatic System

2019 ◽  
Vol 9 (17) ◽  
pp. 3472 ◽  
Author(s):  
Chen ◽  
Tao ◽  
Liu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Robust Control ◽  
Network Control ◽  
Parametric Estimation ◽  
Pneumatic Cylinder ◽  
Unmodeled Dynamics ◽  
Neural Network Control ◽  
Tracking Accuracy ◽  
Neural Network Controller

In this paper, an adaptive robust neural network controller (ARNNC) is synthesized for a single-rod pneumatic actuator to achieve high tracking accuracy without knowing the bounds of the parameters and disturbances. The ARNNC control framework integrates adaptive control, robust control, and neural network control intelligently. Adaptive control improves the precision of dynamic compensation with parametric estimation, and robust control attenuates the effect of unmodeled dynamics and unknown disturbances. In reality, the unmodeled dynamics of the complicated pneumatic systems and unpredictable disturbances in working conditions affect the tracking precision. However, these cannot be expressed as an exact formula. Therefore, the real-time learning radial basis function (RBF) neural network component is considered for better compensation of unmodeled dynamics, random disturbances, and estimation errors of the adaptive control. Although the bounds of the parameters and disturbances for the pneumatic systems are unknown, the prescribed transient performance and final tracking accuracy of the proposed method can be still achieved with fictitious bounds. Asymptotic tracking performance can be acquired under the provided circumstance. The comparative experiments with a pneumatic cylinder driven by proportional direction valve illustrate the effectiveness of the proposed ARNNC as shown by a high tracking accuracy is achieved.

A Self-Constructing Wavelet Neural Network Controller to Mitigate the Subsynchronous Oscillations

2012 ◽  
Vol 135 (2) ◽  
Author(s):  
Mohsen Farahani ◽  
Soheil Ganjefar
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Adaptive Learning ◽  
Control Process ◽  
Wavelet Neural Network ◽  
Intelligent Control System ◽  
Intelligent Technique ◽  
Neural Network Controller ◽  
Learning Rates ◽  
On Line

This study proposes a new intelligent controller based on self-constructing wavelet neural network (SCWNN) to suppress the subsynchronous resonance (SSR) in power systems compensated by series capacitors. In power systems, the use of intelligent technique is inevitable, because of the uncertainties such as operating condition variations, different kinds of disturbances, etc. Accordingly, an intelligent control system that is an on-line trained SCWNN controller with adaptive learning rates is used to mitigate the SSR. The Lyapunov stability method is used to extract the adaptive learning rates. Hence, the convergence of the proposed controller can be guaranteed. At first, there is no wavelet in the structure of controller. They are automatically generated and begin to grow during the control process. In the whole design process, the identification of the controlled plant dynamic is not necessary according to the ability of the proposed controller. The effectiveness and robustness of the proposed controller are demonstrated by using the simulation results.

Some Applications of Neural Network Technology to Fluid Power Systems

1997 ◽  
Author(s):  
R. T. Burton ◽  
P. R. Ukrainetz ◽  
G. J. Schoenau ◽  
C. M. Sargent ◽  
X. P. Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Fluid Power ◽  
Network Technology ◽  
Fluid Power Systems ◽  
Neural Network Technology

scholarly journals Design of an IMCPID Optimized Neural Network for Stepless Flow Control of Reciprocating Mechinery

2021 ◽  
Vol 11 (17) ◽  
pp. 7785
Author(s):  
Huaibin Hong ◽  
Zhinong Jiang ◽  
Wensheng Ma ◽  
Wei Xiong ◽  
Jinjie Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Flow Control ◽  
Control Method ◽  
Mimo System ◽  
Methodological Approach ◽  
Internal Model Control ◽  
Tracking Accuracy ◽  
Point Tracking ◽  
Model Control ◽  
Multiple Input

It is usually difficult to design a controller for a nonlinear multiple-input and multiple-output (MIMO) system. The methodological approach taken in this study is a mixed methodology based on a PID-type internal model control (IMC) method and neural network (NN) optimization algorithm. The NN controller is designed for adjusting the sole parameter in IMCPID and compensating the characteristic changes and non-linearity in stepless flow control. In this study, a simulation of a nonlinear MIMO system with strong coupling is carried out. The simulation results indicate that the proposed control method has a better performance in settle time, overshoot, robustness and set-point tracking accuracy compared with other considered methods.

Sign in / Sign up

Export Citation Format

Share Document