Real-time simulation based robust adaptive control of hydraulic servo system

2011 ◽  
Author(s):  
Hamid Roozbahani ◽  
Huapeng Wu ◽  
Heikki Handroos
Keyword(s):  
Adaptive Control ◽  
Real Time ◽  
Servo System ◽  
Robust Adaptive Control ◽  
Real Time Simulation ◽  
Time Simulation ◽  
Simulation Based ◽  
Hydraulic Servo ◽  
Hydraulic Servo System ◽  
Robust Adaptive

Real-time, Simulation-based Identification of Cyber-Security Attacks of Industrial Plants

2021 ◽  
Author(s):  
Abhi Patel ◽  
Tim Schenk ◽  
Steffi Knorn ◽  
Heiko Patzlaff ◽  
Dragan Obradovic ◽  
...  
Keyword(s):  
Real Time ◽  
Cyber Security ◽  
Security Attacks ◽  
Industrial Plants ◽  
Real Time Simulation ◽  
Time Simulation ◽  
Simulation Based

scholarly journals Real-Time Simulation-Based Testing of Modern Energy Systems: A Review and Discussion

2020 ◽  
Vol 14 (2) ◽  
pp. 28-39 ◽  
Author(s):  
Andrea Benigni ◽  
Thomas Strasser ◽  
Giovanni De Carne ◽  
Marco Liserre ◽  
Marco Cupelli ◽  
...  
Keyword(s):  
Real Time ◽  
Energy Systems ◽  
Real Time Simulation ◽  
Time Simulation ◽  
Simulation Based

scholarly journals Research on Performance of Real-Time Simulation Based on Inverter-Dominated Power Grid

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 1137-1153
Author(s):  
Xinya Song ◽  
Hui Cai ◽  
Teng Jiang ◽  
Tom Sennewald ◽  
Jan Kircheis ◽  
...  
Keyword(s):  
Real Time ◽  
Power Grid ◽  
Real Time Simulation ◽  
Time Simulation ◽  
Simulation Based

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.

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