Implementation of electrohydraulic shaking table controllers with a combined adaptive inverse control and minimal control synthesis algorithm

2011 ◽  
Vol 5 (13) ◽  
pp. 1471-1483 ◽  
Author(s):  
G. Shen ◽  
J.-W. Han ◽  
Z.-M. Ye ◽  
D.-C. Cong ◽  
G.-M. Lv
Keyword(s):  
Shaking Table ◽  
Control Synthesis ◽  
Adaptive Inverse Control ◽  
Synthesis Algorithm ◽  
Inverse Control

Adaptive inverse control of time waveform replication for electrohydraulic shaking table

2010 ◽  
Vol 17 (11) ◽  
pp. 1611-1633 ◽  
Author(s):  
Gang Shen ◽  
Shu-Tao Zheng ◽  
Zheng-Mao Ye ◽  
Qi-Tao Huang ◽  
Da-Cheng Cong ◽  
...  
Keyword(s):  
Shaking Table ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
Time Waveform

Combined control strategy using internal model control and adaptive inverse control for electro-hydraulic shaking table

2012 ◽  
Vol 227 (10) ◽  
pp. 2348-2360 ◽  
Author(s):  
Gang Shen ◽  
Zhencai Zhu ◽  
Yu Tang ◽  
Lei Zhang ◽  
Guangda Liu ◽  
...  
Keyword(s):  
Transfer Function ◽  
Control Strategy ◽  
Internal Model ◽  
Internal Model Control ◽  
Shaking Table ◽  
Tracking Accuracy ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
Model Control ◽  
Combined Control

An electro-hydraulic shaking table is a useful experimental apparatus to real-time replicate the desired acceleration signal for evaluating the performance of the tested structural systems. The article proposes a combined control strategy to improve the tracking accuracy of the electro-hydraulic shaking table. First, the combined control strategy utilizes an adaptive inverse control as a feedforward controller for extending the acceleration frequency bandwidth of the electro-hydraulic shaking table when the estimated plant model may be a nonminimum phase system and its inverse model is an unstable system. The adaptive inverse control feedforward compensator guarantees the stability of the estimated inverse transfer function. Then, the combined control strategy employs an improved internal model control for obtaining high fidelity tracking accuracy after the modeling error between the estimated inverse transfer function using adaptive inverse control and the electro-hydraulic shaking table actual inverse system is improved by the improved internal model control. So, the proposed control strategy combines the merits of adaptive inverse control feedforward compensator and improved internal model control. The combined strategy is programmed in MATLAB/Simulink, and then is compiled to a real-time PC system with xPC target technology for implementation. The experimental results demonstrate that a better tracking performance with the proposed combined control strategy is achieved in an electro-hydraulic shaking table than with a conventional controller.

A new Technique for Position Control of Induction Motor Using Adaptive Inverse Control

2010 ◽  
Vol 6 (2) ◽  
pp. 116-122
Author(s):  
Aamir Hashim Obeid Ahmed ◽  
Martino O. Ajangnay ◽  
Shamboul A. Mohamed ◽  
Matthew W. Dunnigan
Keyword(s):  
Induction Motor ◽  
Position Control ◽  
New Technique ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
A New Technique

scholarly journals A New Solution to Adaptive Inverse Control

1991 ◽  
Vol 24 (1) ◽  
pp. 35-40
Author(s):  
A. Farhang-Boroujeny ◽  
K. Ayatollahi
Keyword(s):  
Adaptive Inverse Control ◽  
Inverse Control

Research of the 3-DOF Helicopter System Based on Adaptive Inverse Control

2013 ◽  
Vol 389 ◽  
pp. 623-631 ◽  
Author(s):  
Xiu Yan Wang ◽  
Ying Wang ◽  
Zong Shuai Li
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Fuzzy Inference ◽  
Reference Model ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Actual System ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
Network Connection

For the flight control problem occurred in 3-DOF Helicopter System, reference adaptive inverse control scheme based on Fuzzy Neural Network model is designed. Firstly, fuzzy inference process of identifier and controller is achieved by using the network structure. Meanwhile, the neural network connection weights are used to express parameters of fuzzy inference. Then, back-propagation algorithm is adopted to amend the network connection weights in order to automatically identify the fuzzy model and adjust its membership functions and parameters, so that the actual system output of adaptive inverse controller control which is adjusted can track the reference model output. Finally, the simulation result of 3-DOF Helicopter System based on the scheme shows that the method is effective and feasible.

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