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

Author(s):  
Gang Shen ◽  
Zhencai Zhu ◽  
Yu Tang ◽  
Lei Zhang ◽  
Guangda Liu ◽  
...  

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.

2011 ◽  
Vol 18 (10) ◽  
pp. 1474-1493 ◽  
Author(s):  
Gang Shen ◽  
Guang-ming LV ◽  
Zheng-mao Ye ◽  
Da-cheng Cong ◽  
Jun-wei Han

In this paper, an improved feed-forward inverse control scheme is proposed for transient waveform replication (TWR) on an electro-hydraulic shaking table (EHST). TWR is to determine whether a test article can remain operational and retain its structural integrity when subjected to a specific shock and vibration environment. Feed-forward inverse transfer function compensation is a useful technique to improve the tracking accuracy of the TWR on the EHST system due to their inherent hydraulic dynamics. Whenever a feed-forward inverse transfer function is employed, it is critical to design the identification accuracy of the inverse transfer function. A combined control strategy, which combines a feed-forward inverse transfer function compensation approach with a simple internal model control (IMC) and a real-time feedback controller, is proposed to minimize the effect of the system uncertainty and modeling error, and further to improve the tracking accuracy of the TWR. Thus, the proposed control strategy combines the merits of feed-forward inverse transfer function compensation and IMC. The procedure of the proposed control strategy is programmed in MATLAB/Simulink, and then is compiled to a real-time PC with Microsoft Visual Studio.NET for implementation. Simulation and experimental results demonstrated the viability of the proposed combined control strategy.


Robotica ◽  
2000 ◽  
Vol 18 (5) ◽  
pp. 505-512 ◽  
Author(s):  
D. T. Pham ◽  
Şahin Yildirim

This paper describes the design of an Internal Model Control (IMC) system for a planar two-degree-of-freedom robot. IMC was investigated as an alternative to the basic inverse control scheme which is difficult to implement. The proposed IMC system consisted of a forward internal neural model of the robot, a neural controller and a conventional feedback controller, all of which were realised easily. Both the neural model and the neural controller were based on recurrent networks which were trained using the backpropagation (BP) algorithm. The paper presents the results obtained with two types of recurrent networks as well as a conventional PID system.


AIChE Journal ◽  
1991 ◽  
Vol 37 (7) ◽  
pp. 1065-1081 ◽  
Author(s):  
Michael A. Henson ◽  
Dale E. Seborg

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