Offline iterative control method using frequency-splitting to drive double-layer shaking tables

2021 ◽  
Vol 152 ◽  
pp. 107443
Author(s):  
Yingpeng Tian ◽  
Tao Wang ◽  
Yundong Shi ◽  
Qinghua Han ◽  
Peng Pan
Keyword(s):  
Double Layer ◽  
Control Method ◽  
Frequency Splitting ◽  
Iterative Control

Research of Multi-Movement Parameters Iterative Control of Multi-Degrees-of-Freedom Earthquake Simulation

2013 ◽  
Vol 353-356 ◽  
pp. 2008-2014
Author(s):  
Pan Tian ◽  
Zhang Wei Chen
Keyword(s):  
Degrees Of Freedom ◽  
Control Method ◽  
Synthesis Method ◽  
Low Frequency ◽  
Control Area ◽  
Synthetic Control ◽  
Earthquake Simulation ◽  
Simulation Test ◽  
Movement Parameters ◽  
Iterative Control

Acceleration iterative control is generally used in multi-degrees-of-freedom earthquake simulation test. To overcome the shortcoming of poor displacement amplitude accuracy in this control, a multi-degrees-of-freedom earthquake simulation control strategy based on multi-movement parameters iterative control is proposed. This control divide the frequency range of earthquake simulation test into displacement control area, the synthetic control area and acceleration control area from the low frequency to the high frequency, and correct the acceleration errors spectrum and displacement error spectrum by iterative control method, and produce shaker driver signal by synthesis method, and realize the displacement replication and acceleration replication at the same time. The experimental results which are got in multi-degrees-of-freedom earthquake simulation facility indicate that this control can achieve high accuracy multi-degrees-of-freedom earthquake simulation test after several times iterative.

scholarly journals Adaptive PID Control and Its Application Based on a Double-Layer BP Neural Network

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1475
Author(s):  
Ming-Li Zhang ◽  
Yi-Jie Zhang ◽  
Xiao-Long He ◽  
Zheng-Jie Gao
Keyword(s):  
Double Layer ◽  
Working Conditions ◽  
Pid Control ◽  
Control Method ◽  
Back Propagation ◽  
Engineering Application ◽  
Hydraulic Drive ◽  
Control Parameters ◽  
Hydraulic Drive Unit ◽  
The Relationship

In this paper, focusing on the inconvenience of variable value PID based on manual parameter adjustment for the hydraulic drive unit (HDU) of a legged robot, a method employing double-layer back propagation (BP) neural networks for learning the law of PID control parameters is proposed. The first layer is used to learn the relationship between different control parameters and the control performance of the system under various working conditions. The second layer is used to study the relationship between the parameters of the working conditions and the optimizing control parameters under various working conditions. The effectiveness of the proposed control method was verified by simulation and experiment. The results showed that the proposed method can provide a theoretical and experimental basis for the selection of control parameters, and can be extended to similar controllers, therefore possessing engineering application value.

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