Integrated optimization of structural topology and control for piezoelectric smart trusses using genetic algorithm

2007 ◽  
Vol 307 (3-5) ◽  
pp. 393-427 ◽  
Author(s):  
B. Xu ◽  
J.S. Jiang ◽  
J.P. Ou
Keyword(s):  
Genetic Algorithm ◽  
Structural Topology ◽  
Integrated Optimization ◽  
And Control

Integrated Optimization of Structure and Control in Ultra-Precision Motion Systems

2019 ◽  
Author(s):  
Jing Wang ◽  
Ming Zhang ◽  
Yu Zhu ◽  
Xin Li ◽  
Leijie Wang
Keyword(s):  
Complex Dynamics ◽  
System Level ◽  
Structural Topology ◽  
Worst Case ◽  
Integrated Optimization ◽  
Motion System ◽  
Material Interpolation ◽  
Ultra Precision ◽  
Precision Motion ◽  
And Control

Abstract Ever-increasing demands for precision and efficiency in ultra-precision motion systems will result in a lightweight and flexible motion system with complex dynamics. In this paper, a systematic approach is proposed where control gains, 3D structural topology and actuator configuration are integrated into optimization to derive a system-level optimal design which possesses a high vibration control performance, and still satisfies multiple design constraints. A material interpolation model with high accuracy is proposed for the integrated optimization, a simple integral equation utilizing R-functions and level-set functions is established to represent complex non-overlapping constraints of actuators. Over-actuation degrees are utilized to actively control the dominant flexible modes. Responses of residual flexible modes are restricted by increasing the coincidence of their nodal areas at actuators (sensors) locations. The objective function is the constructed worst-case vibration energy of the flexible modes. A dual-loop solving strategy combining the genetic algorithm and the modified optimal criteria method is adopted to solve the optimization problem. A fine stage in the wafer stage is designed to prove the effectiveness of the proposed method.

Integrated Design of Control/Structural Systems Based on Genetic Algorithm

2012 ◽  
Vol 204-208 ◽  
pp. 4855-4867 ◽  
Author(s):  
Bin Xu ◽  
Yi Li Zhang ◽  
Shu Xian Yao ◽  
Jie Sheng Jiang
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Integrated Design ◽  
Seismic Load ◽  
Structural Systems ◽  
Integrated Optimization ◽  
Design Variables ◽  
Single Structure ◽  
Plane Frame ◽  
And Control

The integrated optimization of structure and control systems is investigated for 2-D single structure using a typical planar frame taken from a building. The vibration control model is established for the plane frame, and the LQR algorithm is used to obtain equivalent stiffness and damping parameters of assigned passive controllers. The integrated optimization model, including design variables, the objective function and constraint function, is built. The design variables are the size parameters of structural elements, the parameters of the weighted matrix, the number and locations of the controllers. The maximal displacement of the controlled system in time domain is introduced as the objective function. Genetic algorithm is adopted to solve such a kind of optimization problem with discrete and continuous design variables. The results of a numerical example show that the proposed method is reasonable and effective for the integrated design of control/structural systems subjected to seismic load.

Hybrid genetic algorithm-based optimization of powertrain and control parameters of plug-in hybrid electric bus

2015 ◽  
Vol 352 (3) ◽  
pp. 776-801 ◽  
Author(s):  
Liang Li ◽  
Yahui Zhang ◽  
Chao Yang ◽  
Xiaohong Jiao ◽  
Lipeng Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Control Parameters ◽  
Hybrid Electric Bus ◽  
Electric Bus ◽  
Hybrid Electric ◽  
And Control

Simultaneous Design of Structural Topology and Control

2017 ◽  
pp. 308-318
Author(s):  
Zhu Ye ◽  
Jinhao Qiu ◽  
Du Hejun ◽  
Junji Tani ◽  
Masanori Murai
Keyword(s):  
Structural Topology ◽  
Simultaneous Design ◽  
And Control

scholarly journals ELMAN-RECURRENT NEURAL NETWORK FOR LOAD SHEDDING OPTIMIZATION

SINERGI ◽  
2020 ◽  
Vol 24 (1) ◽  
pp. 29
Author(s):  
Widi Aribowo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Power System ◽  
Recurrent Neural Networks ◽  
Load Shedding ◽  
Feed Forward Neural Network ◽  
Linear Problems ◽  
Elman Recurrent Neural Network ◽  
And Control

Load shedding plays a key part in the avoidance of the power system outage. The frequency and voltage fluidity leads to the spread of a power system into sub-systems and leads to the outage as well as the severe breakdown of the system utility.  In recent years, Neural networks have been very victorious in several signal processing and control applications.  Recurrent Neural networks are capable of handling complex and non-linear problems. This paper provides an algorithm for load shedding using ELMAN Recurrent Neural Networks (RNN). Elman has proposed a partially RNN, where the feedforward connections are modifiable and the recurrent connections are fixed. The research is implemented in MATLAB and the performance is tested with a 6 bus system. The results are compared with the Genetic Algorithm (GA), Combining Genetic Algorithm with Feed Forward Neural Network (hybrid) and RNN. The proposed method is capable of assigning load releases needed and more efficient than other methods. 

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