PID+CMAC Neural Network Control of Micro Positioning Platform

Xiao Jing Yang; Yao Li

doi:10.4028/www.scientific.net/AMM.713-715.768

Paper Titles

Design and Simulation of the Hydraulic Synchronous Control System of the Upper Roller in Bending Machine
p.752

Research on Flux Damping Control Strategy of DFIG during Grid Voltage Dips
p.756

The Reliability Analysis of Mechanical Leg (2UPS+UP) Based on a Discrete Time Repairable Markov Model
p.760

Research of Control System of the Large-Scale Blow Molding Machine
p.764

PID+CMAC Neural Network Control of Micro Positioning Platform
p.768

Simulation of Motion Control for a Modular Robot of Five Degrees of Freedom
p.773

A Hybrid Differential Flatness and Sliding Modes Controller for Dynamical Structural Testing on Lower Limp Prostheses
p.777

Design and Implementation of Mechatronic Prosthesis for Amputees with Trans-Humeral Amputation
p.781

Mechanical Design of a Self-Balancing Platform for Transporting Purposes
p.785

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 713-715PID+CMAC Neural Network Control of Micro...

PID+CMAC Neural Network Control of Micro Positioning Platform

Abstract:

Because of some intrinsic properties of the PZT actuator, such as hysteresis, creep and nonlinearity effect, it’s difficult to achieve satisfactory results and control precision for conventional PID controller. In order to improve the control precision of the PZT driven of micro positioning platform, we proposed a CMAC neural network based on PID control scheme, which has the characteristics of realizing feed-forward control and obtaining the inverse dynamic model of controlled object by the CMAC neural network controller, and realizing feedback control by conventional controller, which can ensure the system stability and suppress disturbance. Form MATLAB simulation results, it demonstrates that the CMAC+PID control algorithm can be able to improve the control precision and response time of the system, and enhance the anti-interference ability and robustness, comparing with the traditional digit PID control algorithms.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 713-715)

Pages:

768-772

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.713-715.768

Citation:

Cite this paper

Online since:

January 2015

Authors:

Xiao Jing Yang*, Yao Li

Keywords:

CMAC Neural Network, PID Control, PZT Actuator, Simulation

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Tingpeng Huang. Design and Development on the Piezoelectric Actuated Micro-positioning Stage[D]. Hangzhou, Mechanical and Energy Engineering, Zhejiang University. 2008: 53.

Google Scholar

[2] Qiang Wei, Yulin Zhang, Huijuan Hao. Control of STM Micro-displacement Stage Based on Genetic Neural Network[J]. MICRO FABRICATION TECHNOLOGY, 2006, 10-14.

Google Scholar

[3] Lina Xu. Neural Network Control(3rd edition)[M]. Beijing, PUBLISHING HOUSE OF ELECTRONICS INDUSTRY, 2009: 120.

Google Scholar

[4] Yunfei Hong, Fu Chen, Xingwu Fu. PID Control System Based on CMAC Neural Netwok[J]. Microcomputer Information, 2004, 20(4): 24-25.

Google Scholar