An Output Based Adaptive Iterative Learning Control with Particle Swarm Optimization for Robotic Systems

Ying Chung Wang; Chiang Ju Chien; Chi Nan Chuang

doi:10.4028/www.scientific.net/AMM.479-480.737

Paper Titles

The Design of Multi-Parameter Bio-Signal Sensor for Applying a Smartphone m-Health Service
p.713

Interpretation of the Oscillating Signals of the Smart NO_x Sensors Used in Urea Selective Catalyst Reduction Systems via Spectral Analysis
p.719

Design of Photo Diode Sensors for Measuring Solar Light Orientation
p.724

Singularity Avoidance for a Redundant Robot Using Fuzzy Motion Planning
p.729

An Output Based Adaptive Iterative Learning Control with Particle Swarm Optimization for Robotic Systems
p.737

Distributed Control Intelligent Robotic Gripper
p.742

A Laboratory-Based Smart Grid Sensor Network Testbed
p.747

Discoid and Asymmetrical Micro-Satellite Propulsion Mode Attitude Control with Great Mass Change and without Angular Rate Sensor
p.753

A Fuzzy-Based Management for Power-Aware Wireless Sensor Network
p.758

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 479-480An Output Based Adaptive Iterative Learning...

An Output Based Adaptive Iterative Learning Control with Particle Swarm Optimization for Robotic Systems

Abstract:

We consider an output based adaptive iterative learning control (AILC) for robotic systems with repetitive tasks in this paper. Since the joint velocities are not measurable, a sliding window of measurements and an averaging filter approach are used to design the AILC. Besides, the particle swarm optimization (PSO) is used to adjust the learning gains in the learning process to improve the learning performance. Finally, a Lyapunov like analysis is applied to show that the norm of output tracking error will asymptotically converge to a tunable residual set as iteration goes to infinity.

You might also be interested in these eBooks

Applied Science and Precision Engineering Innovation

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 479-480)

Pages:

737-741

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.479-480.737

Citation:

Cite this paper

Online since:

December 2013

Authors:

Ying Chung Wang, Chiang Ju Chien, Chi Nan Chuang

Keywords:

AILC, PSO Algorithm, Robotic Systems, Sliding Window of Measurements

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] T.J. Jang, C.H. Choi and H.S. Ahn, Iterative learning control in feedback systems, Automatica, Vol. 31 (1995), No. 2, pp.243-248.

DOI: 10.1016/0005-1098(94)00064-p

Google Scholar

[2] B.H. Park, T.Y. Kuc and J.S. Lee, Adaptive learning of uncertain robotic systems, Int. J. Contr., Vol. 65 (1996), No. 5, pp.725-744.

Google Scholar

[3] A. I. Selvakumar and K. Thanushkodi, A new particle swarm optimization solution to nonconvex economic dispatch problems, IEEE Trans. Power Systems, Vol. 22 (2007), No. 1, pp.42-51.

DOI: 10.1109/tpwrs.2006.889132

Google Scholar

[4] Anthony J. Calise, Naira Hovakimyan and Moshe Idan, Adaptive output feedback control of nonlinear systems using neural networks, Automatica, Vol. 37 (2001), p.1201–1211.

DOI: 10.1016/s0005-1098(01)00070-x

Google Scholar

[5] Y. C. Wang, C. J. Chien, and D. T. Lee, A hybrid adaptive scheme of fuzzy-neural iterative learning controller for nonlinear dynamic systems, International Journal of Fuzzy Systems, Vol. 7 (2005), No. 4, pp.147-157.

Google Scholar

[6] Y. -C. Wang and C. -J. Chien. An observer based adaptive iterative learning control for robotic systems,. 2011 IEEE International Conference on Fuzzy Systems, (2011).

DOI: 10.1109/fuzzy.2011.6007425

Google Scholar