Abstract
In this paper, a new adaptive control algorithm is presented for unknown nonlinear and non-Gaussian stochastic systems. The method combines the minimum entropy control with an Iterative Learning Control (ILC) framework, where the control horizon is divided into a number of time-domain intervals called Batches. Within each batch a PI controller is used to control the plant so as to achieve the required tracking performance, where a neural network is used to learn the dynamics of the unknown plant. Between any two adjacent batches, a D-type ILC law is applied to tune the PI control coe±cients so that the tracking error entropy for the closed loop system is reduced batch by batch. The analysis on the ILC convergence is made and a set of demonstrable experiment results on a test rig are also provided to show the effectiveness of the obtained adaptive control algorithm. Copyright © 2007 International Federation of Automatic Control All Rights Reserved.
| Original language | English |
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| Title of host publication | IFAC Proceedings Volumes (IFAC-PapersOnline)|IFAC Proc. Vol. (IFAC-PapersOnline) |
| Volume | 17 |
| DOIs | |
| Publication status | Published - 2008 |
| Event | 17th World Congress, International Federation of Automatic Control, IFAC - Seoul Duration: 1 Jul 2008 → … http://www.ifac-papersonline.net/Detailed/37247.html |
Conference
| Conference | 17th World Congress, International Federation of Automatic Control, IFAC |
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| City | Seoul |
| Period | 1/07/08 → … |
| Internet address |
Keywords
- Adaptive control
- Application of nonlinear analysis and design
- Nonlinear system control