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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Pingan He Jagannathan, S. |
| Copyright Year | 2003 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Missouri Univ., Rolla, MO, USA (Pingan He; Jagannathan, S.) |
| Abstract | A novel adaptive critic-based multilayer neural network (NN) controller in discrete-time is designed to deliver a desired tracking performance for a class of nonlinear systems in the presence of magnitude constraints on the input. Reinforcement learning scheme in discrete-time is proposed for the NN controller, where the action generating NN learning is performed based on a certain performance measure, which is supplied from a critic. Using the Lyapunov approach and with a novel weight updates, the uniform ultimately boundedness (UUB) of the closed-loop tracking error and weight estimates are shown. The adaptive critic NN does not require an offline learning phase and the weights can be initialized at zero or randomly. It is shown via simulation that taking magnitude constraints on the input help reduce transients. |
| Sponsorship | Honeywell Lab. Xerox Nat. Instruments Math Works |
| Starting Page | 5709 |
| Ending Page | 5714 |
| File Size | 397994 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780379241 |
| ISSN | 01912216 |
| DOI | 10.1109/CDC.2003.1271914 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-12-09 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Adaptive systems Programmable control Adaptive control Neural networks Nonlinear control systems Control systems Nonlinear systems Multi-layer neural network Learning Performance evaluation |
| Content Type | Text |
| Resource Type | Article |
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