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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wenzhi Gao Selmic, R.R. Shengjun Su |
| Copyright Year | 2005 |
| Description | Author affiliation: Dept. of Electr. Eng., Louisiana Tech. Univ., Ruston, LA (Wenzhi Gao; Selmic, R.R.; Shengjun Su) |
| Abstract | A neural network-based saturation compensation signal is inserted into the actuator control, effectively preventing it from being saturated. The proposed neural network (NN) saturation compensation scheme presents a form of intelligent anti-windup saturation where NN adjusts its output to prevent saturation of the control signal. On-line weights tuning law, the overall closed-loop performance, and the boundedness of the NN weights are derived and guaranteed based on the Lyapunov approach. The actuator saturation is assumed to be unknown, and the compensator is inserted into a feedforward path. The simulation results indicate that the proposed scheme can effectively compensate for the saturation nonlinearity in the presence of system uncertainty |
| Starting Page | 456 |
| Ending Page | 461 |
| File Size | 422964 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780389360 |
| ISSN | 21589860 |
| DOI | 10.1109/.2005.1467058 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-06-27 |
| Publisher Place | Cyprus |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Adaptive systems Programmable control Adaptive control Neural networks Intelligent networks Motion control Control systems Nonlinear dynamical systems Intelligent actuators Uncertainty |
| Content Type | Text |
| Resource Type | Article |
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