| Autor: |
Johan van Doornik, Abraham K. Ishihara, Shahar Ben-Menahem |
| Rok vydání: |
2008 |
| Předmět: |
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| Zdroj: |
IFAC Proceedings Volumes. 41:11708-11713 |
| ISSN: |
1474-6670 |
| DOI: |
10.3182/20080706-5-kr-1001.01984 |
| Popis: |
Neural network based control of a serial-link robotic manipulator is considered subject to a signal dependent noise (SDN) model corrupting the training signal. A radial basis function (RBF) network is utilized in the feedforward control to approximate the unknown inverse dynamics. The weights are adaptively adjusted according to a gradient descent plus a regulation term (Narendra's e -modification). A typical quadratic stochastic Lyapunov function is constructed which shows under certain noise models it is not necessary to employ quartic Lyapunov functions as is typically carried out in stochastic adaptive backstepping designs. Bounds on the feedback gains, and learning rate parameters are derived that guarantee the origin of the closed loop system is semi-globally, uniformly bounded in expectation (SGUBE). |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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