Cerebellar Model Articulation Controller with introspective voting weight updates for quadrotor application
| Autor: | Chris J. B. Macnab, Troy Clark |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science Feedback control media_common.quotation_subject 02 engineering and technology 020901 industrial engineering & automation Cerebellar model articulation controller Robustness (computer science) Control theory Voting 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Robust control Simulation media_common |
| Zdroj: | 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). |
| Popis: | This paper explores control of a four rotor aerial vehicle, or quadrotor. The quadrotor exhibits under-damped and under-actuated dynamics, so designing a feedback control that can provide robustness to disturbances such as wind and adapt to payloads provides a research challenge. The Cerebellar Model Articulation Controller (CMAC) is a type of neural network that can provide the basis for a stable adaptive neurocontrol. However, traditional robust weight update schemes for the CMAC must often sacrifice performance to eliminate bursting caused by weight drift. This work applies an introspective voting weight update scheme to a CMAC to avoid busting, without reducing performance. The proposed scheme achieves stable, adaptive, and robust control in both simulation and experiment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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