Abstract
This paper presents a robust Adaptive Fuzzy Neural Controller (AFNC) suitable for motion control of a multilink robot manipulator. The proposed controller has the following salient features: (1) dynamic fuzzy neural networks structure, i.e. fuzzy control rules can be generated or deleted automatically, (2) adaptive learning, (3) online learning of the robot dynamics, (4) fast learning speed, and (5) fast convergence of tracking error. Global stability of the system is established using Lyapunov approach. Computer simulation studies of a two-link robot manipulator demonstrate that excellent tracking performance can be achieved under external disturbances
Original language | English |
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Pages | 4828-4833 |
Number of pages | 5 |
Publication status | Published - 27 Jun 2001 |
Event | American Control Conference - Arlington, VA, USA Duration: 25 Jun 2001 → 27 Jun 2001 |
Conference
Conference | American Control Conference |
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City | Arlington, VA, USA |
Period | 25/06/01 → 27/06/01 |
Keywords
- on-line
- adaptive control
- robot manipulators
- dynamic
- fuzzy neural networks