Multiple Model Adaptive Estimation Techniques for Adaptive Model-Based Robot Control
Abstract
The use of robotic manipulators for future Air Force applications will require a manipulator capable of emulating the performance of the human arm. To emulate human arm motion, a robot must be capable of adapting quickly and accurately to changes in the environment while maintaining accurate high speed tracking performance. One approach to adaptive robotic control is the use of Multiple Model Adaptive Estimation (MMAE) techniques within a model-based control structure. The MMAE techniques employ a bank of Kalman filters whose models are based on different assumed values of the uncertain parameters. Using this bank of filters, the MMAE provides an estimate of the uncertain parameters. A previous development used a closed-loop form of MMAE with a model-based controller and was called Multiple Model-Based Control (MMBC). Further analysis of the MMBC showed it has limited applications to manipulators whose dynamics and tracking performance depend heavily on the payload. This is not the case for the PUMA-560 manipulator. As a result, a new form of adaptive model-based control called Open-Loop Multiple Model-Based Control (OL/MMBC) was developed. The OL/MMBC combines a model-based controller with a MMAE algorithm whose filters are based on an open-loop linearized perturbation model. The OL/MMBC was simulated and experimentally evaluated on a PUMA-560 manipulator. Theses. (RRH)
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 1989
- Accession Number
- ADA215742
Entities
People
- Samuel J. Sablan
Organizations
- Air Force Institute of Technology