Neurocontroller alternatives for "fuzzy" ball-and-beam systems with nonuniform nonlinear friction

The ball-and-beam problem is a benchmark for testing control algorithms. Zadeh proposed (1994) a twist to the problem, which, he suggested, would require a fuzzy logic controller. This experiment uses a beam, partially covered with a sticky substance, increasing the difficulty of predicting the ball...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2000-03, Vol.11 (2), p.423-435
Main Authors: Eaton, P.H., Prokhorov, D.V., Wunsch, D.C.
Format: Article
Language:English
Subjects:
Backpropagation algorithms
Benchmark testing
Control algorithms
Control systems
Control theory
Design engineering
Dynamic programming
Dynamical systems
Dynamics
Fuzzy control
Fuzzy logic
Neural networks
Neurocontrollers
Nonuniform
Position measurement
Recurrent neural networks
Studies
Velocity control
Velocity measurement
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Summary:The ball-and-beam problem is a benchmark for testing control algorithms. Zadeh proposed (1994) a twist to the problem, which, he suggested, would require a fuzzy logic controller. This experiment uses a beam, partially covered with a sticky substance, increasing the difficulty of predicting the ball's motion. We complicated this problem even more by not using any information concerning the ball's velocity. Although it is common to use the first differences of the ball's consecutive positions as a measure of velocity and explicit input to the controller, we preferred to exploit recurrent neural networks, inputting only consecutive positions instead. We have used truncated backpropagation through time with the node-decoupled extended Kalman filter (NDEKF) algorithm to update the weights in the networks. Our best neurocontroller uses a form of approximate dynamic programming called an adaptive critic design. A hierarchy of such designs exists. Our system uses dual heuristic programming (DHP), an upper-level design. To our best knowledge, our results are the first use of DHP to control a physical system. It is also the first system we know of to respond to Zadeh's challenge. We do not claim this neural network control algorithm is the best approach to this problem, nor do we claim it is better than a fuzzy controller. It is instead a contribution to the scientific dialogue about the boundary between the two overlapping disciplines.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/72.839012