In:
Journal of Dynamic Systems, Measurement, and Control, ASME International, Vol. 123, No. 2 ( 2001-06-01), p. 253-264
Abstract:
The dynamic response of an adaptive fuzzy neural network (FNN) controlled quick-return mechanism, which is driven by a permanent magnet (PM) synchronous servo motor, is described in this study. The crank and disk of the quick-return mechanism are assumed to be rigid. First, Hamilton’s principle and Lagrange multiplier method are applied to formulate the mathematical model of motion. Then, based on the principle of computed torque, an adaptive controller is developed to control the position of a slider of the quick-return servomechanism. Moreover, since the selection of control gain of the adaptive controller has a significant effect on the system performance, an adaptive FNN controller is proposed to control the quick-return servomechanism. In the proposed adaptive FNN controller, an FNN is adopted to facilitate the adjustment of control gain on line. Simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behavior of the proposed adaptive FNN control system are robust with regard to parametric variations and external disturbances.
Type of Medium:
Online Resource
ISSN:
0022-0434
,
1528-9028
Language:
English
Publisher:
ASME International
Publication Date:
2001
detail.hit.zdb_id:
240760-7
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