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Proceedings Paper

Hierarchical nearest-neighbor controller
Author(s): Zhonghao Bao; Gerald M. Flachs; Jay B. Jordan
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Paper Abstract

An adaptive and trainable hierarchical nearest neighbor controller (HNNC) is presented for performing difficult control functions. The controller combines concepts from the theory of finite automata, nearest neighbor decision theory and control theoiy. In the initial implement, the top level uses a finite state machine to assess the control situation and select an appropriate nearest neighbor controller in the second level to control the system using the nearest neighbor concept. The controllers in the second level are very simple "neural-like" controllers that perform very simple control tasks. A training procedure is used to generate the supervisory finite state machine and second level nearest neighbor control points In the state space that define the control law. A hierarchical nearest neighbor controller is presented to balance an inverted pendulum mounted on a moveable cart and to remotely position a trailer truck to a specified position in a constrained region using a video tracking system. These problems demonstrate the power and simplicity of the hierarchical nearest neighbor controller in nonlinear systems.

Paper Details

Date Published: 9 July 1992
PDF: 12 pages
Proc. SPIE 1699, Signal Processing, Sensor Fusion, and Target Recognition, (9 July 1992); doi: 10.1117/12.138242
Show Author Affiliations
Zhonghao Bao, New Mexico State Univ. (United States)
Gerald M. Flachs, New Mexico State Univ. (United States)
Jay B. Jordan, New Mexico State Univ. (United States)


Published in SPIE Proceedings Vol. 1699:
Signal Processing, Sensor Fusion, and Target Recognition
Vibeke Libby; Ivan Kadar, Editor(s)

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