Abstract
This paper deals with the problem of identifying and controlling nonlinear time varying plants. The algorithm is defined to reach the following objectives. First, to achieve a quick and efficient identification, the plant is represented by a linear model defined with hyperplanes. They are found by minimizing errors between the last measures and their estimations. Secondly, to obtain a smooth control and a good accuracy, control law is derived from the optimization of a quadratic criteria defined to minimize outputs errors and the successive derivatives of the control. This identification and control law overcome the problems met in adaptive control based on neural networks, for which learning is often long, and where an important number of neurons and weights is required to get accurate results. Finally the proposed approach is applied to control a two-link planar robot manipulator and a PUMA 560 robot.
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Canart, R., Borne, P. Implementation of Adaptive Hyperplanes for the Determination of Robust Control. Journal of Intelligent and Robotic Systems 18, 289–308 (1997). https://doi.org/10.1023/A:1007952315886
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DOI: https://doi.org/10.1023/A:1007952315886