A Multiple Models Approach for Adaptation and Learning in Mobile Robots Control
Journal of Intelligent and Robotic Systems
Neural network control of underwater vehicles
Engineering Applications of Artificial Intelligence
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This article considers the tracking control problem of an underwater vehicle used in the exploitation of combustible gas deposits at great sea depths. The vehicle is subjected to different load configurations that introduce considerable variations of its mass and inertial parameters. In this work it is assumed that the possible vehicle configurations are known, but the time instants when the changes occur and the new vehicle configuration following the change are unknown. A neural network-based switching control is proposed for the considered mode-switch process. This solution simplifies the control scheme implementation and reduces the control signal chattering.