Neurocontrol I: Self-organizing speed-fileld tracking
Cs. Szepesvári and A. Lõrincz
Neural Network World
6,
875--896
(1996)
Abstract
The problems of controlling a plant while avoiding obstacles and
experiencing perturbations in the plants dynamics are considered.
It is assumed that the plant's dynamics is not known in advance.
To solve this problem a self-organizing artificial neural network
(ANN) solution is advanced here. The ANN consists of various parts.
The first part discretizes the state space of the plant and also
learns the geometry of the state space. The learnt geometrical
relations are represented by lateral connections. These connections
are utilized for planning a speed field, allowing collision free
motion. The speed field is defined over the neural represention of
the state space and is transformed into control signals with the help
of interneurons associated with the lateral connections: connections
between interneurons and control neurons encode the inverse dynamics of
the plant. These connections are learnt during a direct system inverse
identification process by Hebbian learning. Theoretical results and
computer experiments show the robustness of approach.
