Abstract:
Vision-based target tracking and fixation to keep objects
that move in three dimensions in view is important for many
tasks in several fields including intelligent transportation systems
and robotics. Much of the visual control literature has focused on
the kinematics of visual control and ignored a number of significant
dynamic control issues that limit performance. In line with
this, this paper presents a neural network (NN)-based binocular
tracking scheme for high-performance target tracking and fixation
with minimum sensory information. The procedure allows the
designer to take into account the physical (Lagrangian dynamics)
properties of the vision system in the control law. The design objective
is to synthesize a binocular tracking controller that explicitly
takes the systems dynamics into account, yet needs no knowledge
of dynamic nonlinearities and joint velocity sensory information.
The combined neurocontroller–observer scheme can guarantee
the uniform ultimate bounds of the tracking, observer, and NN
weight estimation errors under fairly general conditions on the
controller–observer gains. The controller is tested and verified via
simulation tests in the presence of severe target motion changes
