This paper investigates vision-based robot control based on passivity for three-dimensional (3-D) target tracking. First, using standard body-attached coordinate frames (the world frame, camera frame, and object frame), we represent the relative position and orientation between a moving target and a camera as an element of SE(3). Using this representation we derive a nonlinear observer to estimate the relative rigid body motion from the measured camera data. We then establish the relationship between the estimation error in a 3-D workspace and in the image plane. We show passivity of the dynamic visual feedback system by combining the passivity of both the visual feedback system and the manipulator dynamics which allows us to prove stability in the sense of Lyapunov for the full 3-D dynamic visual feedback system. The L₂ -gain performance analysis, which deals with the disturbance attenuation problem, is then considered via dissipative systems theory. Finally, experimental results are presented to verify the stability and L₂-gain performance of the dynamic visual feedback system

This paper addresses passivity-based pose synchronization in the Special Euclidean group SE(3) . We first develop a passivity-based distributed velocity input law to achieve pose synchronization. We next show that the pose synchronizes exponentially fast under an assumption on the initial states of the bodies, with an exponential convergence rate given by algebraic connectivity of interconnection graphs. We also prove pose synchronization in the presence of communication delays and topology switches. We moreover give further extensions of the present law, where desirable velocities and collision avoidance are taken into account. Finally, the effectiveness of the present inputs is demonstrated through numerical simulations and experiments on a planar (2D) test bed.

This paper deals with /spl mu/-synthesis of an electromagnetic suspension system. First, an issue of modeling a real physical electromagnetic suspension system is discussed. We derive a nominal model as well as a set of models in which the real system is assumed to reside. Different model structures and possible model parameter values are fully employed to determine unstructured additive plant perturbations, which directly yield uncertainty frequency weighting function. Second, based on the set of plant models, we setup robust performance control objectives. Third, we make use of the D-K iteration approach for the controller design. Finally, implementing the controller with a digital signal processor, experiments are carried out. With these experimental results, we show robust performance of the designed control system.< >