Passive separation approach to adaptive visual tracking for robotic systems - Wang, Hanlei (2020)
Separation of the kinematic and dynamic loops is important for industrial/commercial robotic applications (i.e., for designing kinematic control schemes) and also for simplifying the controller structure, but most visual servoing algorithms in the literature, due to the lack of such separation, are hard to be justified as applied to most industrial/commercial robotic systems with a hidden inner control loop. In this brief, we investigate how passivity and nonlinear feedback are used to realize the objective of separation for visual tracking of robotic systems with parametric uncertainty and with time-varying depth, in the case of no image-space velocity measurement. We propose two new passive image-space observers that rely on the joint reference velocity rather than joint velocity, and based on these observers, we develop two adaptive controllers that do not require the image-space velocity measurement and more importantly achieve the separation of the kinematic and dynamic loops. The loop separation is achieved by resorting to the adaptive inverse-Jacobian-like control and nonlinear feedback in the controller and observer, yielding two adaptive kinematic schemes applicable to robots with a closed architecture yet admitting the design of the joint velocity (or position) command. In addition, the proposed second adaptive controller does not require inversion of the estimated depth at the expense of using a potentially-high-gain feedback. The performance of the proposed controllers is shown by numerical simulations and the implementation issues concerning the application to industrial/commercial robots are also discussed.