In this paper, we investigate the adaptive control problem for robot manipulators with both the uncertain kinematics and dynamics. We propose two adaptive control schemes to realize the objective of task-space trajectory tracking irrespective of the uncertain kinematics and dynamics. The proposed controllers have the desirable separation property, and we also show that the first adaptive controller with appropriate modifications can yield improved performance, without the expense of conservative gain choice. The performance of the proposed controllers is shown by numerical simulations.
In this paper, we investigate the visual tracking problem for robotic systems without image-space velocity measurement, simultaneously taking into account the uncertainties of the camera model and the manipulator kinematics and dynamics. We propose a new image-space observer that exploits the image-space velocity information contained in the unknown kinematics, upon which, we design an adaptive controller without using the image-space velocity signal where the adaptations of the depthrate-independent kinematic parameter and depth parameter are driven by both the image-space tracking errors and observation errors. The major superiority of the proposed observer-based adaptive controller lies in its simplicity and the separation of the handling of multiple uncertainties in visually servoed robotic systems, thus avoiding the overparametrization problem of the existing work. Using Lyapunov analysis, we demonstrate that the image-space tracking errors converge to zero asymptotically. The performance of the proposed adaptive control scheme is illustrated by a numerical simulation.
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