Robust Image-Based Adaptive Fuzzy Controller for Guarantee Field of View With Uncertain Dynamics

Abstract

Visual servoing technology has widely been employed in manufacturing because it is a flexible, realizability, and low-cost way to improve the intelligence of the industry robot. Nevertheless, a worrisome and overlooked issue is that the loss of visual features in the camera's field of view may lead to the failures of the visual servoing tasks. This article addresses the visual features escaping problem, by implementing an asymmetric barrier Lyapunov function with a field-of-view constraint controller. The asymmetric barrier Lyapunov function defines a tightly specified range for the feature coordinate errors and ensures the transient response of the tracking error as well as enables arbitrary tracking accuracy. It is worth noting that the asymmetric barrier Lyapunov function directly handles the visual-robot-coupled dynamics while guaranteeing system stabilities. Besides, to accommodate the uncertain dynamics derived from a high-dimensional coupled system, an adaptive controller is proposed utilizing fuzzy neural networks with computational efficiency and few training parameters to enhance the control performance. Finally, the effectiveness of the proposed control strategy has been demonstrated through both theoretical analysis and experimental verification.