Event-Triggered Nonlinear Visual Predictive Control Strategy for Robots

Abstract

This paper proposes an event-triggered nonlinear visual predictive control strategy for image-based visual servoing of robots. It involves developing a nonlinear model of the visual servoing system and designing a predictive control strategy that addresses safety, real-time performance, robustness, and smooth motion control. Field-of-view constraints ensure image feature visibility, physical constraints respect joint limits, and smooth motion constraints protect hardware from excessive stress. The event-triggered mechanism activates control laws only when necessary, reducing the computational burden of continuous control adjustments and enhancing responsiveness and efficiency. This strategy supports robustness, mitigates issues arising from local minima, and maintains system stability, providing a practical solution for real-time visual servoing tasks. Furthermore, we compare the performance of the proposed strategy against conventional and modified model predictive control strategies in various visual servoing tasks through simulations. Finally, the experiment results demonstrate the effectiveness of the strategy.