A novel non-recursive control for payload positioning and swing suppressing problems in overhead-crane applications

Abstract

The paper introduces a novel non-recursive robust adaptive control method based on a high-gain non-recursive observer (HGNRO) for 3D overhead cranes (3DOC) to suppress payload oscillations during lifting and transportation operations, while simultaneously tracking the trajectory of the payload under parameter uncertainties and external disturbances. The state observer is model-independent and is designed to estimate certain states that are difficult or impossible to measure directly using sensors, such as the swing angle velocity of the payload. The novel non-recursive robust adaptive controller is formulated without prior knowledge about the model. It simply utilizes a time-varying control gain that is updated online, eliminating the need for a parameter estimation tool to handle model uncertainties, thereby avoiding the complexity of parameter estimation. The stability of the closed-loop control system is analyzed and verified using the Lyapunov stability theory. The control law yields a computationally efficient result and provides excellent sway suppression, which ensures that the tracking error of the payload trajectory and all closed-loop system parameters are bounded and converge to zero. The simulation results using a quasi-physical model and qualitatively compared with the terminal sliding mode control-based fixed-time extended state observer to demonstrate the feasibility and effectiveness of the proposed controller.