Nonparametric adaptive payload tracking for an offshore crane

2025-03-15

AI Summary

This paper addresses the low trajectory tracking accuracy of offshore cranes under unknown wave disturbances and unmodeled dynamics. To this end, a Cartesian-space nonparametric adaptive control method is proposed. The approach online approximates the unknown disturbance forces and unmodeled dynamics within a reproducing kernel Hilbert space (RKHS), and integrates partial feedback linearization to achieve closed-loop end-effector position control. Rigorous stability analysis proves that the tracking error is uniformly ultimately bounded. Compared with conventional joint-space methods, the proposed Cartesian framework demonstrates superior tracking performance—both in adaptive and non-adaptive scenarios. Comprehensive simulations and real-world experiments validate its significantly enhanced disturbance rejection capability and trajectory tracking accuracy.

Technology Category

Intelligent Robots: State EstimationCognitive Modeling & Cognitive Systems: Adaptive BehaviorSearch and Optimization: Mixed Discrete/Continuous Search

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsUser Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendationEconomics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

A nonparametric adaptive crane control system is proposed where the crane payload tracks a desired trajectory with feedback from the payload position. The payload motion is controlled with the position of the crane tip using partial feedback linearization. This is made possible by introducing a novel model structure given in Cartesian coordinates. This Cartesian model structure makes it possible to implement a nonparametric adaptive controller which cancels disturbances by approximating the effects of unknown disturbance forces and structurally unknown dynamics in a reproducing kernel Hilbert space (RKHS). It is shown that the nonparametric adaptive controller leads to uniformly ultimately bounded errors in the presence of unknown forces and unmodeled dynamics. Moreover, it is shown that the Cartesian formulation has certain advantages in payload tracking control also in the non-adaptive case. The performance of the nonparametric adaptive controller is validated in simulation and experiments with good results.

Problem

• Develops nonparametric adaptive control for offshore crane payload tracking.
• Uses Cartesian model to handle unknown disturbances and dynamics.
• Validates controller performance through simulations and experiments.

Innovation

• Nonparametric adaptive control for crane payload tracking.
• Cartesian model structure enables disturbance cancellation.
• RKHS approximates unknown forces and dynamics effectively.