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<p>The Helicopter-Slung Load System (HSLS) finds wide application in various fields, including military operations, rescue missions, and transportation tasks. However, there remain limited studies on nonlinear optimal control specifically tailored for such a highly nonlinear system, particularly addressing both trajectory tracking and swing suppression simultaneously. This paper investigates the dynamics modeling, swing suppression, and trajectory tracking of HSLS. The HSLS is a multi-body coupled system, consisting of a helicopter and a suspended load connected by a sling cable, with internal ideal constraint forces and possessing four motion degrees of freedom (DOFs) in the longitudinal plane. This paper employs the Newton–Euler method for dynamics modeling. A holistic approach is adopted to derive the dynamics equations for the overall translational motion of the HSLS. When establishing the dynamics equations for the helicopter’s pitch motion and the swing of the suspended load, the suspension point is selected as the moment center. This modeling method avoids involving internal forces throughout the modeling process, significantly improving modeling efficiency. The HSLS is a complex nonlinear controlled object. This paper introduces the iterative Linear Quadratic Regulator (iLQR) method to design an integrated optimal controller for trajectory tracking and swing suppression for HSLS. By performing iterative computations in function space, iLQR method transforms the design problem of a nonlinear quadratic regulator (NLQR) into a series of linear quadratic regulator (LQR) problems, thereby effectively solving the NLQR problem. The optimal controller, designed based on the iLQR method, employs offline iterative optimization, and the resulting optimal control law takes a state-feedback closed-loop form, effectively ensuring robustness of online control and addressing the poor robustness of traditional open-loop optimal control methods. Numerical simulations validate the effectiveness and superiority of the proposed method.</p>

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An Optimal Control Method for Trajectory Tracking and Swing Suppression in Helicopter-Slung Load System

  • 期刊论文

  • 发表日期：

    2025年11月3日

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  • Yanhua Han,
  • Ruofan Li,
  • Yong Zhang

摘要

The Helicopter-Slung Load System (HSLS) finds wide application in various fields, including military operations, rescue missions, and transportation tasks. However, there remain limited studies on nonlinear optimal control specifically tailored for such a highly nonlinear system, particularly addressing both trajectory tracking and swing suppression simultaneously. This paper investigates the dynamics modeling, swing suppression, and trajectory tracking of HSLS. The HSLS is a multi-body coupled system, consisting of a helicopter and a suspended load connected by a sling cable, with internal ideal constraint forces and possessing four motion degrees of freedom (DOFs) in the longitudinal plane. This paper employs the Newton–Euler method for dynamics modeling. A holistic approach is adopted to derive the dynamics equations for the overall translational motion of the HSLS. When establishing the dynamics equations for the helicopter’s pitch motion and the swing of the suspended load, the suspension point is selected as the moment center. This modeling method avoids involving internal forces throughout the modeling process, significantly improving modeling efficiency. The HSLS is a complex nonlinear controlled object. This paper introduces the iterative Linear Quadratic Regulator (iLQR) method to design an integrated optimal controller for trajectory tracking and swing suppression for HSLS. By performing iterative computations in function space, iLQR method transforms the design problem of a nonlinear quadratic regulator (NLQR) into a series of linear quadratic regulator (LQR) problems, thereby effectively solving the NLQR problem. The optimal controller, designed based on the iLQR method, employs offline iterative optimization, and the resulting optimal control law takes a state-feedback closed-loop form, effectively ensuring robustness of online control and addressing the poor robustness of traditional open-loop optimal control methods. Numerical simulations validate the effectiveness and superiority of the proposed method.