Robust Helicopter Ship Deck Landing With Guaranteed Timing Using Shrinking-Horizon Model Predictive Control
Authors: Philipp Schitz, Paolo Mercorelli, Johann C. Dauer
Organizations: Institute of Flight Systems, German Aerospace Center (DLR), 38108 Braunschweig, Germany · Institute for Production Technology and Systems, Leuphana University of Lueneburg, 21335 Lueneburg, Germany
We present a runtime efficient algorithm for autonomous helicopter landings on moving ship decks based on Shrinking-Horizon Model Predictive Control (SHMPC). First, a suitable planning model capturing the relevant aspects of the full nonlinear helicopter dynamics is derived. Next, we use the SHMPC together with a touchdown controller stage to ensure a pre-specified maneuver time and an associated landing time window despite the presence of disturbances. A high disturbance rejection performance is achieved by designing an ancillary controller with disturbance feedback. Thus, given a target position and time, a safe landing with suitable terminal conditions is be guaranteed if the initial optimization problem is feasible. The efficacy of our approach is shown in simulation where all maneuvers achieve a high landing precision in strong winds while satisfying timing and operational constraints with maximum computation times in the millisecond range.
Figures & tables
Fig. 1: Common ship deck landing approaches for helicopters. Maneuver a) is called straight-in, b) a diagonal and c) a lateral approach. The wind velocity vector is denoted by w .
Fig. 2: Visual representation of the used set operations where dots mark the coordinate origin of the respective sets. The top row shows set addition S1⊕S2 , the middle row set erosion S1⊖S2 , and the bottom row an affine transformation AS1+b .
Fig. 3: Constraint set F . Left: Attitude constraint and maximum accelerations for which geometric features intersect the ground. Right: Linear VRS constraint and exemplary nonlinear boundary computed based on [ 14 ] .
Fig. 4: Terminal sets for NTD=50 . Left: Projections of touchdown constraint set FTD , the starting set XˉTD encoding a successful touchdown, and the resulting terminal set XˉT . Right: Projections of XˉT for different slices in dˉx .
Man.
∣Δϕ∣
∣Δθ∣
∣Δph∣
∣Δvh∣
∣Δvz∣
T
a)
0.6∘
0.2∘
0.06 m
0.04 m/s
0.95 m/s
10.30 s
b)
0.7∘
0.6∘
0.15 m
0.33 m/s
0.91 m/s
10.44 s
c)
0.6∘
1.1∘
0.14 m
0.54 m/s
0.98 m/s
10.54 s
max
4∘
6∘
0.60 m
0.60 m/s
1.15 m/s
10.90 s
TABLE I: Ship-relative attitude Δϕ , Δθ , horizontal position and velocity Δph and Δvh , and vertical velocity Δvz at touchdown, as well as maneuver time T .
Fig. 5: Ship and helicopter trajectories for the considered maneuvers.
Fig. 6: Pitch angles θ (left) and roll angles ϕ (right) trajectories during the landing maneuvers with true and commanded values to the attitude controller marked by solid and dashed lines, respectively.
Fig. 7: Longitudinal (left) and lateral (right) disturbance trajectories during the landing maneuvers. The boundaries of disturbance set D=dˉ+Dv are marked by dashed lines.
Fig. 8: Error trajectories in the longitudinal (left) and vertical (right) axis.
Aerial robotics studies the design, perception, control, and coordination of flying robots for navigation, inspection, transport, and physical interaction.
Landing UAVs on heaving marine platforms is challenging because relative vertical motion can generate large impact forces and cause rebound on touchdown. To address this, we develop an impact-aware Model Predictive Control (MPC) framework that models landing as a velocity-level rigid-body impact governed by Newton's restitution law. We embed this as a linear complementarity problem (LCP) within the MPC dynamics to predict the discontinuous post-impact velocity and suppress rebound. In simulation, restitution-aware prediction reduces pre-impact relative velocity and improves landing robustness. Experiments on a heaving-deck testbed show an 86.2% reduction in post-impact deflection compared to a tracking MPC.
Jess Stephenson, Melissa Greeff
Robora Lab, Ingenuity Labs Research Institute, Department of Electrical and Computer Engineering, Queen’s University, Kingston, Canada
Autonomous landing of unmanned aerial vehicle (UAV) on unmanned surface vehicle (USV) is difficult in high sea states. Existing hierarchical landing methods often infer future USV motion only implicitly from short observation histories, which limits their ability to adapt to severe and rapidly changing sea conditions. This paper presents a prediction-informed hierarchical control framework. A pretrained model is developed to explicitly predict the USV attitude during high-level landing decisions. The prediction helps identify suitable landing windows and choose the right touchdown time, while a conventional low-level flight controller maintains UAV stability and tracks motion commands. To evaluate the framework under realistic disturbances, a real-sim validation setup is constructed in which USV-motion data collected in real sea conditions are transferred to a MuJoCo USV. The experiments demonstrate the feasibility of using explicit attitude prediction to improve landing-window selection and support timing-aware UAV landing planning in rapidly varying marine environments.
Kaizhen Li, Xi Zhou, Dan Zhang +1
School of Mechatronic Engineering and Automation Shanghai University Shanghai, China
This paper introduces an estimation and control framework for dynamic landing of multi-rotor uncrewed aerial vehicles on moving platforms. The proposed method integrates nonlinear model predictive control with a real-time minimum-jerk trajectory planner that enforces a prescribed touchdown time, enabling consistent timing during the terminal descent. To enhance robustness in the presence of time-varying sensing quality, we utilize an adaptive unscented kalman filter that updates the process and measurement noise statistics online. In addition, we provide a reference feasibility analysis showing that minimum-jerk references induce bounded thrust and torque commands under standard tracking hypotheses. The proposed framework is evaluated in simulation and hardware experiments, and it is shown to achieve repeatable landings and improved platform velocity prediction accuracy relative to EKF/UKF-based methods.
Mohammadreza Izadi, Zeinab Shayan, Steven Waslander +1
Autonomous Vehicles Laboratory, Department of Aerospace Engineering, Toronto Metropolitan University, Toronto, Canada · University of Toronto Institute for Aerospace Studies, University of Toronto, Toronto, Canada