Autonomous unmanned aerial vehicle (UAV) landing on a moving ship deck is challenging even with Global Positioning System (GPS), and in GPS-denied seas, the vehicle must estimate its motion relative to the heaving deck while committing to a touchdown whose location and time are not yet known. This article presents an integrated estimation, guidance, and control architecture that treats the landing vessel not as a passive target but as an active cooperative sensing platform, transmitting time-stamped inertial packets to the UAV over a delayed and lossy wireless link. These packets are fused with the local UAV inertial measurement unit (IMU), ultrawideband (UWB) anchor–tag ranges, and altimetry through a cross-platform covariance-coupled dual extended Kalman filter (CP-DEKF). By preserving the cross-covariance between the fast relative-navigation states and the slow UAV and vessel IMU biases, the filter lets UWB and altimeter updates correct the biases indirectly, although no sensor observes them directly. A finite-horizon identifiability condition and a companion sensitivity analysis establish when this channel is effective and how packet delay and covariance scheduling reduce the correction it provides. For guidance, a predictive-terminal sequential convex programming (PT-SCP) planner linearizes the moving touchdown manifold about the free final time under a quadratic terminal-accuracy bound, and a linear time-varying (LTV) model predictive control (MPC) closes the loop. In simulation, the CP-DEKF outperforms single-platform extended Kalman filter (EKF) baselines, most notably in velocity, with the margin set by vessel-link latency, packet quality, and UWB geometry. A 600-trial Monte Carlo campaign across Sea States 2–5 yields closed-loop landing success rates of 85%–72% within a 0.5 m threshold.
This brief studies obstacle-avoiding reference generation and swing-suppressed tracking for overhead cranes under small-angle and constant-rope-length assumptions. In a cluttered workspace, a collision-free trolley path may still be unsuitable if its timing and acceleration profile excite swing or leave insufficient room for braking. To address this issue, a timed elastic band (TEB)-style local planner is formulated over position, velocity, acceleration, and transition-time variables. For each candidate local trajectory, the acceleration sequence is propagated through a linearized swing model, and the predicted swing is penalized in the local objective. The obstacle influence radius is also updated along the same candidate trajectory according to a learned braking-distance estimate, which depends on the local speed and predicted swing state. The optimized time-parameterized reference is then tracked by two constrained axis-wise MPC controllers. The static-obstacle simulations, one moving-obstacle case, and laboratory-scale experiments show that the proposed scheme reduces payload swing and avoids overly conservative detours within the tested operating regime.
We investigate the state estimation problem for a general class of nonlinear systems subject to multisource heterogeneous disturbances including dynamic uncertainties, stochastic noises, and intermittent inputs. In particular, the intermittent input term in the state transition equation, stemming from packet loss of the control signal without acknowledgment or occasional failure of the actuator, is an important source of uncertainties in practice. Nevertheless, this intermittent input term has received relatively little research attention. In this article, we use a Bernoulli binary indicator to describe the intermittent availability of input signals. On this basis, a composite non-Gaussian filtering scheme is developed, which consists of a variational Bayes-adapted disturbance observer (DO) to estimate the joint distribution of dynamic uncertainty and binary indicator, and a robust particle filter (PF) with a modified proposal distribution to track the non-Gaussian posterior probability density function (pdf) of the state variable. Compared with the existing methods, our approach can make more effective use of the heterogeneous characteristics of dynamic uncertainties, stochastic noises, and intermittent inputs, thus producing refined antidisturbance estimation results. Furthermore, the proposed scheme is applied to attitude estimation of quadrotor unmanned aerial vehicles (UAVs) subject to packet loss of the controller-to-actuator channel, enhancing the resilience of the UAV attitude determination system against unreliable control signal transmission.
To address time delays commonly observed in industrial thermal processes, this article proposes an observer-based Robin boundary control strategy for a class of convection-coupled thermal models. Traditional infinite-dimensional control approaches are often constrained by the simultaneous presence of delayed Robin boundary actuation and convection-coupled dynamics, which prevents the direct use of standard modal decoupling and predictor-based compensation techniques. To overcome this challenge, a lifting function and coordinate transformation are introduced to convert the system into a homogeneous form. This transformation ensures the orthogonality of the weighted Sturm–Liouville eigenfunctions, thereby facilitating the analytical computation of system coefficients through spectral decomposition. Additionally, a finite-dimensional observer is constructed using measurements from a limited number of points, and an integral predictor is designed as the boundary controller to compensate for time delays, enabling precise temperature regulation. The closed-loop stability of the system is established via an explicit Artstein transformation and a carefully designed Lyapunov function. Simulation and experimental results demonstrate that the proposed approach significantly outperforms traditional methods by mitigating actuator saturation and reducing temperature overshoot by 12%, thereby validating its practical value in complex heat transfer processes.
