This paper introduces a novel position and attitude trajectory tracking control law for unmanned aerial manipulators (UAMs) with aerobatic flight capabilities, enabling them to perform complex aerial manipulation tasks with objects of unknown inertia parameters. The proposed control law is structured in a cascaded form, with control gains selected through frequency analysis based on a previously developed adaptive incremental nonlinear dynamic inversion (INDI) controller. A quaternion representation is used to avoid singularity issues in attitude control during unmanned aerial vehicle flight maneuvers and simultaneous arm manipulation. The methodology is validated through extensive simulations, demonstrating that the control law enables the UAM to perform static and dynamic pitch-hovering motions while effectively compensating for disturbances and inertia variations induced by the manipulator as it moves the object in midair.
This paper addresses the challenge of navigating unmanned aerial vehicles (UAVs) in contested environments by introducing a cooperative multi-agent framework that increases the likelihood of safe UAV traversal. The approach involves two types of UAVs: low-priority agents that explore and localize threats and a high-priority agent that navigates to its target destination while minimizing the risk of detection by enemy radar systems. The low-priority agents employ a decentralized optimization algorithm to balance exploration, radar localization, and the identification of safe paths for the high-priority agent. For the high-priority agent, two path-planning methods are proposed: one for deterministic scenarios using weighted Voronoi diagrams and another for uncertain scenarios that leverages generalized Voronoi diagrams (using a non-Euclidean criterion derived from uncertainty in the radar's probability of detection) together with probabilistic constraints. Both methods use optimization techniques to refine trajectories while accounting for kinematic constraints and radar detection probabilities. Numerical simulations demonstrate the effectiveness of the proposed framework. This research advances UAV path-planning methodologies by combining heterogeneous multi-agent cooperation, probabilistic modeling, and optimization to enhance mission success in adversarial environments.
To safely accommodate significantly higher air traffic demands, the future air traffic management (ATM)concept will make use of trajectory-based operations (TBOs) in combination with reduced separation criteria,where the intended flight trajectories will be better communicated to the air traffic control system on the ground,and an improved ground capability for monitoring the realization of or deviation from the communicated flightplan will be available. Current ATM makes use of ground target tracking systems that account for basic modeswitching between level flight, climb, and descent. However, future ATM is in need of a ground system that alsoconsiders onboard flight guidance modes. In a prior contribution, we developed an interacting multiple model(IMM) filter that takes onboard flight guidance modes into account and demonstrated that such an approachperforms well under nominal conditions. But the IMM was developed under the unrealistic assumption that, foreach guidance mode, the actual control set point is known. The objective of the current contribution is to extend ourearlier work by dropping such an unrealistic assumption. In nonlinear filtering, a simultaneous switching of aguidance mode and a jump in the control set point is referred to as a hybrid jump. To cope with such hybrid jumps,the standard IMM has been extended to a generalized IMM (GIMM). The current article develops this GIMMapproach, considering ADS-B and enhanced mode S surveillance data, for the joint estimation of simultaneousjumps in aircraft guidance modes and control set points and shows its performance on both simulated and real flightdata.
A novel and analytical Koopman-Operator-Theory-based methodology is presented to derive rigid-body position and attitude dynamics, with direct applications to underactuated quadrotor and multirotor unmanned aerial vehicles (UAVs). The presented methodology may be used to derive, implement, and test control strategies for generalized robotic platforms and other aerospace systems. Unlike existing data-driven Koopman-based techniques, this formulation is model based and allows for an exact linear model representation of the original nonlinear position and attitude underlying system dynamics. The system model is linear in the autonomous component and state dependent in the control component. The validity range of the finite Koopman model truncation is determined, followed by the controllability and stabilizability analysis. Compared to existing literature formulations, the analytically derived Koopman-based model results in a better approximation of the original dynamics because it uses a more compact truncation of the lifted state space. Further, the system model is derived by using the Koopman approach on the complete system dynamics, without the need for angular velocity dynamic compensation. We show that a truncated subset of the infinite-dimensional model embeds most of the original nonlinear dynamics and can be used to design linear controllers in the lifted space; this corresponds to designing nonlinear controllers in the original state space. A quadrotor UAV is used for implementation and proof-of-concept demonstration purposes. The main advantages of the proposed methodology center around the effective use of linear control strategies for nonlinear plants and for solving the underactuation problem employing a single control loop.
Accurate relative pose estimation between unmanned aerial vehicles (UAVs) and marine vessels is critical for autonomous UAV operations in ocean environments, including ship-based launch and recovery, as well as aerial data collection. Traditional visual-based and Global Positioning System-based systems often suffer from occlusion, lighting variability, and signal degradation over water. This paper proposes a novel approach to estimate the six-degree-of-freedom relative pose of a UAV with respect to a vessel using a single light detection and ranging (LiDAR) sensor. This method leverages the attention-based mechanisms of the point transformer architecture to capture both local and global geometric features from sparse and noisy point clouds acquired in marine conditions. We constructed synthetic and real-world datasets that incorporate ocean-specific noise profiles and validated our model across multiple trajectories and demonstrated robust performance even with partial scans. It is shown that the proposed architecture significantly outperforms traditional point-based baselines in both accuracy and robustness, enabling reliable pose estimation for autonomous maritime UAV operations.
To enhance the aerodynamic efficiency of micro aerial vehicles (MAVs) with rotary wings, a bio-inspired hybrid flapping-wing rotor (HFWR) configuration can be designed that achieves a power efficiency more than twice that of conventional rotors. Nevertheless, up to the present, the controllable flight of HFWR has so far eluded realization due to severe flapping-induced structural vibrations and nonlinear coupling between aerodynamic and elastic dynamics. This paper provides a practical step toward stable, controllable HFWR flight through two key innovations: a thrust-vectoring gimbal architecture that delivers continuous control moments under strong oscillations, and an enhanced nonlinear model predictive control (E-MPC) framework implemented as a distributed two-layer architecture. In this architecture, the outer layer consists of a lower-rate offboard MPC that generates constraint-aware attitude trim and bias commands, while the inner layer is a high-rate onboard proportional angular-rate loop that provides rapid damping of high-frequency perturbations caused by flapping-induced vibrations and communication or optimization latency. Hover and yaw flight tests demonstrate that the integrated architecture improves attitude stability compared with cascade PID and a baseline offboard MPC without the onboard rate loop, reducing peak deviation, overshoot, and steady-state error by up to 83%, 92%, and 80%, respectively, while substantially lowering control energy. These results demonstrate a practical pathway toward stable control of flapping-rotor MAVs for the first time, bridging the gap between bio-inspired aerodynamic efficiency and flight controllability.
There is a pressing need for accurate and reliable position, navigation, and timing (PNT) support on and around the moon; multiple space agencies have stated PNT as a requirement for upcoming missions. The first phases of lunar PNT focus on the lunar South Pole and low lunar orbit up to 100 km altitude. This paper seeks to provide insight into the primary contributions to positioning error on the lunar surface. To improve the estimations of performance, an alternative metric, user-equivalent range-error-weighted geometric dilution of precision (KDOP), is studied. The assumption that ranging error is constant across systems is shown to be invalid for candidate lunar PNT systems and leads to significant differences in performance estimation between KDOP and geometric dilution of precision, a commonly used measure. Parameters related to user error, onboard clock stability, and orbit determination performance are varied to find and quantify monotonic relationships between these design parameters and performance metrics. The metric KDOP is found to be needed for accurate performance estimation under reasonable technology performance assumptions.
The goal of filtering is to estimate the current state using past and present information. In contrast, smoothing refers to estimating past states using current and future information. Under certain assumptions, the Kalman filter (KF) provides a closed-form solution to the filtering problem, from which fixed-interval smoothers such as the Fraser-Potter (FP) and Rauch-Tung-Striebel (RTS) algorithms can be derived. To address numerical instabilities in the KF and associated smoothers, covariance factorization techniques, such as UDU factorization, are used. In this work, different versions of the FP and RTS smoothers are derived and analyzed. Specifically, an FP UDU smoother and an RTS UDU smoother are proposed. For the derivation of the RTS smoother, the weighted hyperbolic Householder reflector is introduced as a generalization of both the Householder reflector and the hyperbolic Householder reflector. The UDU smoothers are compared with traditional and stable formulations in a numerical example, demonstrating their numerical equivalence and validating their implementation.
Autonomous dynamic soaring can be used to increase the endurance and range of unmanned aerial vehicles by harvesting energy from the vertical gradient of the horizontal wind. This study aims to develop a guidance and control strategy that allows precise following of an optimal dynamic-soaring path for a glider vehicle. The proposed control architecture combines a geometric path-following guidance law with an SO(3)-based attitude control law. High-fidelity six-degree-of-freedom simulation shows that the proposed method can achieve a position accuracy of 0.1 m for a glider with a wingspan of 2 m while adhering to the constraints present on a glider airframe. The high tracking accuracy makes it possible to conduct autonomous dynamic-soaring operations with patterns that were considered impossible in previous studies, such as a travel pattern mimicking the albatrosses’ dynamic-soaring pattern in close proximity to the ocean surface.
It is often imperative to test and validate the development of active flutter suppression technology in wind tunnels or in flight near and beyond the flutter speed. This is done not only to fine-tune mathematical models but also to examine the active control laws that were synthesized from such models. However, in-flight flutter tests are often dangerous, costly, and sometimes infeasible. Wind tunnel tests are usually more affordable and less risky, leading to higher productivity in evaluating alternative configurations and control law strategies. The University of Washington's MARGE-I aeroservoelastic wind tunnel model is a flexible half-wing-body-tail model with active ailerons and an elevator. The work here studies SISO mixed-sensitivity H infinity control for active flutter suppression, which is designed subject to requirements and hardware constraints. Such a controller is validated by wind tunnel tests, where the closed-loop margins in both simulation and experiments are quantified and compared. A previously published safe wind tunnel and flight test technique is used to generate flutter mechanisms. The set of control surfaces is partitioned into destabilizing and stabilizing ones, where a destabilizing controller reshapes the aeroservoelastic system to yield the desired unstable dynamic characteristics, and the stabilizing controller is used to study active flutter control strategies.
Terrain-following (TF) guidance is essential for evading radar detection and enabling safe low-altitude flight. While optimal-control-based TF methods have addressed terrain constraints, they remain limited by computational complexity, input saturation, and pilot-induced oscillations (PIOs). PIOs are particularly hazardous, as they induce sustained oscillations that can cause excessive overshoot over high terrain or collisions. This paper presents a real-time guidance framework that embeds a human pilot model into a model predictive path integral (MPPI) controller with terrain-dependent weighted cost functions. A path-integral-based parameter estimation (PIPE) algorithm is developed to adaptively identify pilot gain and delay online, enabling in-flight suppression of PIOs. Simulation results demonstrate that the proposed MPPI-PIPE framework reduces terrain-following oscillation amplitude by 36.8% compared to MPPI without pilot adaptation, while maintaining real-time feasibility through GPU-parallelized trajectory sampling. Hardware-in-the-loop simulation results further confirm effective PIO mitigation and accurate low-altitude tracking across diverse pilot characteristics.
This paper investigates entry guidance for the second stage of super heavy-lift launch vehicles, which are lifting-body vehicles equipped with aerodynamic control surfaces. The vehicle performs a gliding entry, demanding very high targeting accuracy. Existing methods relying solely on bank-angle control typically achieve only kilometer-level targeting accuracy. This paper proposes a predictor-feedback entry guidance method realizing meter-level targeting accuracy. The high accuracy necessitates the use of both angle of attack and bank angle as controls. However, this leads to a difficulty in that the controls have a strong coupled effect on the downrange and cross-range errors. To address this, the proposed method first applies reference control profiles to predict the terminal position error, then introduces an appropriate transformation to obtain a transformed terminal position error. This error serves as feedback to the designed guidance laws for computing the control commands. A unique property is the near decoupling of the controls' influence on the transformed error, enabling significant improvement in targeting accuracy. Path constraints on heating rate, dynamic pressure, and load factor are also incorporated via reference control refinement. Notably, the proposed entry guidance's stability is theoretically established. Numerical results clearly demonstrate the effectiveness, strong robustness, and high targeting accuracy of the proposed method.
