Future aircraft with increasingly flexible high aspect ratio wings are more vulnerable to gust and turbulence encounters. Active control technologies are therefore required to mitigate the effects of atmospheric disturbances and reduce structural sizing loads. However, the achievable load alleviation performance is constrained by system limitations such as time delays, parasitic dynamics, actuator limits, and sensor noise. In this context, model predictive control systems offer strong potential, as they can address these limitations. This paper presents the design and evaluation of such a model predictive gust load alleviation controller for a flexible test wing. The aeroelastic simulation model is based on a modal description of the structural dynamics and aerodynamic strip theory, with its parameters identified from ground vibration and wind tunnel tests. A Kalman filter is designed to estimate structural loads and non-measurable quantities including generalized structural coordinates and wind disturbances from highly noisy wind tunnel measurements. Preview information of upcoming gusts is provided to the controller, enabling feedforward control to compensate for time delays. The formulation can account for actuator limits and maximum allowable loads, ensuring effective operation within the system boundaries. To reduce the computational effort of the controller, Laguerre functions and an efficient soft output constraint formulation are employed. The resulting control system is evaluated in virtual wind tunnel tests based on the identified model with gust encounters of varying frequency. Further, the effects of degraded actuator limits and failure cases are investigated. Particular emphasis is placed on encounters with short and load-critical gusts, where the controller achieves good load alleviation performance despite restrictive system limitations.
Flying-wing aircraft are more susceptible to wind disturbance due to their smaller wing loading, making gust alleviation critical for flight performance and safety. Conventional control surfaces may exhibit insufficient manipulation efficiency on such configurations, motivating the adoption of active flow control, particularly circulation control (CC) due to its favorable control efficiency. This paper presents an Active Disturbance Rejection Control (ADRC) framework for gust load alleviation (GLA) of flying-wing aircraft equipped with CC actuators, which enables real-time estimation and compensation of both gust disturbance and practical uncertainties and is validated through closed-loop wind-tunnel experiments under various sinusoidal gust conditions. An unsteady aerodynamic model with experimental data is established and simulations are performed for further investigation of alleviation performance and response characteristics under a wide range of gust conditions. Results show that both ADRC and PID exhibit degraded performance at higher gust frequencies and larger gust ratios, but ADRC achieves higher alleviation efficiency across the tested conditions. Furthermore, ADRC maintains satisfactory performance with actuator delays up to 0.04 s and outperforms PID under measurement noise and Dryden turbulence. These findings validate the effectiveness and robustness of ADRC for GLA, underscoring its practical potential for active flow control systems.
Xueqi Liao, Weilin Zhang, Zhiwei Shi et al.· Aerospace· 0 citations
This study proposes a robust model predictive controller (RMPC) for the 5 MW fatigue, aerodynamics, structures, and turbulence (FAST) model, a popular wind turbine model developed by the National Renewable Energy Laboratory, to cope with fluctuations and uncertainties associated with the wind more effectively than existing controllers, such as the standard MPC. In line with other model‐based controllers, the proposed RMPC relies on linear control design models obtained by linearizing the high‐fidelity nonlinear aeroelastic FAST model. As with standard MPC, no prior information on the wind speed is given, and the wind speed is considered a disturbance. Robustness against wind disturbances and modeling errors that arise between the control design model and the process (i.e., the simulation model) is ensured by computing a feedback law and a robust invariant set through linear matrix inequalities. The resulting feedback control law is incorporated into the standard MPC, yielding the RMPC, which is thoroughly tested and analyzed by application to the FAST model, compared to the standard MPC, PI control, and light detection and ranging (LiDAR) based feedforward MPC under various realistic wind conditions. The results demonstrate that the proposed controller, which operates without relying on LiDAR measurements, achieves performance nearly comparable to that of a feedforward controller that relies on costly LiDAR technology.
Visakamoorthi Balasubramani, Sung-ho Hur· Optimal control applications...· 0 citations
Gust disturbances, dynamic vertical inflow and ground effect are key adverse aerodynamic phenomena that induce variations in the forces acting on a multirotor and complicate its flight control. Miniature rotorcraft typically rely on simplified modelling of such effects to compute adjustments in thrust to counteract these forces. In the most basic case, disturbance force estimations are derived from the aircraft's motion and the generated thrust is assumed to exactly match that requested by the controller. However, such systems rely on the aircraft's trajectory to be affected before disturbances can be sensed and compensated. Numerous approaches presented over the last 15-20 years aim to reject external disturbances more quickly, but challenges remain. This paper presents a new approach in this category by measuring the instantaneous force of the rotors directly at the point of generation using load-cells and implementing high-speed control to accurately track the desired thrust. Measurements from load-cells were previously considered too noisy to provide meaningful input, but the experiments presented in the paper using purpose-built hardware from low-cost commodity components in single- and dual rotor see-saw models and a flying aircraft demonstrate both the feasibility and the effectiveness of the approach in the presence of complex aerodynamic phenomena.
Peter Böhm, Michael Brünig, Peyman Z. Moghadam et al.· 0 citations
Existing Sparse Identification of Nonlinear Dynamics (SINDy)–based flight-control studies for fixed-wing aircraft have offered limited treatment of longitudinal–lateral coupling and explicit disturbance estimation, while nonlinear model predictive control remains highly sensitive to aerodynamic-model fidelity. This study develops a physics-informed SINDy framework and an integrated nonlinear model predictive control (NMPC) architecture with a supertwisting sliding-mode disturbance observer for fixed-wing unmanned aerial vehicles. Known physical terms, including gravity and thrust, are retained, whereas uncertain aerodynamic components are identified from limited, noisy flight data and embedded consistently in both the NMPC predictor and the observer. High-fidelity flight simulations using a wind-tunnel-derived ground-truth aerodynamic model are used for validation. Over the disturbance-active interval, the proposed controller reduces trajectory root-mean-square errors by factors of 2–3 relative to controllers without a disturbance observer. Compared with a least-squares-model-based controller equipped with a linear extended state observer, the proposed framework reduces the average root-mean-squared error by 20.6% over the full trajectory and by 14.9% over the disturbance-active interval.
Unknown authors· Journal of Aerospace Informa...· 0 citations