This paper proposes an incremental nonlinear control method for an aeroelastic system’s gust load alleviation and active flutter suppression. These two control objectives can be achieved without modifying the control architecture or the control parameters. The proposed method has guaranteed stability in the Lyapunov sense and also has robustness against external disturbances and model mismatches. The effectiveness of this control method is validated by wind tunnel tests of an active aeroelastic parametric wing apparatus, which is a typical wing section containing heave, pitch, flap, and spoiler degrees of freedom. Wind tunnel experiment results show that the proposed nonlinear incremental control can reduce the maximum gust loads by up to 46.7% and the root mean square of gust loads by up to 72.9%, while expanding the flutter margin by up to 15.9%.

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This paper proposes an incremental nonlinear control method for aeroelastic sys- tem gust load alleviation and active flutter suppression. These two control objectives can be achieved without modifying the control architecture or the control parameters. The proposed method has guaranteed stability in the Lyapunov sense and also has robustness against external disturbances and model mismatches. The effectiveness of this control method is validated by wind tunnel tests of an active aeroelastic parametric wing apparatus, which is a typical wing section containing heave, pitch, flap, and spoiler degrees of freedom. Wind tunnel experiment results show that the proposed nonlinear incremental control can reduce the maximum gust loads by up to 46.7% and the root mean square of gust loads by up to 72.9%, while expanding the flutter margin by up to 15.9%.@en

The sensor-based Incremental Nonlinear Dynamic Inversion (INDI) control has shown promising robustness in the aerospace research field. This control framework only requires a partial knowledge of plant (control effectiveness) because of its usage of angular accelerations and actuator output measurements. However, there are still un-negligible uncertainties of the control effectiveness model in the flight control system, especially when the aircraft is subjected to structural damage/actuator faults. This paper shows that the conventional INDI control fails to satisfy the sufficient conditions for closed-loop stability in the presence of severe damage. Therefore, this paper also proposes a predictor-based gain adaptive INDI control (named PGA-INDI) which can successfully deal with control effectiveness parametric errors caused by structural damage, actuator faults, and model uncertainties. Various simulations using a public aircraft model have demonstrated the effectiveness of the proposed approach.

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Incremental control, including incremental nonlinear dynamic inversion (INDI) and incremental backstepping (IBS), is a sensor-based control framework that enhances the control robustness by exploiting sensor measurements. Although its effectiveness has been demonstrated on various aerospace systems, the explicit and quantifiable expression for the ultimate bound of the tracking error, as a function of the sampling frequency and perturbation bound, has not been presented. This issue is addressed by the discrete-time domain stability analysis of the incremental control in this paper, which allows convenient yet realistic performance assessment and parameter tuning before performing real-world flight tests. Another challenge faced by the incremental control is that its stability criterion can be violated in severe aircraft fault scenarios, such as the control reversal. Therefore, this paper proposes a discrete-time control-direction-based incremental sliding mode control, denoted as D-INDI-SMC, which has broader applicability than the state-of-the-art incremental control methods. The robustness of D-INDI-SMC against control reversal, sensing errors, model uncertainties, actuator faults, and structural damage has been theoretically proved and numerically demonstrated.@en

This paper presents the development and initial characterization of an active, para- metric wing section with aileron and spoiler control surfaces. This wing section is intended to replace the currently used passive wing section setup and facilitate research in active con- trol techniques of aeroelastic phenomena such as flutter, gust load alleviation, and limit cycle oscillations. Baseline capabilities of the passive setup such as a variable center of mass, pitch axis location and spring stiffnesses are retained, while control surfaces, sensors, a single-board computer, and a mechanism for adjustable aileron free play were includedin the new design. Various system identification tests, such as a ground vibration test, flutter and control reversal speed tests, the actuator frequency response and static aerodynamic interaction between spoiler and aileron, were performed to identify the characteristics of the wing section. Finally, as a proof-of-concept, gust load alleviation tests show the difference between open- and closed-loop gust response when the aileron and spoiler are controlled with PID control.@en

This article exposes that although some sensor-based nonlinear fault-tolerant control frameworks including incremental nonlinear dynamic inversion control can passively resist a wide range of actuator faults and structural damage without requiring an accurate model of the dynamic system, their stability heavily relies on a sufficient condition, which is unfortunately violated if the control direction is unknown. Consequently, it is proved in this article that no matter, which perturbation compensation technique (adaptive, disturbance observer, sliding-mode) is implemented, none of the existing nonlinear incremental control methods can guarantee closed-loop stability. Therefore, this article proposes a Nussbaum function-based adaptive incremental control framework for nonlinear dynamic systems with partially known (control direction is unknown) or even completely unknown control effectiveness. Its effectiveness is proved in the Lyapunov sense and is also verified via numerical simulations of an aircraft attitude tracking problem in the presence of sensing errors, parametric model uncertainties, structural damage, actuator faults, as well as inversed and unknown control effectiveness.

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