Vision-Based Nonlinear Incremental Control for a Morphing Wing With Mechanical Imperfections

Abstract

Morphing structures have acquired much attention in the aerospace community because they enable an aircraft to actively adapt its shape during flight, leading to fewer emissions and fuel consumption. Researchers have designed, manufactured, and tested a morphing wing named SmartX-Alpha, which can actively alleviate loads while achieving the optimal lift distribution. However, the widely existing mechanical imperfections can degrade the performance of the morphing wing and even lead to instabilities. To tackle these issues, this article proposes a vision-based adaptive control approach to actively compensate for mechanical imperfections. In this approach, an incremental model is constructed online to identify the system dynamics using servo commands and vision measurements, and then, nonlinear dynamic inversion control is applied based on the identified model. This data-driven control approach with visual feedback has been validated by real-world experiments on the SmartX-Alpha. The results demonstrate that the vision-based system combined with the proposed control methodology can actively compensate for mechanical imperfections with minimal adjustments to the actual system design. Compared to a controller that only uses a feedforward input-output mapping, this proposed approach improves the system performance and decreases the tracking errors by more than 62% despite disturbances. The results collectively demonstrate the effectiveness of Manuscript

On-Line Black-Box Aerodynamic Performance Optimization for a Morphing Wing With Distributed Sensing and Control

“…The Lyapunov-based stability and robustness proofs of the proposed control method are presented in our recent publication [41]. Real-world experiment results show that this nonlinear adaptive controller is able to decrease the tracking error by more than 62% despite model uncertainties, external disturbances, frictions, and nonlinear backlash hysteresis [41].…”

“…It is noteworthy that this nonlinear adaptive control is superior to the incremental nonlinear dynamic inversion control in the literature [40] mainly because of its abilities of online adaptation and dealing with hysteresis effects [41]. The Lyapunov-based stability and robustness proofs of the proposed control method are presented in our recent publication [41].…”

“…It is noteworthy that this nonlinear adaptive control is superior to the incremental nonlinear dynamic inversion control in the literature [40] mainly because of its abilities of online adaptation and dealing with hysteresis effects [41]. The Lyapunov-based stability and robustness proofs of the proposed control method are presented in our recent publication [41]. Real-world experiment results show that this nonlinear adaptive controller is able to decrease the tracking error by more than 62% despite model uncertainties, external disturbances, frictions, and nonlinear backlash hysteresis [41].…”

Optimal Control for Distributed Aeroelastic Morphing Structure with Uncertainties and Imperfections

“…Here 𝑘 1 and 𝑘 2 are positive scalar values representing linear slopes and 𝑢 𝑓 + describe the free-play dead-band. In a more challenging scenario, the input 𝑢 produced by the actuator is not only defined by the commanded input, 𝑢 𝑐 but also its velocity 𝑢 𝑐 [21,22]: Such phenomenon, characterizing dependency on the time history of the commanded signal, known as a backlash, was investigated previously for the SmartX-Alpha demonstrator [4,5]. The two types of actuator uncertainties are depicted in Fig.…”

Incremental Nonlinear Control for Aeroelastic Wing Load Alleviation and Flutter Suppression

“…Although these linear control approaches have shown their effectiveness in practice, the resulting controllers only have a guaranteed stability and performance around the linearization point; thus, the additional and tedious gain-scheduling method [7] is required to expand these linear controllers to a wider flight envelope. Furthermore, it is challenging for linear controllers to passively tolerate some specific nonlinearities (i.e., free-play, backlash hysteresis [8], bifurcation [9]), sudden faults in actuators and/or sensors, and structure damage.…”