Derivative-Free Observability Analysis for Sensor Placement Optimization of Bioinspired Flexible Flapping Wing System.

Observability analysis of a bioinspired flexible flapping wing system provides a measure of how well the states of flexible flapping wing micro-aerial vehicles can be estimated from real-time measurements during high-speed flight. However, the traditional observability analysis approaches have trouble in terms of lack of quantitative analysis index, high computational complexity, low accuracy, and unavailability in stochastic systems with memory, including bioinspired flexible flapping wing systems. Therefore, a novel derivative-free observability analysis method is proposed here based on the generalized polynomial chaos expansion. By formulating a surrogate model to represent the relationship between the cumulative measurement and the random initial state, the observability coefficient matrix is calculated and the observability rank condition is stated. Consequently, several observability indices are proposed to quantity the observability of the system. Altogether, the proposed method avoids the disadvantages of the traditional approaches, especially in assessing the observability degree of each state and the effect of stochastic noise on observability. The validation of the proposed method is first provided by demonstrating the equivalence between the traditional and proposed methods and subsequently by comparing the observability of the Lorenz system calculated via three different approaches. Finally, the proposed method is applied on a bioinspired flexible wing system to optimize the placement of sensors, which is consistent with the natural configuration of campaniform sensilla on the wing of the hawkmoth.

Asymmetric Airfoil Morphing via Deep Reinforcement Learning.

Morphing aircraft are capable of modifying their geometry configurations according to different flight conditions to improve their performance, such as by increasing the lift-to-drag ratio or reducing their fuel consumption. In this article, we focus on the airfoil morphing of wings and propose a novel morphing control method for an asymmetric deformable airfoil based on deep reinforcement learning approaches. Firstly, we develop an asymmetric airfoil shaped using piece-wise Bézier curves and modeled by shape memory alloys. Resistive heating is adopted to actuate the shape memory alloys and realize the airfoil morphing. With regard to the hysteresis characteristics exhibited in the phase transformation of shape memory alloys, we construct a second-order Markov decision process for the morphing procedure to formulate a reinforcement learning environment with hysteresis properties explicitly considered. Subsequently, we learn the morphing policy based on deep reinforcement learning techniques where the accurate information of the system model is unavailable. Lastly, we conduct simulations to demonstrate the benefits brought by our learning implementations and validate the morphing performance of the proposed method. The simulation results show that the proposed method provides an average 29.8% performance improvement over traditional methods.