Smart Vortex Generators (SVGs) are used in a CFD study for control of loads on aeroelastic wings governed by mass-damper-spring structural model in the presence of unsteady wind gust disturbances, leveraging interpretable reduced-order system identification. The primary objective of the study is thus gust load alleviation (GLA). Unlike prior formulations, the dominant disturbance, such as a wind gust, is included explicitly as a control input within the model, allowing the MPC controller to respond adaptively and in anticipation to external system forcing. System dynamics are identified partly by Sparse Identification of Nonlinear Dynamics with Control (SINDYc) and Linear Parameter-Varying (LPV) system. SINDYc captures the wing-gust subsystem, while LPV offers physics-interpretable and sparse representation of the nonlinear aerodynamic effects associated with the different operating regimes of the SVGs—vortex generator and massive flow separation. Two types of nonlinearity are observed. Firstly, nonlinearities affecting amplitude and non-minimum-phase-like behaviour of the induced loads can be captured through one scheduling parameter—the SVG deflection angle. The second type of identified nonlinearity concerns the different GLA capability of SVGs in the presence of gusts with varying magnitude. This can be captured by second scheduling parameter—the SVG deflection angle multiplied by the effective angle of attack, identified by SINDYc. Once fully coupled reduced-order fluid-structure interaction dynamical model is constructed, model-predictive control (MPC) is deployed in order to study the maximum potential GLA capability of SVGs. It has been shown in previous studies that simple on/off control strategy induces secondary structural oscillations, resulting in more structural fatigue. It is shown here that optimal control strategy can mitigate these to an extent. MPC-controlled SVGs can best alleviate loads of low frequency high amplitude gusts, by up to ΔCL ≈ 0.23, while high frequency gusts require head start for SVG actuation for optimal GLA performance. In summary, the findings of this thesis demonstrate that SVGs constitute a viable candidate technology for active gust load alleviation when deployed together with optimal control framework.

This paper presents a novel corrective algorithm bridging the gap between simulation and reality by online fine-tuning an offline pre-trained deep reinforcement learning agent. The novel control architecture is inspired by the incremental model-based heuristic dynamic programming, which is described together with the basics of reinforcement learning first. This novel control architecture is applied in an illustrative control environment. It was found that the corrective algorithm can help reach the desired reference state in an environment governed by moderately different dynamics from those used during pre-training of the reinforcement learning agent.