Rotorcraft Trim by a Neural Model-Predictive Auto-Pilot

Modern comprehensive finite element-based tools for the aeromechanic analysis of rotorcraft require the ability of accurately computing the model trim settings. Proportional control laws (auto-pilots) have often been used in many practical instances, because this technique is not directly related to the complexity of the system. On the other hand, classical auto-pilots must be carefully tuned for every desired flight condition. This work focuses on improving the auto-pilot technique by means of non-linear model-predictive control. A reference model of the system augmented with an adaptive neural element is used to predict the system response and solve an optimal control problem, which in turn produces the control strategy that is used for regulating the system. The adaptive element allows for the identification and correction of the mismatch between reduced model and controlled system, thereby improving the predictive capabilities of the controller. Tests on the wind-tunnel trim of a rotor multibody model and comparisons with an existing implementation of a classical auto-pilot are discussed.