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The aim of this work is to apply an innovative adaptive ℒ1 techniques to control flutter phenomena affecting highly flexible wings and to evaluate the efficiency of this control algorithm and architecture by performing the following tasks: i) adaptation and analysis of an existing simplified nonlinear plunging/pitching 2D aeroelastic model accounting for structural nonlinearities and a quasi-steady aerodynamics capable of describing flutter and post-flutter limit cycle oscillations, ii) implement the ℒ1 adaptive control on the developed aeroelastic system to perform initial control testing and evaluate the sensitivity to system parameters, and iii) perform model validation and calibration by comparing the performance of the proposed control strategy with an adaptive back-stepping algorithm. The effectiveness and robustness of the ℒ1 adaptive control in flutter and post-flutter suppression is demonstrated.

Innovative aircraft design studies have noted that uncertainty effects could become significant and greatly emphasized during the conceptual design phases due to the scarcity of information about the new aero-structure being designed. The introduction of these effects in design methodologies are strongly recommended in order to perform a consistent evaluation of structural integrity. The benefit to run a Robust Optimization is the opportunity to take into account uncertainties inside the optimization process obtaining a set of robust solutions. A major drawback of performing Robust Multi-Objective Optimization is the computational time required. The proposed research focus on the reduction of the computational time using mathematic and computational techniques. In the paper, a generalized approach to operate a Robust Multi-Objective Optimization (RMOO) for Aerospace structure using MSC software Patran/Nastran to evaluate the Objectives Function, is proposed.

A Multi-Objective Optimization (MOO) problem concerning the thermal control problem of Multifunctional Structures (MFSs) is here addressed. In particular the use of Multi-Objective algorithms from an optimization tool and Self-Organizing Maps (SOM) is proposed for the identification of the optimal topological distribution of the heating components for a multifunctional test panel, the Advanced Bread Board (ABB). MFSs are components that conduct many functions within a single piece of hardware, shading the clearly defined boundaries that identify traditional subsystems. Generally speaking, MFSs have already proved to be a disrupting technology, especially in aeronautics and space application fields. The case study exploited in this paper refers to a demonstrator breadboard called ABB. ABB belongs to a particular subset of an extensive family of MFS, that is, of thermo-structural panels with distributed electronics and a health monitoring network.

The future revolution of the air traffic system imposes the development of a new class of Flight Management Systems (FMS), capable of providing the aircraft with real-time reference flight parameters, necessary to fly the aircraft through a predefined sequence of waypoints, while minimizing fuel consumption, noise and pollution emissions. The main goal is to guarantee safety operations while reducing the aircraft environmental impact, according to the main international research programs. This policy is expected to affect also the Unmanned Aerial Systems (UASs), as soon as they will be allowed to fly beyond the restricted portions of the aerospace where they are currently confined. In the future, in fact, UASs are expected to fly within the whole civilian airspace, under the same requirements deriving from the adoption of the Performance Based Navigation (PBN).

Nautilus S.p.A. and the Polytechnic of Turin, in cooperation with Blue Engineering, have developed a very versatile product, the ELETTRA Twin Flyers [6] (ETF), which consists in a very innovative remotely-piloted airship equipped with high precision sensors and communication devices. This multipurpose platform is particularly suitable for border and maritime surveillance missions and for telecommunication, both in military and civil area. To assess the actual maneuver capabilities of the airship [14], a prototype of reduced size and complexity has been assembled [16]. Before the flight tests a further assessment on the flight simulator is needed, because the first version of the software is tuned on the full scale prototype. Steady state performance and static stability of the demonstrator have been evaluated with CFD analysis.

This paper presents a numerical methodology to generate lookup tables that provide d- and q-axis stator current references for the control of electric motors. The main novelty with respect to other literature references is the introduction of the iron power losses in the equivalent-circuit electric motor model implemented in the optimization routine. The lookup tables generation algorithm discretizes the motor operating domain and, given proper constraints on maximum stator current and magnetic flux, solves a numerical optimization problem for each possible operating point to determine the combination of d- and q- axis stator currents that minimizes the imposed objective function while generating the desired torque. To demonstrate the versatility of the proposed approach, two different variants of this numerical interpretation of the motor control problem are proposed: Maximum Torque Per Ampere and Minimum Electromagnetic Power Loss.