The actual performance of model-based pathfollowing methods for Unmanned Aerial Vehicles (UAVs) show considerable dependence on the wind knowledge and on the fidelity of the dynamic model used for design. This work analyzes and demonstrates the performance of an adaptive Vector Field (VF) control law which can compensate for the lack of knowledge of the wind vector and for the presence of unmodelled course angle dynamics. Extensive simulation experiments, calibrated on a commercial fixed-wing UAV and proven to be realistic, show that the new VF method can better cope with uncertainties than its standard version. In fact, while the standard VF approach works perfectly for ideal first-order course angle dynamics (and perfect knowledge of the wind vector), its performance degrades in the presence of unknown wind or unmodelled course angle dynamics. On the other hand, the estimation mechanism of the proposed adaptive VF effectively compensates for wind uncertainty and unmodelled dynamics, sensibly reducing the path-following error as compared to the standard VF.Index Terms-Adaptive Vector Field, fixed-wing UAV, pathfollowing, unmodelled course angle dynamics. I. INTRODUCTIONBorn initially for military applications, Unmanned Aerial Vehicles (UAVs) have nowadays also civil applications, such as monitoring, aerial mapping, small cargo deliveries, search and rescue operations [1]. UAVs must rely on accurate pathfollowing algorithms: wind disturbances, unmodelled dynamics, and the quality of sensing and control, are all critical limits to the achievable accuracy [2]- [5]. Taking into account that UAVs must operate in windy environments where wind speeds are 20-50% of the UAV airspeed, the design of highperformance path-following strategies is compelling.Path-following techniques can be developed using geometric or control-theoretic approaches [6]. The first class include the pure pursuit and line-of-sight guidance laws [7]-[11], which make use of a virtual target point where the UAV is directed to. Control-theoretic techniques include PIDs, linear quadratic control, sliding-mode control, model predictive

This paper discusses the design and software-inthe-loop implementation of adaptive formation controllers for fixed-wing Unmanned Aerial Vehicles (UAVs) with parametric uncertainty in their structure, namely uncertain mass and inertia. In fact, when aiming at autonomous flight, such parameters cannot assumed to be known as they might vary during the mission (e.g. depending on the payload). Modelling and autopilot design for such autonomous fixed-wing UAVs are presented. The modelling is implemented in Matlab, while the autopilot is based on ArduPilot, a popular open-source autopilot suite. Specifically, the ArduPilot functionalities are emulated in Matlab according to the Ardupilot documentation and code, which allows us to perform software-in-the-loop simulations of teams of UAVs embedded with actual autopilot protocols. An overview of realtime path planning, trajectory tracking and formation control resulting from the proposed platform is given. The software-inthe-loop simulations show the capability of achieving different UAV formations while handling uncertain mass and inertia.Index Terms-Fixed-wing UAVs, ArduPilot, adaptive formation control, software-in-the-loop simulations.

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Heating, ventilation and air-conditioning (HVAC) units in buildings form a system-of-subsystems entity that must be accurately integrated and controlled by the building automation system to ensure the occupants’ comfort with reduced energy consumption. As control of HVACs involves a standardized hierarchy of high-level set-point control and low-level Proportional-Integral-Derivative (PID) controls, there is a need for overcoming current control fragmentation without disrupting the standard hierarchy. In this work, we propose a model-based approach to achieve these goals. In particular: the set-point control is based on a predictive HVAC thermal model, and aims at optimizing thermal comfort with reduced energy consumption; the standard low-level PID controllers are auto-tuned based on simulations of the HVAC thermal model, and aims at good tracking of the set points. One benefit of such control structure is that the PID dynamics are included in the predictive optimization: in this way, we are able to account for tracking transients, which are particularly useful if the HVAC is switched on and off depending on occupancy patterns. Experimental and simulation validation via a three-room test case at the Delft University of Technology shows the potential for a high degree of comfort while also reducing energy consumption.