This survey of model-based fault diagnosis focuses on those methods that are applicable to aerospace systems. To highlight the characteristics of aerospace models, generic non-linear dynamical modelling from flight mechanics is recalled and a unifying representation of sensor and actuator faults is presented. An extensive bibliographical review supports a description of the key points of fault detection methods that rely on analytical redundancy. The approaches that best suit the constraints of the field are emphasized and recommendations for future developments in in-flight fault diagnosis are provided.

We solve output- and state-consensus problems for multi-agent high-order systems in feedback form. We consider systems interconnected over arbitrary (connected) undirected-topology networks as well as directed spanning-trees and directed cycles. We assume that the systems may be subject to multiple restrictions in the form of output or state constraints, such as limited-range measurements, physical limitations, etc. In addition, we suppose that the systems may be subject to external disturbances. Under these conditions, we present a control framework and a formal analysis that establishes robust stability in the input-to-state sense. The former relies on a modified backstepping method and the latter on multi-stability theory. Finally, we apply our approach to a case-study of interest in the aerospace industry: safety-aware rendezvous control of underactuated UAVs subject to connectivity and collision-avoidance constraints.

Reusable liquid-propellant rocket engines (LPREs) imply more demanding robustness requirements than expendable ones due to their extended capabilities. Therefore, the goal of this work was to develop a control loop adapted to all the operating phases of LPRE, including transients, and robust to internal parametric variations. Firstly, thermo-fluid-dynamic simulators representative of the gas-generator-cycle engines were built. These simulators were subsequently translated into nonlinear state-space models. Based on these models, the continuous subphase of the start-up transient is controlled to track precomputed reference trajectories. Beyond the start-up, throttling scenarios are managed with end-state-tracking algorithm. Model Predictive Control has been applied in a linearised manner with robustness considerations to both scenarios, in which a set of hard state and control constraints must be respected. Tracking of pressure (thrust) and mixture-ratio operating points within the design envelope is achieved in simulation while respecting constraints. Robustness to variations in the predominant parameters, to external state perturbations and to the possible impact of an observer on the loop, is demonstrated.