Nonlinear flight control

New control techniques have emerged in the field of nonlinear flight control: Nonlinear Dynamic Inversion (NDI) and Backstepping (BKS). These methods are quite similar in their basic ideas but, being derived using different approaches, they have considerable differences in terms of performance and stability characteristics. Although the several advancements on both approaches, all NDI and BKS strategies, being model-based, exhibit the drawback of relying on precise knowledge of the aircraft dynamics. With the development of novel sensor techniques providing information about the angular accelerations, accurate models are no longer required for control design purposes.

Motivated by new sensor mechanisms, incremental NDI and BKS control laws were developed, taking this extended sensor information into account. These new sensor-based controllers use an incremental approach based on computing incremental commands instead of the total control inputs, and employing acceleration feedback (sensor measurements) to extract information relative to aerodynamic changes. This will be the basis of the novel flight control concept addressed in INCEPTION.

Adaptive Augmentation

Adaptive augmentation can be understood in the context of adaptive control. The adaptive control system aims to maintain the performance of the control system remaining close to the nominal performance under uncertainties. In order to achieve the aim, adaptive control generally includes two principal components: a model that approximates the actual uncertainty and adaptive augmentation that augments the nominal controller with the approximate uncertainty model. If the model for the uncertainty has a parametric form, the ideal direction for an adaptation law is to estimate the optimal values of the parameters, which minimize the parameter estimation error. This allows the adaptive element to offset the uncertainty as much as possible.

For better practice of the above-mentioned basic notion of adaptive control in actual implementations, the following specific objectives are desirable to be achieved. First, fast adaptation is advantageous. It is because the control system can quickly compensate for the uncertainty if the parameter convergence speed is high. Second, convergence of the parameters to their ideal values is beneficial. Also, more accurate parameter convergence corresponds to the identification of more accurate model.

Flight Envelope Prediction and Protection

In case of failures, it is highly desirable to characterize the adverse conditions (detect and identify faults, damages) and estimate the achievable flight envelope of the aircraft. Past accidents and incidents proved in a dramatic manner that stabilizing a severely damaged aircraft is a major challenge and modern commercial aircraft, such as the Boeing 777 and the Airbus A380, are equipped with partial Flight Envelope Protection (FEP) systems to protect from stall, exceeding over-speed, limit angle of attack and load factors. However extensive envelope prediction and protection approaches major drawbacks:

  • highly demanding computational effort,
  • only the nominal flight envelope is protected in case of vehicle integrity (no deviations from the nominal model) - no adjustment to degradation caused by failure, damage or other circumstances
  • classical envelope protections are chosen in an often rather conservative way, limiting the available manoeuvring and performance to a subset with reduced margins when compared to the physical capabilities of the vehicle.

Even though the fault detection and envelope protection challenge has already been addressed (FP7 ADDSAFE), the remaining but equally important challenge of predicting the remaining vehicle capabilities and inherent flight envelope has not been addressed. Flying out of the physical, stabilisable envelope makes any controller fail, as a system may never be controlled beyond its limits. Predicting a safe flight envelope under adverse conditions and utilizing it for the planning of safe continuation and return trajectories in real-time still remains an open challenge that will be addressed in INCEPTION.

This project has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation (H2020-MG-2016-Two-Stages) under grant agreement No. 723515. This publication/multimedia product/presentation reflects the views of the author, and the European Union cannot be held responsible for any use which might be made of the information contained therein.