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Reconfigurable Autopilot Design using Nonlinear Model Predictive Control: Application to High Performance and Autonomous Aircraft

The work presented in this thesis examines several aspects of Nonlinear Model Predictive Control (NMPC) that display and confirm its promising potentials as a powerful reconfigurable control scheme. The effects of significant nonlinearities and the intrinsically unstable nature of high performance fighter aircraft, among other challenges, have been shown to be well handled in the NMPC framework. This work illustrates how complex control and stability augmentation measures (which are normally realized through ad hoc mode switching strategies) can be formulated and implemented as NMPC objectives and constraints. Further suggestions on robustness strategies for model/plant mismatch and compensation for coupling effects which are not properly accounted for, have been presented and examined in this work. Results on fault tolerance of NMPC are also presented and discussed in this thesis. In this direction, NMPC has been shown to have unique inherent fault detection capabilities due to its effective utilization of feedback and its internal model predictions. Different types of actuator/control surface failures, including extreme cases of total actuator failure are examined as test cases for the NMPC reconfigurable fault tolerant control scheme developed in this work. The NMPC autopilots are designed for an F-16 fighter aircraft, and the implementation and simulations were done using ACADO nonlinear optimization solver, interfaced with the MATLAB/Simulink environment.