Despite their ability to operate on the limits of performance, handle multivariable and nonlinear systems, and offer online adaptation and reconfiguration capabilities, model predictive control approaches to aerospace applications suffer from limitations related to the online computational burden and complexity of the underlying optimization problem. This paper focuses on quadratic programming (QP) formulations that represent certain types of predictive flight control problems, and proposes a parallelizable QP solver based on operator-splitting and fast gradient methods. The presented methodology and solution approach promise real-time implementation of QP-based predictive flight control schemes on future embedded platforms. This paper also provides formal analysis and guidelines on how to reshape the feasible region of the model predictive control problem at each problem instance to ensure recursive feasibility and closed-loop stability. Finally, simulation results for the longitudinal control of an Airbus passenger aircraft are presented to show how the obtained computable certificates can be simplified in practice and bring the proposed approach a step closer to future on-board real-time implementation.
|Pages (from-to)||265 - 277|
|Journal||Journal of Guidance, Control, and Dynamics: devoted to the technology of dynamics and control|
|Publication status||Published - 2017|