Fuzzy Modeling and Parallel Distributed Compensation for Aircraft Flight Control from Simulated Flight Data

A method is described that combines fuzzy system identification techniques with Parallel Distributed Compensation (PDC) to develop nonlinear control methods for aircraft using minimal a priori knowledge, as part of NASAs Learn-to-Fly initiative. A fuzzy model was generated with simulated flight data, and consisted of a weighted average of multiple linear time invariant state-space cells having parameters estimated using the equation-error approach and a least-squares estimator. A compensator was designed for each subsystem using Linear Matrix Inequalities (LMI) to guarantee closed-loop stability and performance requirements. This approach is demonstrated using simulated flight data to automatically develop a fuzzy model and design control laws for a simplified longitudinal approximation of the F-16 nonlinear flight dynamics simulation. Results include a comparison of flight data with the estimated fuzzy models and simulations that illustrate the feasibility and utility of the combined fuzzy modeling and control approach

Design of a nonlinear dynamic inversion controller for trajectory following and maneuvering for fixed wing aircraft

This paper presents the design of a robust linear controller that can be used for trajectory following and maneuvering of fixed-wing aircraft using Nonlinear Dynamic Inversion (NDI) principles. The design addresses control coupling to exploit multiple redundant controls. It can also be easily extended to state decoupling. The design procedure exploits the nature of the equations of motion written in the wind axis resulting in a cascaded linear controller structure with inner and outer loops. A systematic methodology is evolved which uses only the relevant stability and control derivatives in the control synthesis, as opposed to the inversion of the complete nonlinear equations used in conventional NDI designs. The tuning of the control gains is based on the requirements of adequate trajectory following and robustness to control surface failures. Finally, it is shown how a series of controllers can be derived depending on the sensor complement available on the aircraft. The proposed approach is ideal for fixed-wing Unmanned Aerial Vehicles (UAVs)