Comparison of Intelligent, Adaptive, and Nonlinear Flight Control Laws

Abstract

Seven different nonlinear control laws for multiaxis control of a high-performance aircraft are compared in simulation. The control law approaches are fuzzy logic control, backstepping adaptive control, neural network augmented control, variable structure control, and indirect adaptive versions of model predictive control and dynamic inversion. In addition, a more conventional scheduled dynamic inversion control law is used as a baseline. In some of the cases, a stochastic genetic algorithm was used to optimize e xed parameters during design. The control laws are demonstrated on a six-degree-of-freedom simulation with nonlinear aerodynamic and engine models, actuator models with position and rate saturations, and turbulence. Simulation results include a variety of single- and multiple-axis maneuvers in normal operation and with failures or damage. The specie c failure and damage cases that are examined include single and multiple lost surfaces, actuator hardovers, and an oscillating stabilatorcase. Therearealso substantial differences between thecontrol law design and simulation models,which are used to demonstrate some robustness aspects of the different control laws.