Neurocontrol design and analysis for a multivariable aircraft control problem

The feasibility of using artificial neural networks as control systems for modern, complex aerospace vehicles is investigated via an aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model-following controller using A/oo control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function that is a weighted sum of tracking errors and control input commands and rates. To gain insight into the neurocontrol design, linearized representations of the neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline //<x based controller. Robustness issues of the neurocontrol design are identified and addressed in the context of neural training. Future areas of research are identified to enhance the practical applicability of neural networks to flight control design.