Investigation of Drive-Reinforcement Learning and Application of Learning to Flight Control

This report describes results obtained during a multiphase research program having the broad aim of investigating the application of learning systems to automatic control in general, and to flight control in particular. The first phase analyzed the drive-reinforcement learning paradigm and examined its application to automatic control, with mixed results. The second phase compared a number of alternative strategies for learning augmented control, and resulted in the conception of a new hybrid adaptive/learning control scheme. Subsequently, in the third phase, this hybrid control approach was more fully developed and applied to several nonlinear dynamical systems, including a cart- pole system, aeroelastic oscillator, and a three-degree-of-freedom aircraft. The fourth phase revisited drive-reinforcement learning from the point of view of optimal control and successfully applied a version embedded in the associative control process architecture to regulate an aeroelastic oscillator. The fifth phase examined the problem of learning augmented estimation, and resulted in the development of a preliminary estimation scheme consistent with the hybrid control approach. In the sixth and final phase, the hybrid control methodology was applied to a nonlinear, six-degree-of-freedom flight control problem, and then demonstrated via a challenging multiaxis maneuver. ACP Network, Drive- reinforcement, Reinforcement learning, Adaptive control, Hybrid control, Nonlinear control, Aircraft flight control, Learning control, Optimal control.