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In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to as the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly; however, a large adaptive gain can lead to high-frequency oscillations which can adversely affect the robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient robustness. A damping term (v) is added in the modification to increase damping as needed. Simulations were conducted on a damaged F-18 aircraft (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) with both the standard baseline dynamic inversion controller and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model.

This paper presents an L 1 adaptive control augmentation system design for multi-input multi-output nonlinear systems in the presence of unmatched uncertainties which may exhibit significant cross-coupling effects. A piecewise continuous adaptive law is adopted and extended for applicability to multi-input multi-output systems that explicitly compensates for dynamic cross-coupling. In addition, explicit use of high-fidelity actuator models are added to the L 1 architecture to reduce uncertainties in the system. The L 1 multi-input multi-output adaptive control architecture is applied to the X-29 lateral/directional dynamics and results are evaluated against a similar single-input single-output design approach.

This paper focuses on the development of an intelligent control technology for in-flight drag reduction. The system is integrated with and demonstrated on the full X-48B nonlinear simulation. The intelligent control system utilizes a peak-seeking control method implemented with a time-varying Kalman filter. Performance functional coordinate and magnitude measurements, or independent and dependent parameters respectively, are used by the Kalman filter to provide the system with gradient estimates of the designed performance function which is used to drive the system toward a local minimum in a steepestdescent approach. To ensure ease of integration and algorithm performance, a single-input single-output approach was chosen. The framework, specific implementation considerations, simulation results, and flight feasibility issues related to this platform are discussed.