Hypersonic flight conditions produce temperature variations that can alter both the structural dynamics and flight dynamics. These aerothermoelastic effects are modeled by a nonlinear, temperature-dependent, parameter-varying state-space representation. The model includes an uncertain parameter-varying state matrix, an uncertain parameter-varying nonsquare (column-deficient) input matrix, and a nonlinear additive bounded disturbance. A Lyapunov-based continuous robust controller is developed that yields exponential tracking of a reference model, despite the presence of bounded nonvanishing disturbances. Simulation results for a hypersonic aircraft are provided to demonstrate the robustness and efficacy of the proposed controller.

A sufficient condition to solve an optimal control problem is to solve the Hamilton-Jacobi-Bellman (HJB) equation. However, finding a value function that satisfies the HJB equation for a nonlinear systems is challenging. Previous efforts have utilized feedback linearization methods which assume exact model knowledge, or have developed neural network (NN) approximations of the HJB value function. The current effort builds on our previous efforts to illustrate how a NN can be combined with a recent robust feedback method to asymptotically minimize a given quadratic performance index as the generalized coordinates of a nonlinear Euler-Lagrange system asymptotically track a desired time-varying trajectory despite general uncertainty in the dynamics. A Lyapunov analysis is provided to examine the stability of the developed optimal controller.

With the motivation of using more information to update the parameter estimates to achieve improved tracking performance, composite adaptation that uses both the system tracking errors and a prediction error containing parametric information to drive the update laws, has become widespread in adaptive control literature. However, despite its obvious benefits, composite adaptation has not been widely implemented in neural network-based control, primarily due to the neural network (NN) reconstruction error that destroys a typical prediction error formulation required for the composite adaptation. This technical note presents a novel approach to design a composite adaptation law for NNs by devising an innovative swapping procedure that uses the recently developed robust integral of the sign of the error (RISE) feedback method. Semi-global asymptotic tracking is proven for a Euler-Lagrange system. Experimental results are provided to illustrate the concept.

Abstract-An output feedback (OFB) dynamic inversion control strategy is developed for an unmanned aerial vehicle (UAV) that achieves global asymptotic tracking of a reference model. The UAV is modeled as an uncertain linear time-invariant (LTI) system with an additive bounded nonvanishing nonlinear disturbance. A continuous tracking controller is designed to mitigate the nonlinear disturbance and inversion error, and an adaptive law is utilized to compensate for the parametric uncertainty. Global asymptotic tracking of the measurable output states is proven via a Lyapunovlike stability analysis, and high-fidelity simulation results are provided to illustrate the applicability and performance of the developed control law.Index Terms-Adaptive control, dynamic inversion (DI), Lyapunov methods, nonlinear control, robust control.

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Abstract-With the motivation of using more information to update the parameter estimates to achieve improved tracking performance, composite adaptation that uses both the system tracking errors and a prediction error containing parametric information to drive the update laws, has become widespread in adaptive control literature. However, despite its obvious benefits, composite adaptation has not been widely implemented in neural network-based control, primarily due to the neural network (NN) reconstruction error that destroys a typical prediction error formulation required for the composite adaptation. This technical note presents a novel approach to design a composite adaptation law for NNs by devising an innovative swapping procedure that uses the recently developed robust integral of the sign of the error (RISE) feedback method. Semi-global asymptotic tracking is proven for a Euler-Lagrange system. Experimental results are provided to illustrate the concept.

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