Integration of Adaptive Estimation and Adaptive Control Design for Uncertain Nonlinear Systems
Sattigeri, Ramachandra J.
Calise, Anthony J.
Kim, Byoung Soo
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This paper presents a method to integrate adaptive estimation and adaptive control designs for a class of uncertain nonlinear systems having both parametric uncertainties and unmodeled dynamics. The method is based on Lyapunov-like stability analysis of all the errors in the closed-loop system. The adaptive estimator considered is a linear, time-varying Kalman filter augmented by the output of an observer neural network. The observer neural network compensates the nominal Kalman filter for modeling errors. The estimated states are used in the construction of an adaptive control solution that is based on approximate feedback linearization augmented with the outputs of an adaptive neural network controller. The presented approach is then applied to a vision-based formation flight control problem. The objective is for a follower aircraft to maintain range from a maneuvering leader aircraft using a monocular fixed camera for passive sensing of the leader's relative motion. In the implementation, the states of the adaptive estimator are estimates of line-of-sight variables and the outputs of the observer neural network are estimates of the leader acceleration. The adaptive control solution considered is an integrated guidance and control design that includes online adaptation to unmodeled nonlinearities such as the unknown leader aircraft acceleration and parametric uncertainties in the own-aircraft aerodynamic derivatives. Simulation results using a nonlinear 6DOF simulation model of a fixed-wing UAV are presented to illustrate the feasibility and efficacy of the approach.