Adaptive Estimation for Control of Uncertain Nonlinear Systems with Applications to Target Tracking

Adaptive estimation;Neural networks;Extended Kalman filters;Missile-target tracking;Obstacle avoidance;Adaptive estimation for control

Madyastha, Venkatesh ; Aerospace Engineering

University:Georgia Institute of Technology

Department:Aerospace Engineering

关键词: Adaptive estimation;    Neural networks;    Extended Kalman filters;    Missile-target tracking;    Obstacle avoidance;    Adaptive estimation for control;   

Others  :  https://smartech.gatech.edu/bitstream/1853/7567/1/madyastha_venkatesh_k_200512_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Design of nonlinear observers has received considerable attention since the early development of methods for linear state estimation. The most popular approach is the extended Kalman filter (EKF), that goes through significant degradation in the presence of nonlinearities, particularly if unmodeled dynamics are coupled to the process and the measurement. For uncertain nonlinear systems, adaptive observers have been introduced to estimate the unknown state variables where no priori information about the unknown parameters is available. While establishing global results, these approaches are applicable only to systems transformable to output feedback form. Over the recent years, neural network (NN) based identification and estimation schemes have been proposed that relax the assumptions on the system at the price of sacrificing on the global nature of the results. However, most of the NN based adaptive observer approaches in the literature require knowledge of the full dimension of the system, therefore may not be suitable for systems with unmodeled dynamics. We first propose a novel approach to nonlinear state estimation from the perspective of augmenting a linear time invariant observer with an adaptive element. The class of nonlinear systems treated here are finite but of otherwise unknown dimension. The objective is to improve the performance of the linear observer when applied to a nonlinear system. The approach relies on the ability of the NNs to approximate the unknown dynamics from finite time histories of available measurements. Next we investigate nonlinear state estimation from the perspective of adaptively augmenting an existing time varying observer, such as an EKF. EKFs find their applications mostly in target tracking problems. The proposed approaches are robust to unmodeled dynamics, including unmodeled disturbances. Lastly, we consider the problem of adaptive estimation in the presence of feedback control for a class of uncertain nonlinear systems with unmodeled dynamics and disturbances coupled to the process.The states from the adaptive EKF are used as inputs to the control law, which in target tracking usually takes the form of a guidance law. The applications of this approach lie in the areas of missile-target tracking, formation flight control and obstacle avoidance.