Reinforcement Solver for H-infinity Filter with Bounded Noise
Abstract
H-infinity filter has been widely applied in engineering field, but copping with bounded noise is still an open problem and difficult to solve. This paper considers the H-infinity filtering problem for linear system with bounded process and measurement noise. The problem is first formulated as a zero-sum game where the dynamic of estimation error is non-affine with respect to filter gain and measurement noise. A nonquadratic Hamilton-Jacobi-Isaacs (HJI) equation is then derived by employing a nonquadratic cost to characterize bounded noise, which is extremely difficult to solve due to its non-affine and nonlinear properties. Next, a reinforcement learning algorithm based on gradient descent method which can handle nonlinearity is proposed to update the gain of reinforcement filter, where measurement noise is fixed to tackle non-affine property and increase the convexity of Hamiltonian. Two examples demonstrate the convergence and effectiveness of the proposed algorithm.
Cite
@article{arxiv.2008.00674,
title = {Reinforcement Solver for H-infinity Filter with Bounded Noise},
author = {Jie Li and Shengbo Eben Li and Kaiming Tang and Yao Lv and Wenhan Cao},
journal= {arXiv preprint arXiv:2008.00674},
year = {2020}
}