Secure State Estimation for Nonlinear Systems Under Sparse Attacks With Application to Robotic Manipulators
Xiaoyu Guo, Chenliang Wang, Zhen Dong, Zhengtao Ding
Abstract
Secure state estimation against sparse injection attacks and disturbances is a challenging problem of both theoretical and practical importance, and existing results mainly focus on linear systems despite many practical systems being nonlinear. In this article, a novel secure state estimation scheme is proposed for a class of nonlinear systems with application to robotic manipulators. A kind of high-gain K-filters is constructed to estimate the unmeasured states, which can attenuate the disturbances to an arbitrary level. Moreover, a monitoring function and a switching scheme are introduced, which successfully preclude attacked measurements after a finite number of switchings. With these efforts, the proposed estimation scheme steers the estimation error into a residual set which can be made arbitrarily small by properly choosing some design parameters, regardless of the disturbances and possibly unbounded attacks. Both simulation and experimental results on a robotic manipulator demonstrate the effectiveness of the proposed method.