Upper-bound-based state estimation with large-time-delay measurement and its applications to motion control

Abstract

In many motion control applications, it is essential to handle a delayed measurement to estimate the unknown states correctly. However, a literature review shows that difficulties still remain if the delay time is quite large in comparison with the period of the control input. For instance, some methods need to increase the size of the original system considerably. This paper describes a novel filter for a linear system with a large-time-delay measurement. By utilizing the norm-bound scheme based on a matrix inequality, the upper-bound of the estimation error covariance is obtained, and the filter gain can be computed easily. The proposed algorithm is almost similar to the standard Kalman filter without any increase in the dimension of the system. The algorithm is developed for two cases: Case 1: considering only a delayed measurement and Case 2: considering the fusion of delayed and nondelayed measurements. Two motion control applications are performed to verify the algorithm: sideslip angle estimation for vehicle motion control and target position estimation for visual servo control.