Robust LCEKF for Mismatched Nonlinear Systems with Non-Additive Noise/Inputs and Its Application to Robust Vehicle Navigation

It is well known that the standard state estimation technique performance is particularly sensitive to perfect system knowledge, where the underlying assumptions are: (i) Process and measurement functions and parameters are known, (ii) inputs are known, and (iii) noise statistics are known. These are rather strong assumptions in real-life applications; therefore, a robust filtering solution must be designed to cope with model misspecifications. A possible way to design robust filters is to exploit linear constraints (LCs) within the filter formulation. In this contribution we further explore the use of LCs, derive a linearly constrained extended Kalman filter (LCEKF) for systems affected by non-additive noise and system inputs, and discuss its use for model mismatch mitigation. Numerical results for a robust tracking and navigation problem are provided to show the performance improvement of the proposed LCEKF, with respect to state-of-the-art techniques, that is, a benchmark EKF without mismatch and a misspecified EKF not accounting for the mismatch.

Maximum Correntropy Based Unscented Particle Filter for Cooperative Navigation with Heavy-Tailed Measurement Noises

In this paper, a novel robust particle filter is proposed to address the measurement outliers occurring in the multiple autonomous underwater vehicles (AUVs) based cooperative navigation (CN). As compared with the classic unscented particle filter (UPF) based on Gaussian assumption of measurement noise, the proposed robust particle filter based on the maximum correntropy criterion (MCC) exhibits better robustness against heavy-tailed measurement noises that are often induced by measurement outliers in CN systems. Furthermore, the proposed robust particle filter is computationally much more efficient than existing robust UPF due to the use of a Kullback-Leibler distance-resampling to adjust the number of particles online. Experimental results based on actual lake trial show that the proposed maximum correntropy based unscented particle filter (MCUPF) has better estimation accuracy than existing state-of-the-art robust filters for CN systems with heavy-tailed measurement noises, and the proposed MCUPF has lower computational complexity than existing robust particle filters.

An Embedded Vision System for RoboCup

In this chapter, the authors describe their vision system used in the Standard Platform League (SPL), one of the official leagues in RoboCup competition. The characteristics of SPL are very demanding, as all the processing must be done on board, and the changeable environment requires powerful methods for extracting information and robust filters. The purpose is to show a vision system that meets these goals. The chapter describes the architecture of the authors’ system as well as the flowchart of the image process, which is designed in such a manner that allows a rapid and reliable calibration. The authors deal with field features detection by finding intersections between field lines at frame rate, using a fuzzy-Markov localisation technique. Also, the methods implemented to recognise the ball and goals are explained.

Internal Model Control for Dynamic Systems With Preceded Backlash

A discrete-time internal model control approach for nonsmooth nonlinear systems described by a pseudo-Hammerstein model with backlash is presented. In this method, the controlled system is described by the pseudo-Hammerstein model, and the corresponding inverse model is constructed. Considering the existence of the model mismatch, the internal model control is implemented. As the model is switched among the different operating zones, the piecewise robust filters are proposed to improve the robust stability and transient performance of the control system. Finally, the simulation results based on the proposed method for a mechanical transmission system are presented.