The focus of this paper is Bayesian state and parameter estimation using nonlinear models. A recently developed method, the particle filter, is studied that is based on stochastic simulation. Unlike the well-known extended Kalman filter, the particle filter is applicable to highly nonlinear models with non-Gaussian uncertainties. Recently developed techniques that improve the convergence of the...

This paper presents first and second order convergence analysis of the sparsity aware l0-RLS adaptive filter. The theorems 1 and 2 state the steady state value of mean and mean square deviation of the adaptive filter weight vector.

The two step filter is applied to process intersatellite radar measurements to determine the motion of one satellite relative to another in close elliptical orbits. This filter breaks a nonlinear estimation problem into two state vectors. The “first step” state is chosen so as to have a linear measurement equation. This is nonlinearly related to the “second step” state which describes the dynam...

this paper presents a simple model predictive control (mpc) approach to control the power injection system (pis) for renewable energy applications. a dc voltage source and a single-phase inverter that is connected to the grid by an lcl filter form the pis. grid voltage is considered a disturbance for the system. for eliminating this disturbance, a modified model is proposed. it is usual to cont...

In this paper the problems of state estimation, tracking control and shape control in a micro-cantilever beam with nonlinear electrostatic actuation are investigated. The system’s partial differential equation of motion is converted into a set of ordinary differential equations by projection method. Observabillity of the system is proven and a state estimation system is designed using extended ...