Unknown input and state estimation of nonlinear systems using a multiple model approach
نویسندگان
چکیده
This paper presents a new recursive filter to joint input and state estimation for noisy discrete time Takagi-Sugeno (T-S) fuzzy models. For each local linear model one local filter is designed using Kalman filter theory. Steady state and unknown input solutions can be found for each of the local filters. The global filter is a linear combination of linear filters. The local filter is time invariant, which greatly reduces the computational complexity of the global filter. The global filter is optimal in the sense of the unbiased minimum variance (UMV) criteria.
منابع مشابه
Design of nonlinear parity approach to fault detection and identification based on Takagi-Sugeno fuzzy model and unknown input observer in nonlinear systems
In this study, a novel fault detection scheme is developed for a class of nonlinear system in the presence of sensor noise. A nonlinear Takagi-Sugeno fuzzy model is implemented to create multiple models. While the T-S fuzzy model is used for only the nonlinear distribution matrix of the fault and measurement signals, a larger category of nonlinear systems is considered. Next, a mapping to decou...
متن کاملOnline State Space Model Parameter Estimation in Synchronous Machines
The purpose of this paper is to present a new approach based on the Least Squares Error method for estimating the unknown parameters of the nonlinear 3rd order synchronous generator model. The proposed method uses the mathematical relationships between the machine parameters and on-line input/output measurements to estimate the parameters of the nonlinear state space model. The field voltage is...
متن کاملAdaptive fuzzy pole placement for stabilization of non-linear systems
A new approach for pole placement of nonlinear systems using state feedback and fuzzy system is proposed. We use a new online fuzzy training method to identify and to obtain a fuzzy model for the unknown nonlinear system using only the system input and output. Then, we linearized this identified model at each sampling time to have an approximate linear time varying system. In order to stabilize...
متن کاملRotated Unscented Kalman Filter for Two State Nonlinear Systems
In the several past years, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.The UKF has consistently outperformed for estimation. Sometimes least estimation error doesn't yieldwith UKF for the most nonlinear systems. In this paper, we use a new approach for a two variablestate no...
متن کاملA Linear Matrix Inequality (LMI) Approach to Robust Model Predictive Control (RMPC) Design in Nonlinear Uncertain Systems Subjected to Control Input Constraint
In this paper, a robust model predictive control (MPC) algorithm is addressed for nonlinear uncertain systems in presence of the control input constraint. For achieving this goal, firstly, the additive and polytopic uncertainties are formulated in the nonlinear uncertain systems. Then, the control policy can be demonstrated as a state feedback control law in order to minimize a given cost funct...
متن کاملNonlinear System Identification Using Hammerstein-Wiener Neural Network and subspace algorithms
Neural networks are applicable in identification systems from input-output data. In this report, we analyze theHammerstein-Wiener models and identify them. TheHammerstein-Wiener systems are the simplest type of block orientednonlinear systems where the linear dynamic block issandwiched in between two static nonlinear blocks, whichappear in many engineering applications; the aim of nonlinearsyst...
متن کامل