A new Minimum Variance Observer for Stochastic LPV systems with Unknown Inputs ⋆
نویسندگان
چکیده
Abstract: This paper is dedicated to the design of a state estimator for discrete-time Linear Parameter Varying (LPV) systems affected by unknown inputs and random Gaussian noises. Contrary to the existing work, the observer designed in this paper takes measures at several time steps into account in order to improve the performance (in terms of minimizing the variance estimation error). This approach is based on combining the classical Kalman Filter with the design strategies of deterministic observer for LPV systems in deterministic framework. Then, as an extension of this result, the observer is used for estimation of LPV systems without unknown inputs when state noises have a very high variance in comparison to the measurement noises. Simulation results are presented to illustrate the effectiveness of the proposed approach.
منابع مشابه
An LPV Approach to Sensor Fault Diagnosis of Robotic Arm
One of the major challenges in robotic arms is to diagnosis sensor fault. To address this challenge, this paper presents an LPV approach. Initially, the dynamics of a two-link manipulator is modelled with a polytopic linear parameter varying structure and then by using a descriptor system approach and a robust design of a suitable unknown input observer by means of pole placement method along w...
متن کاملUnbiased minimum variance estimation for systems with unknown exogenous inputs
A new method is developed for the state estimation of linear discrete-time stochastic system in the presence of unknown disturbance. The obtained filter is optimal in the unbiased minimum variance sense. The necessary and sufficient conditions for the existence and the stability of the filter are given.
متن کاملRobust fault detection of singular LPV systems with multiple time-varying delays
In this paper, the robust fault detection problem for LPV singular delayed systems in the presence of disturbances and actuator faults is considered. For both disturbance decoupling and actuator fault detection, an unknown input observer (UIO) is proposed. The aim is to compute a residual signal which has minimum sensitivity to disturbances while having maximum sensitivity to faults. Robustness...
متن کاملExtension of minimum variance estimation for systems with unknown inputs
In this paper, we address the problem of minimum variance estimation for discrete-time time-varying stochastic systems with unknown inputs. The objective is to construct an optimal filter in the general case where the unknown inputs affect both the stochastic model and the outputs. It extends the results of Darouach and Zasadzinski (1997) where the unknown inputs are only present in the model. ...
متن کاملOn the Optimality of Two-stage Kalman Filtering for Systems with Unknown Inputs
This paper is concerned with the optimal solution of two-stage Kalman filtering for linear discrete-time stochastic time-varying systems with unknown inputs affecting both the system state and the outputs. By means of a newlypresented modified unbiased minimum-variance filter (MUMVF), which appears to be the optimal solution to the addressed problem, the optimality of two-stage Kalman filtering...
متن کامل