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An enhanced L 2 state estimation for linear parabolic PDE systems with mobile sensors
Author(s) -
Wu HuaiNing,
Zhang XiaoWei
Publication year - 2019
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.3050
Subject(s) - estimator , control theory (sociology) , mathematical optimization , computer science , mathematics , lyapunov function , linear matrix inequality , partial differential equation , bilinear interpolation , linear system , state (computer science) , algorithm , nonlinear system , artificial intelligence , statistics , mathematical analysis , physics , control (management) , quantum mechanics
Summary This paper studies an enhancedL 2state estimation problem of distributed parameter processes modeled by a linear parabolic partial differential equation using mobile sensors. The proposed estimation scheme contains a state estimator and the guidance of mobile sensors, where the spatial domain is decomposed into multiple subdomains according to the number of sensors and each sensor is capable of moving within the respective subdomain. The state estimator is desired to make the state estimation error system exponentially stable while providing anL 2performance bound. The mobile sensor guidance is used to enhance the transient performance of the error system. By the Lyapunov direct technique, an integrated design of state estimator and mobile sensor guidance laws is developed in the form of bilinear matrix inequalities (BMIs) to meet the desired design objectives. Moreover, to make theL 2performance bound as small as possible, a suboptimal enhancedL 2state estimation problem is formulated as a BMI optimization one, which can be solved via an iterative linear matrix inequality algorithm. Finally, numerical simulations are given to show the effectiveness of the proposed method.