Title :
Moving-Horizon State Estimation for Nonlinear Systems Using Neural Networks
Author :
Alessandri, Angelo ; Baglietto, Marco ; Battistelli, Giorgio ; Gaggero, Mauro
Author_Institution :
Dept. of Production Eng., Thermoenergetics, & Math. Models, Univ. of Genoa, Genova, Italy
fDate :
5/1/2011 12:00:00 AM
Abstract :
Moving-horizon (MH) state estimation is addressed for nonlinear discrete-time systems affected by bounded noises acting on system and measurement equations by minimizing a sliding-window least-squares cost function. Such a problem is solved by searching for suboptimal solutions for which a certain error is allowed in the minimization of the cost function. Nonlinear parameterized approximating functions such as feedforward neural networks are employed for the purpose of design. Thanks to the offline optimization of the parameters, the resulting MH estimation scheme requires a reduced online computational effort. Simulation results are presented to show the effectiveness of the proposed approach in comparison with other estimation techniques.
Keywords :
discrete time systems; least squares approximations; minimisation; neural nets; nonlinear control systems; state estimation; bounded noises; measurement equations; minimization; moving-horizon state estimation; neural networks; nonlinear discrete-time systems; nonlinear parameterized approximating functions; offline optimization; sliding-window least-squares cost function; Approximation methods; Artificial neural networks; Cost function; Estimation error; Minimization; State estimation; Moving horizon; nonlinear systems; offline optimization; state estimation; Algorithms; Artificial Intelligence; Computer Simulation; Mathematical Concepts; Models, Theoretical; Neural Networks (Computer); Nonlinear Dynamics; Problem Solving;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2011.2116803
