Lessons in digital estimation theory
Lessons in digital estimation theory
Robust stabilization of linear systems with norm-bounded time-varying uncertainty
Systems & Control Letters
Stochastic differential equations (3rd ed.): an introduction with applications
Stochastic differential equations (3rd ed.): an introduction with applications
H∞ filtering for a class of uncertain nonlinear systems
Systems & Control Letters
Robust/ H∞ filtering for nonlinear systems
Systems & Control Letters
SIAM Journal on Control and Optimization
State Feedback $H_\infty$ Control for a Class of Nonlinear Stochastic Systems
SIAM Journal on Control and Optimization
Genetic algorithm approach to fixed-order mixed H2/H∞ optimal deconvolution filter designs
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
New approach to mixed H2/H∞ filtering for polytopic discrete-time systems
IEEE Transactions on Signal Processing - Part II
Robust H∞ filtering for nonlinear stochastic systems
IEEE Transactions on Signal Processing
Robust H2/H∞ filtering for linearsystems with error variance constraints
IEEE Transactions on Signal Processing
Mixed H2/H∞ filtering design inmultirate transmultiplexer systems: LMI approach
IEEE Transactions on Signal Processing
Reduced-order H∞ filtering for stochastic systems
IEEE Transactions on Signal Processing
Filtering for discrete fuzzy stochastic systems with sensor nonlinearities
IEEE Transactions on Fuzzy Systems
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This paper proposes a robust global linearization filter design for a nonlinear stochastic system with exogenous disturbance. The nonlinear dynamic system is modeled by Itô-type stochastic differential equations. For a general nonlinear stochastic system with exogenous disturbance, the robust H∞ filter can be obtained by solving a second-order nonlinear Hamilton-Jacobi inequality (HJI). In general, it is difficult to solve the second-order nonlinear HJI. In this paper, based on the global linearization scheme, the robust H∞ global linearization filter design for nonlinear stochastic systems is proposed via solving linear matrix inequalities (LMIs) instead of a second-order HJI. When the worst case disturbance attenuation of H∞ filtering is considered, a suboptimal H2 global linearization filtering problem is also solved by minimizing the upper bound on the H2 norm of the estimation error variance. The suboptimal global linearization filtering design problem under a desired worst case disturbance attenuation (i.e., the mixed H2/H∞ filtering design problem) is also transformed into a constrained optimization problem characterized in terms of LMI constraints, which can efficiently be solved by convex optimization techniques via the LMI toolbox of Matlab. Therefore, the proposed robust global linearization filter is potential for practical state estimation of nonlinear stochastic systems with intrinsic random fluctuation and external disturbance. A simulation example is provided to illustrate the design procedure and to confirm the expected robust filtering performance.