Abstract
This chapter is interested in designing the \(\mathcal {H}_{\infty } \) reduced-order filter for T-S fuzzy delayed systems with stochastic perturbation. Using a novel Lyapunov function and the reciprocally convex approach, the fuzzy-dependent conditions are established to ensure that the resulting filtering system is mean-square asymptotically stable and satisfies the prescribed \(\mathcal {H}_{\infty }\) performance. Next, feasible solutions of the designed reduced-order filter are specified, which can be converted to a convex optimization problem via the convex linearization strategy. Finally, simulation examples, including that of an inverted pendulum, are given to demonstrate the validity and superiority of the presented \(\mathcal {H}_{\infty } \) reduced-order filtering scheme.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Su, X., Wen, Y., Yang, Y., Shi, P. (2022). Reduced-Order Filter Design of Fuzzy Stochastic Systems. In: Intelligent Control, Filtering and Model Reduction Analysis for Fuzzy-Model-Based Systems. Studies in Systems, Decision and Control, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-81214-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-81214-0_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-81213-3
Online ISBN: 978-3-030-81214-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)