A dual-model jumping fuzzy system approach to networked control systems design.

Area of Science:

• Control Systems Engineering
• Fuzzy Logic Systems
• Stochastic Systems

Background:

• Networked control systems (NCSs) often exhibit asymmetric characteristics due to random communication delays and packet dropouts.
• Modeling these uncertainties is crucial for robust controller design in NCSs.
• Hidden Markov Models (HMMs) provide a powerful framework for representing systems with unobserved states, such as communication channel conditions.

Purpose of the Study:

• To propose a discrete-time jump fuzzy system incorporating two HMMs to model the asymmetric network characteristics of nonlinear NCSs.
• To design a less conservative state feedback controller and a dual-model-dependent guaranteed cost controller.
• To develop a homotopy-based iterative algorithm for solving nonlinear matrix inequalities (NMIs) to obtain control gains.

Main Methods:

• A discrete-time jump fuzzy system model with two HMMs was developed to capture random communication delays and packet dropouts.
• State feedback and guaranteed cost controllers were designed based on the proposed model.
• A homotopy-based iterative algorithm was employed to solve the resulting nonlinear matrix inequalities (NMIs) for controller synthesis.

Main Results:

• The proposed discrete-time jump fuzzy system effectively models the asymmetric network characteristics of NCSs.
• The designed state feedback and guaranteed cost controllers ensure stability and performance despite communication uncertainties.
• The homotopy-based algorithm successfully computed the required control gains.

Conclusions:

• The developed discrete-time jump fuzzy system with HMMs provides an effective approach for analyzing and controlling nonlinear NCSs with communication impairments.
• The proposed control design and solution algorithm demonstrate the feasibility and effectiveness of the presented methodology.
• Simulation results validate the superior performance of the proposed approach in handling network-induced uncertainties.