Adaptive Fuzzy Decentralized Dynamic Surface Control for Switched Large-Scale Nonlinear Systems With Full-State...

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Artificial Intelligence

Background:

• Switched large-scale nonlinear systems present significant control challenges due to inherent nonlinearities, interconnections, and state constraints.
• Existing control methods struggle with the complexity of deferred asymmetric and time-varying full-state constraints in such systems.
• Fuzzy-logic systems (FLS) offer a powerful approach for approximating unknown nonlinearities.

Purpose of the Study:

• To develop an adaptive fuzzy decentralized dynamic surface control (DSC) strategy for switched large-scale nonlinear systems.
• To address the difficulties posed by deferred asymmetric and time-varying full-state constraints.
• To design a controller that guarantees system stability and constraint satisfaction.

Main Methods:

• Utilized fuzzy-logic systems to approximate unknown nonlinear functions within the system.
• Employed dynamic surface control (DSC) to mitigate the 'curse of dimensionality'.
• Developed a novel fuzzy adaptive decentralized controller using a convex combination technique and a state-dependent switching law.

Main Results:

• Demonstrated that the proposed controller ensures all closed-loop system states remain bounded.
• Proved that the system strictly obeys deferred asymmetric and time-varying full-state constraints.
• Simulation results confirmed the effectiveness of the developed control strategy.

Conclusions:

• The proposed fuzzy adaptive decentralized DSC approach successfully manages complex constraints in switched large-scale nonlinear systems.
• The controller guarantees system stability and strict adherence to all state constraints.
• This method offers a robust solution for challenging control problems in nonlinear systems.