Observer-Based Decentralized Control for Non-Strict-Feedback Fractional-Order Nonlinear Large-Scale Systems With...

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Fractional Calculus

Background:

• Decentralized control of large-scale systems is challenging due to interconnections and state uncertainties.
• Fractional-order systems introduce complexities not present in integer-order systems.
• Immeasurable states and unknown nonlinearities, including dead zones, hinder effective control design.

Purpose of the Study:

• To develop an output-feedback decentralized control scheme for fractional-order nonlinear large-scale nonstrict-feedback systems.
• To address challenges posed by immeasurable states, unknown nonlinear functions, and dead zones.
• To ensure stability and convergence of tracking and observer errors.

Main Methods:

• Utilizing neural networks (NNs) for identifying unknown nonlinear functions.
• Establishing decentralized state observers based on NNs to estimate immeasurable states.
• Employing fractional-order adaptation laws and fractional-order dynamic surface control (FODSC) within an adaptive backstepping framework.
• Addressing algebraic loops using NN basis function properties.
• Modeling nonsymmetric dead zones as time-varying uncertain systems.

Main Results:

• A stable adaptive NNs' output-feedback decentralized control scheme was developed.
• The proposed method effectively handles unknown nonlinearities and immeasurable states.
• Tracking and observer errors were shown to converge to a small neighborhood of zero.
• The fractional-order Lyapunov stability criterion confirmed system stability.

Conclusions:

• The developed control scheme is effective for fractional-order nonlinear large-scale nonstrict-feedback systems.
• The integration of NNs, FODSC, and adaptive backstepping provides a robust solution.
• Simulation examples validate the performance and stability of the proposed control strategy.