Decentralized adaptive quantized feedback tracking of a class of uncertain interconnected lower-triangular nonlinear...

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Systems Theory

Background:

• Decentralized adaptive control is crucial for complex systems.
• Quantization of state variables presents significant challenges in control design.
• Existing methods often require full state information or have limitations on system nonlinearities.

Purpose of the Study:

• To develop a decentralized adaptive control strategy for uncertain nonlinear systems with quantized local state feedback.
• To address the challenges posed by hysteresis quantizers and interconnections.
• To establish a robust stability analysis framework for the proposed control design.

Main Methods:

• A novel recursive design approach is employed for adaptive control.
• Hysteresis quantizers are utilized for local state variable quantization.
• Technical lemmas are derived to analyze the boundedness of quantization errors.
• The control design specifically handles unmatched interconnections and nonlinearities.

Main Results:

• A decentralized adaptive control scheme is successfully designed using only local quantized state variables.
• The proposed method effectively manages system uncertainties, interconnections, and nonlinearities.
• Stability of the closed-loop system is rigorously proven, considering quantization effects.
• The approach overcomes limitations of existing methods by not restricting virtual control laws.

Conclusions:

• The developed adaptive quantized feedback control strategy is effective for uncertain interconnected nonlinear systems.
• The use of local quantized states offers a practical solution for decentralized control applications.
• The recursive design and stability analysis provide a robust framework for handling complex system dynamics.