Distributed Output-Feedback Asymptotic Consensus Tracking for High-Order Multiagent Systems With Quantized Input.

Area of Science:

• Control Systems Engineering
• Networked Systems
• Nonlinear Control Theory

Background:

• Multiagent systems (MAS) face challenges with uncertainty, unmeasured states, and input quantization.
• Achieving consensus tracking in such systems requires robust control strategies.
• Existing methods often struggle with combined nonlinearities and parameter uncertainties.

Purpose of the Study:

• To develop a distributed adaptive asymptotic consensus tracking control scheme for uncertain high-order MAS.
• To address challenges posed by unmeasured states-dependent nonlinearities, unknown parameters, and input quantization.
• To introduce a novel approach for predefining the transient performance of consensus error.

Main Methods:

• Utilizing improved K-filters with dynamic gain for state estimation and handling nonlinearities.
• Proposing a recursive control strategy incorporating first-order dynamic parameter filters.
• Introducing a funnel variable combined with a prespecified time performance function.

Main Results:

• The proposed K-filters effectively estimate unknown states and manage nonlinearities.
• The recursive control strategy counteracts filter errors, driving consensus tracking errors to zero asymptotically.
• The new funnel variable allows for predefinition of transition time and overshoot for consensus errors.

Conclusions:

• The developed output-feedback control scheme offers a robust solution for uncertain high-order MAS.
• The method demonstrates low design complexity while achieving asymptotic consensus tracking.
• Simulation results validate the effectiveness and superiority of the proposed control strategy.