Distributed Iterative Learning Control of Nonlinear Multiagent Systems Using Controller-Based Dynamic Linearization...

Area of Science:

• Control Theory
• Robotics
• Artificial Intelligence

Background:

• Existing distributed iterative learning control (DILC) methods for multiagent systems (MASs) often require precise knowledge of agent dynamics.
• Handling unknown, nonlinear, nonaffine, and heterogeneous agent dynamics presents a significant challenge in MAS control.
• Iteration-varying communication topologies further complicate the design of effective control strategies.

Purpose of the Study:

• To develop a data-driven distributed adaptive iterative learning control (DAILC) method for MASs with unknown, nonlinear, nonaffine, and heterogeneous dynamics.
• To address control challenges arising from iteration-varying communication topologies in MASs.
• To improve tracking accuracy and convergence speed in consensus tracking tasks for MASs.

Main Methods:

• Controller-based dynamic linearization in the iteration domain to derive a parametric learning controller.
• Utilizing local input-output data from neighboring agents in a directed graph.
• Implementing parameter-adaptive learning methods for a data-driven distributed adaptive iterative learning control (DAILC) approach.

Main Results:

• The proposed DAILC method ensures that tracking errors are ultimately bounded in the iteration domain for both iteration-invariant and iteration-varying communication topologies.
• Demonstrated faster convergence speed compared to a typical DAILC method.
• Achieved higher tracking accuracy and more robust learning and tracking performance.

Conclusions:

• The developed DAILC method effectively handles unknown, nonlinear, nonaffine, and heterogeneous agent dynamics in MASs.
• The approach is robust to iteration-varying communication topologies, offering improved performance over existing methods.
• This work advances the field of distributed control for complex multiagent systems.