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Event-triggered decentralized adaptive critic learning control for interconnected systems with nonlinear inequality

Wenqian Du¹, Mingduo Lin², Guoling Yuan³

¹School of Systems Science, Beijing Normal University, Beijing, 100875, China; Traffic Management Bureau of Wuhan Public Security Bureau, Wuhan, 430030, China.

Neural Networks : the Official Journal of the International Neural Network Society

|February 13, 2026

Summary

A new event-triggered decentralized adaptive critic learning (ACL) control method addresses nonlinear constraints in interconnected systems. This approach optimizes resource usage by updating control policies only when necessary, ensuring system stability.

Keywords:

Adaptive critic learning Adaptive dynamic programming Decentralized control Event-triggered control Inequality state constraints

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Area of Science:

Control Systems Engineering
Artificial Intelligence
Nonlinear Dynamics

Background:

Interconnected systems often face complex nonlinear state constraints.
Decentralized control strategies are crucial for managing large-scale systems.
Adaptive critic learning (ACL) offers a powerful framework for optimal control.

Purpose of the Study:

To propose an event-triggered decentralized adaptive critic learning (ACL) control method.
To handle nonlinear inequality state constraints in interconnected systems.
To enhance computational and communication efficiency.

Main Methods:

Transforming nonlinear inequality constraints into equality constraints using slack functions.
Developing decentralized control laws via local policy iteration and Hamilton-Jacobi-Bellman equations.
Implementing a novel event-triggering mechanism for resource-efficient control updates.

Main Results:

Successfully derived event-triggered decentralized control laws for isolated subsystems.
Developed an overall optimal control strategy for the entire interconnected system.
Guaranteed uniform ultimate boundedness for the closed-loop system and neural network weight estimation errors.

Conclusions:

The proposed event-triggered ACL method effectively manages nonlinear constraints in interconnected systems.
The method significantly reduces computational and communication overhead.
Simulation results validate the approach's effectiveness and stability guarantees.