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Event-triggered ADP-based tracking controller for partially unknown nonlinear uncertain systems with input and state

Raju Dahal¹, Indrani Kar¹

¹Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India.

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

|July 1, 2025

Summary

This study introduces an event-triggered adaptive dynamic programming (ADP) framework for robust tracking control of nonlinear systems with constraints and uncertainties. The method ensures system stability and bounded parameters using neural networks and Lyapunov theory.

Keywords:

Adaptive dynamic programming Event-triggered Input constraint Partially unknown dynamics Safety critical systems State constraint

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Area of Science:

Control Systems Engineering
Nonlinear System Dynamics
Artificial Intelligence in Control

Background:

Nonlinear systems often exhibit uncertainties and constraints, complicating robust tracking control.
Existing methods may struggle with partially unknown dynamics and unmatched uncertainties.
Event-triggered control offers potential for improved efficiency and resource management.

Purpose of the Study:

To develop a robust tracking control strategy for nonlinear systems with unmatched uncertainties, unknown dynamics, and input/state constraints.
To design an event-triggered adaptive dynamic programming (ADP) framework for this purpose.
To ensure system stability and parameter boundedness under disturbances.

Main Methods:

Utilizing an identifier neural network (NN) for estimating unknown system dynamics.
Constructing an augmented system and dividing uncertainties into matched and unmatched components.
Developing a novel event-triggered safe Hamilton-Jacobi-Bellman (HJB) equation using control barrier functions (CBF) and a nonquadratic cost term.
Employing a critic NN to solve the safe HJB equation and a Lyapunov-based triggering rule for controller updates.

Main Results:

Demonstrated stability of the closed-loop system using Lyapunov stability theory.
Proved that identifier and critic network parameters remain uniformly ultimately bounded (UUB).
Validated the effectiveness of the proposed event-triggered ADP approach through simulations.

Conclusions:

The proposed event-triggered ADP framework effectively addresses robust tracking control for constrained nonlinear systems with uncertainties.
The integration of CBF and ADP ensures safety constraints are met while maintaining stability.
The approach offers a promising solution for complex control problems in various engineering applications.