Event-Based MPC for Nonlinear Systems with Additive Disturbances: A Quasi-Differential Type Approach

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Optimization Theory

Background:

• Model Predictive Control (MPC) is effective for complex systems but can be computationally intensive.
• Event-based control strategies aim to reduce communication and computation load.
• Nonlinear systems with disturbances pose significant control challenges.

Purpose of the Study:

• To develop an improved event-based model predictive control (MPC) framework for nonlinear systems subjected to disturbances.
• To enhance system stability and ensure the feasibility of MPC.
• To reduce the frequency of optimization problem solutions and save resources.

Main Methods:

• Integration of an event-based mechanism with the model predictive control (MPC) method.
• Introduction of a novel event-triggering condition based on the gradients of state prediction differences.
• Incorporation of dual-mode control technique to handle nonlinear perturbed systems.

Main Results:

• The proposed framework ensures the feasibility of the MPC method and the stability of the nonlinear system.
• A significant decrease in the number of optimization problems solved was achieved.
• Effective resource saving for information transfer was demonstrated.

Conclusions:

• The developed event-based MPC framework offers an efficient solution for controlling nonlinear systems with disturbances.
• The novel triggering condition and dual-mode control enhance performance and reduce computational burden.
• The approach provides a practical method for resource-constrained control applications.