Integral-Type Event-Triggered Model Predictive Control of Nonlinear Systems With Additive Disturbance

Area of Science:

• Control Engineering
• Nonlinear Systems Theory
• Automation and Robotics

Background:

• Model Predictive Control (MPC) is crucial for complex systems.
• Event-triggered control reduces computational load and communication.
• Continuous-time nonlinear systems present significant control challenges.

Purpose of the Study:

• To develop an integral-type event-triggered MPC for continuous-time nonlinear systems.
• To reduce the average sampling frequency of the control system.
• To enhance robustness against additive disturbances and enlarge the feasible region.

Main Methods:

• An integral-type event-triggered mechanism incorporating state error integrals.
• A novel robustness constraint to handle additive disturbances.
• Rigorous analysis of MPC feasibility and closed-loop system stability.

Main Results:

• Reduced average sampling frequency achieved through the integral-based triggering.
• Improved robustness and potentially enlarged initial feasible region due to the new constraint.
• Sufficient conditions for stability and feasibility established based on key parameters.

Conclusions:

• The proposed integral-type event-triggered MPC effectively controls continuous-time nonlinear systems.
• The approach balances control performance with reduced computational demands.
• Numerical examples validate the algorithm's effectiveness and robustness.