A two-time scale decentralized model predictive controller based on input and output model

Area of Science:

• Control Engineering
• Systems Theory

Background:

• Many control systems exhibit multi-time scale dynamics, complicating controller design.
• Decentralized control strategies are often preferred for complex systems due to reduced computational load.

Purpose of the Study:

• To develop a decentralized model predictive controller (MPC) for systems with two distinct time scales (fast and slow dynamics).
• To validate the stability and effectiveness of the proposed control method through simulations.

Main Methods:

• Utilized singular perturbation to decompose the original transfer function matrix into separate fast and slow time-scale models.
• Designed a decentralized model predictive controller based on these derived two-time scale models.
• Proved the stability of the developed control strategy.

Main Results:

• The singular perturbation method successfully separated the system into two time-scale models.
• The designed decentralized MPC demonstrated effectiveness in simulations.
• The stability of the proposed control method was mathematically proven.

Conclusions:

• The proposed decentralized MPC is a viable and stable control strategy for systems with two-time scale dynamics.
• The method offers an effective way to handle systems with varying dynamic characteristics across different channels.