Pole-placement Predictive Functional Control for over-damped systems with real poles.

Area of Science:

• Control Engineering
• Automation Systems
• Process Control

Background:

• Predictive Functional Control (PFC) typically requires a coincidence horizon greater than one for high-order systems.
• Existing PFC methods often exhibit weak links between design parameters and desired system dynamics.
• This can complicate tuning, coding, and implementation of control strategies.

Purpose of the Study:

• To propose novel design strategies for Predictive Functional Control (PFC) algorithms.
• To demonstrate the feasibility of using a coincidence horizon of one with parallel first-order models.
• To achieve precise closed-loop dynamics and simplify PFC implementation.

Main Methods:

• Development of an independent prediction model using parallel first-order models.
• Analytical derivation of control laws for high-order and non-minimum phase processes.
• Investigation of the impact of a coincidence horizon of one on control performance.

Main Results:

• Successfully utilized parallel first-order models to enable a coincidence horizon of one in PFC.
• Achieved precise control over closed-loop dynamics, directly linking design parameters to desired performance.
• Demonstrated significant simplification in coding, tuning, constraint handling, and implementation.

Conclusions:

• The proposed PFC design using parallel first-order models offers significant advantages over conventional methods.
• A coincidence horizon of one simplifies control system implementation and enhances performance predictability.
• The approach shows strong potential for industrial utility in controlling complex processes.