Distributed model predictive control based on neighborhood optimization for thickness and tension control system in...

Area of Science:

• Industrial Engineering
• Control Systems Engineering
• Materials Science

Background:

• Tandem cold rolling requires precise control of strip thickness and tension for quality assurance.
• Existing field control modes often fail to meet stringent industrial quality requirements.
• Limitations in current control strategies necessitate advanced solutions for improved precision.

Purpose of the Study:

• To develop a novel control strategy for enhancing precision in tandem cold rolling.
• To decrease strip thickness deviation and tension fluctuations during the cold rolling process.
• To improve the overall quality of rolled strip products through advanced control.

Main Methods:

• Established a cold rolling model for multi-stand systems, detailing process parameter relationships.
• Derived state evolution equations using neighborhood optimization theory for each rolling stand.
• Implemented a distributed model predictive control (DMPC) strategy with a focus on input and state information for optimization.

Main Results:

• The DMPC strategy effectively controlled thickness deviation to within 6 × 10^-5 mm and tension deviation to 0.012 kN under roll speed disturbances.
• The proposed control system demonstrated a rapid scan cycle calculation time of 0.0085 s.
• Simulations using actual rolling data validated the superior performance compared to conventional methods.

Conclusions:

• The DMPC strategy combined with neighborhood optimization significantly enhances control precision in tandem cold rolling.
• This advanced control system offers excellent performance, leading to improved strip product quality.
• The DMPC approach provides a viable and effective solution for meeting demanding industrial quality standards.