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Updated: Oct 8, 2025

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Steady-state sequence optimization with incremental input constraints in two-layer model predictive control.

Jianbang Liu1, Haojie Sun2, Yi Zhang3

  • 1School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; Key Laboratory of Networked Control Systems of CAS, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China; University of Chinese Academy of Sciences, Beijing 100049, China.

ISA Transactions
|December 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel steady-state sequence optimization method for two-layer model predictive control. The new approach enhances global optimality of steady-state sequences, improving both steady-state and dynamic performance.

Keywords:
Global optimalityIncremental input constraintsLocal steady-state optimizationSteady-state sequence optimizationTwo-layer model predictive control

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Area of Science:

  • Control Engineering
  • Optimization Theory

Background:

  • Steady-state optimization is crucial for two-layer model predictive control (MPC).
  • Existing local steady-state optimization methods lack guaranteed global optimality.

Purpose of the Study:

  • To propose a novel steady-state sequence optimization approach for MPC.
  • To improve the global optimality of steady-state sequences in two-layer MPC.

Main Methods:

  • Discussed non-global optimality of local methods with an example.
  • Developed a new sequence optimization strategy for steady-state optimization.
  • Analyzed the formulation and parameter lower bound of the proposed strategy.

Main Results:

  • The proposed method effectively improves the global optimality of steady-state sequences.
  • Evaluated steady-state performance, dynamic performance, and computational burden.
  • Simulations confirmed the effectiveness and reliability of the new approach.

Conclusions:

  • The novel approach offers a new method for steady-state optimization in MPC.
  • It significantly enhances the global optimality of calculated steady-state sequences.
  • Provides improved performance and reliability for engineers using two-layer MPC.