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Preference adjustable multi-objective NMPC: An unreachable prioritized point tracking method.

Huirong Zhao1, Jiong Shen2, Yiguo Li2

  • 1Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, Jiangsu Province, China; Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

ISA Transactions
|October 25, 2016
PubMed
Summary
This summary is machine-generated.

A new preference adjustable multi-objective model predictive control (PA-MOMPC) law directly finds optimal solutions for nonlinear systems. This method avoids Pareto front construction, offering transparent and efficient objective priority tuning.

Keywords:
Multi-objectiveOptimizationParetoPredictive controlPriorityStability

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Area of Science:

  • Control Engineering
  • Systems Science
  • Optimization Theory

Background:

  • Model predictive control (MPC) is crucial for complex systems.
  • Multi-objective optimization in MPC often requires Pareto front computation.
  • Existing utopia tracking methods lack flexible preference incorporation.

Purpose of the Study:

  • To introduce a novel preference adjustable multi-objective model predictive control (PA-MOMPC) law.
  • To enable direct derivation of prioritized optimal solutions for constrained nonlinear systems.
  • To enhance objective preference tuning transparency and efficiency.

Main Methods:

  • Development of a PA-MOMPC law utilizing a parametric tracking vector.
  • Incorporation of flexible terminal and stability constraints.
  • Solving a minimal optimization problem without explicit Pareto front construction.

Main Results:

  • The proposed PA-MOMPC law yields solutions with inherent Pareto optimality.
  • Demonstrated priority consistency between the obtained solution and the tuning parametric vector.
  • Feasibility analyses, proof of nominal stability, and a numerical case study validate the approach.

Conclusions:

  • The PA-MOMPC law offers a direct and efficient method for multi-objective control.
  • It provides a transparent mechanism for adjusting objective priorities.
  • The method is suitable for constrained nonlinear systems requiring optimal control strategies.