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

Huirong Zhao, Jiong Shen, Yiguo Li, Joseph Bentsman

Research output: Contribution to journalArticlepeer-review


This paper proposes a new preference adjustable multi-objective model predictive control (PA-MOMPC) law for constrained nonlinear systems. With this control law, a reasonable prioritized optimal solution can be directly derived without constructing the Pareto front by solving a minimal optimization problem, which is a novel development of recently proposed utopia tracking approaches by additionally considering objective preferences with more flexible terminal and stability constraints. The tracking point of the proposed PA-MOMPC law is represented by a parametric vector with the parameters adjustable on the basis of objective preferences. The main result of this paper is that the solution obtained through the proposed PA-MOMPC law is demonstrated to have two important properties. One is the inherent Pareto optimality, and the other is the priority consistency between the solution and the tuning parametric vector. This combination makes the objective priorities tuning process transparent and efficient. The proposed PA-MOMPC law is supported by feasibility analyses, proof of nominal stability, and a numerical case study.

Original languageEnglish (US)
Pages (from-to)134-142
Number of pages9
JournalISA transactions
StatePublished - Jan 1 2017


  • Multi-objective
  • Optimization
  • Pareto
  • Predictive control
  • Priority
  • Stability

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Instrumentation
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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