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

Huirong Zhao; Jiong Shen; Yiguo Li; Joseph Bentsman

doi:10.1016/j.isatra.2016.09.020

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

ISA Trans. 2017 Jan:66:134-142. doi: 10.1016/j.isatra.2016.09.020. Epub 2016 Oct 21.

Authors

Huirong Zhao¹, Jiong Shen², Yiguo Li³, Joseph Bentsman⁴

Affiliations

¹ Key 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. Electronic address: 230129063@seu.edu.cn.
² Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, Jiangsu Province, China. Electronic address: shenj@seu.edu.cn.
³ Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, Jiangsu Province, China. Electronic address: lyg@seu.edu.cn.
⁴ Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. Electronic address: jbentsma@illinois.edu.

PMID: 27773379
DOI: 10.1016/j.isatra.2016.09.020

Abstract

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.

Keywords: Multi-objective; Optimization; Pareto; Predictive control; Priority; Stability.