Name
Abstract | ||
|---|---|---|
Multi-objective model predictive control (MMPC) is an effective method to solve the problem of nonlinear systems with multiple conflicting control objectives. However, the MMPC method usually suffers from the challenge of high computational cost for the determining the optimal objective weights with the process of transforming multiple objectives into a single objective. For low computational cost, an MMPC method with gradient eigenvector algorithm (MMPC-GEA) for nonlinear systems is proposed to comprehensively deal with multiple conflicting control objectives. The proposed MMPC-GEA in the framework of MMPC is composed of a fuzzy neural network (FNN) identifier and a receding optimization algorithm. For the proposed MMPC-GEA, FNN with an adaptive learning algorithm is devised to capture the nonlinear characteristic of systems. Moreover, a gradient eigenvector algorithm (GEA) is designed to gain the optimization solution of the control objectives for nonlinear systems. Specifically, GEA can reduce the computationally demanding by avoiding the determination of the objective weights. Furthermore, the stability and control performance analysis of the MMPC-GEA scheme is provided. Finally, the effectiveness of the proposed MMPC-GEA approach is demonstrated using a numerical simulation and wastewater treatment process. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.ins.2022.04.022 | Information Sciences |
Keywords | DocType | Volume |
Multi-objective model predictive control (MMPC),Adaptive fuzzy neural network,Multi-objective optimization,Gradient eigenvector algorithm (GEA),Computational cost | Journal | 601 |
ISSN | Citations | PageRank |
0020-0255 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong-Gui Han | 1 | 476 | 39.06 |
| Cong Chen | 2 | 0 | 0.34 |
| Haoyuan Sun | 3 | 0 | 0.34 |
| Shengli Du | 4 | 0 | 0.34 |
| Jun-Fei Qiao | 5 | 798 | 74.56 |