2017 13th IEEE International Conference on Control &Amp; Automation (ICCA) 2017
DOI: 10.1109/icca.2017.8003216
|View full text |Cite
|
Sign up to set email alerts
|

A practical tuning approach for multivariable model predictive control

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
5
0

Year Published

2018
2018
2023
2023

Publication Types

Select...
2
2

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(5 citation statements)
references
References 18 publications
0
5
0
Order By: Relevance
“…In this case, at each time instant, MPC should solve two optimization problems: an internal problem, the solution of which is MPC tunable parameter values, and an external problem, the solution of which is the current values of the manipulated variables. Turki et al 43 also suggested an optimization approach for tuning predication horizons in MPC of linear controllable MIMO plants with active constrains. Their optimization problem has three performance indices (a stability degree index, an error index, and a rapidity index).…”
Section: { }mentioning
confidence: 99%
See 4 more Smart Citations
“…In this case, at each time instant, MPC should solve two optimization problems: an internal problem, the solution of which is MPC tunable parameter values, and an external problem, the solution of which is the current values of the manipulated variables. Turki et al 43 also suggested an optimization approach for tuning predication horizons in MPC of linear controllable MIMO plants with active constrains. Their optimization problem has three performance indices (a stability degree index, an error index, and a rapidity index).…”
Section: { }mentioning
confidence: 99%
“…Optimization-based tuning methods have been developed for different control methods including MPC. Based on optimization, Turki et al 43 recommended setting Q = C T C and R = ρI. When applied to SISO and MIMO systems with FOPDT models, this tuning strategy was found to yield better performance in terms of response smoothness and speed, compared to those of Bagheri and Khaki-Sedigh 61 and Iglesias et al 63 Vallerio et al 64 employed a multiobjective optimization approach to tune Q and R by minimizing the deviation of the measured input and output from their references.…”
Section: Control Horizonsmentioning
confidence: 99%
See 3 more Smart Citations