Data-Driven Controller Tuning for MIMO Systems: A Set-Membership Approach

Over time, Single-Input Single-Output systems have received significant attention in the field of data-driven control. However, real-world applications often involve Multi-Input Multi-Output systems, where the challenges associated with multivariable control are considerably greater. This letter pre...

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Vydáno v:IEEE control systems letters Ročník 9; s. 799 - 804
Hlavní autoři: Cordoba-Pacheco, Andres, Ruiz, Fredy
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 2025
Témata:
Complexity theory
Data driven control
Data models
Identification for control
Mathematical models
MIMO
Noise
Noise measurement
Taylor series
Transfer functions
Tuning
uncertain systems
Vectors
ISSN:2475-1456, 2475-1456
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Abstract Over time, Single-Input Single-Output systems have received significant attention in the field of data-driven control. However, real-world applications often involve Multi-Input Multi-Output systems, where the challenges associated with multivariable control are considerably greater. This letter presents an innovative extension of the Set Membership Data-Driven approach from Single-Input Single-Output to Multi-Input Multi-Output systems. Exploiting unknown but bounded assumptions on process noise and fixed bases controllers parametrization, an efficient batch algorithm for controller tuning is developed, relying on low dimensional convex optimization problems. Through a comparative analysis with Virtual Reference Feedback Tuning, it is quantitatively demonstrated that the Set Membership Data-Driven approach significantly outperforms existing solutions, achieving reductions in Integral Square Error and Integral Absolute Error by up to 6% and 27%, respectively, thereby reducing coupling errors. Furthermore, the designed controllers exhibit faster rise and settling times, with improvements of up to 20% and 39%, eliminating the overshoots. These findings indicate that the SMDD approach effectively enhances decoupling and error minimization, making it a reliable solution for managing the complexities of MIMO systems.
AbstractList Over time, Single-Input Single-Output systems have received significant attention in the field of data-driven control. However, real-world applications often involve Multi-Input Multi-Output systems, where the challenges associated with multivariable control are considerably greater. This letter presents an innovative extension of the Set Membership Data-Driven approach from Single-Input Single-Output to Multi-Input Multi-Output systems. Exploiting unknown but bounded assumptions on process noise and fixed bases controllers parametrization, an efficient batch algorithm for controller tuning is developed, relying on low dimensional convex optimization problems. Through a comparative analysis with Virtual Reference Feedback Tuning, it is quantitatively demonstrated that the Set Membership Data-Driven approach significantly outperforms existing solutions, achieving reductions in Integral Square Error and Integral Absolute Error by up to 6% and 27%, respectively, thereby reducing coupling errors. Furthermore, the designed controllers exhibit faster rise and settling times, with improvements of up to 20% and 39%, eliminating the overshoots. These findings indicate that the SMDD approach effectively enhances decoupling and error minimization, making it a reliable solution for managing the complexities of MIMO systems.
Author Ruiz, Fredy
Cordoba-Pacheco, Andres
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SubjectTerms Complexity theory
Data driven control
Data models
Identification for control
Mathematical models
MIMO
Noise
Noise measurement
Taylor series
Transfer functions
Tuning
uncertain systems
Vectors
Title Data-Driven Controller Tuning for MIMO Systems: A Set-Membership Approach
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Volume 9
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