Interpretable fuzzy model reference adaptive controller for linear MIMO systems: MATLAB implementation and simulation toolkit
We present a MATLAB-based software package for designing and simulating an interpretable fuzzy model reference adaptive controller (FMRAC) for linear multi-input-multi-output (MIMO) systems with partially known dynamics. The toolkit implements a step-by-step procedure for synthesizing a fuzzy contro...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
|
| Series: | SoftwareX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711025002766 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849228353144356864 |
|---|---|
| author | Krzysztof Wiktorowicz Jacek Kluska |
| author_facet | Krzysztof Wiktorowicz Jacek Kluska |
| author_sort | Krzysztof Wiktorowicz |
| collection | DOAJ |
| description | We present a MATLAB-based software package for designing and simulating an interpretable fuzzy model reference adaptive controller (FMRAC) for linear multi-input-multi-output (MIMO) systems with partially known dynamics. The toolkit implements a step-by-step procedure for synthesizing a fuzzy controller that guarantees closed-loop stability by applying a rigorous frequency domain absolute stability criterion applicable to the time-varying nonlinearities inherent in adaptive systems. This stability-driven design directly enforces a constrained, sector-bounded structure, resulting in a controller that is interpretable-by-design with a simple, transparent rule base for each input–output channel. Furthermore, a key innovation is the automatic synthesis of a near-optimal nonlinear reference model, which is co-optimized within the same stability constraints to ensure high-performance, achievable trajectories. The toolkit’s effectiveness is demonstrated on a 3rd-order MIMO system, where it automatically derives a controller whose stability is formally guaranteed across a predefined plant uncertainty set, ensuring robust tracking with rapid error convergence following significant abrupt plant parameter variations. The proposed toolkit enables control engineers to design stable and transparent fuzzy adaptive controllers and to evaluate their performance easily in MATLAB. |
| format | Article |
| id | doaj-art-32ce3a4de3a14a8baad1ddc33a121f5a |
| institution | Kabale University |
| issn | 2352-7110 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj-art-32ce3a4de3a14a8baad1ddc33a121f5a2025-08-23T04:48:40ZengElsevierSoftwareX2352-71102025-09-013110231010.1016/j.softx.2025.102310Interpretable fuzzy model reference adaptive controller for linear MIMO systems: MATLAB implementation and simulation toolkitKrzysztof Wiktorowicz0Jacek Kluska1Corresponding author.; Rzeszow University of Technology, 35-959 Rzeszów, Powstańców Warszawy 12, PolandRzeszow University of Technology, 35-959 Rzeszów, Powstańców Warszawy 12, PolandWe present a MATLAB-based software package for designing and simulating an interpretable fuzzy model reference adaptive controller (FMRAC) for linear multi-input-multi-output (MIMO) systems with partially known dynamics. The toolkit implements a step-by-step procedure for synthesizing a fuzzy controller that guarantees closed-loop stability by applying a rigorous frequency domain absolute stability criterion applicable to the time-varying nonlinearities inherent in adaptive systems. This stability-driven design directly enforces a constrained, sector-bounded structure, resulting in a controller that is interpretable-by-design with a simple, transparent rule base for each input–output channel. Furthermore, a key innovation is the automatic synthesis of a near-optimal nonlinear reference model, which is co-optimized within the same stability constraints to ensure high-performance, achievable trajectories. The toolkit’s effectiveness is demonstrated on a 3rd-order MIMO system, where it automatically derives a controller whose stability is formally guaranteed across a predefined plant uncertainty set, ensuring robust tracking with rapid error convergence following significant abrupt plant parameter variations. The proposed toolkit enables control engineers to design stable and transparent fuzzy adaptive controllers and to evaluate their performance easily in MATLAB.http://www.sciencedirect.com/science/article/pii/S2352711025002766Fuzzy model reference adaptive controlMulti-input-multi-output systemsInterpretabilityMATLAB/Simulink |
| spellingShingle | Krzysztof Wiktorowicz Jacek Kluska Interpretable fuzzy model reference adaptive controller for linear MIMO systems: MATLAB implementation and simulation toolkit SoftwareX Fuzzy model reference adaptive control Multi-input-multi-output systems Interpretability MATLAB/Simulink |
| title | Interpretable fuzzy model reference adaptive controller for linear MIMO systems: MATLAB implementation and simulation toolkit |
| title_full | Interpretable fuzzy model reference adaptive controller for linear MIMO systems: MATLAB implementation and simulation toolkit |
| title_fullStr | Interpretable fuzzy model reference adaptive controller for linear MIMO systems: MATLAB implementation and simulation toolkit |
| title_full_unstemmed | Interpretable fuzzy model reference adaptive controller for linear MIMO systems: MATLAB implementation and simulation toolkit |
| title_short | Interpretable fuzzy model reference adaptive controller for linear MIMO systems: MATLAB implementation and simulation toolkit |
| title_sort | interpretable fuzzy model reference adaptive controller for linear mimo systems matlab implementation and simulation toolkit |
| topic | Fuzzy model reference adaptive control Multi-input-multi-output systems Interpretability MATLAB/Simulink |
| url | http://www.sciencedirect.com/science/article/pii/S2352711025002766 |
| work_keys_str_mv | AT krzysztofwiktorowicz interpretablefuzzymodelreferenceadaptivecontrollerforlinearmimosystemsmatlabimplementationandsimulationtoolkit AT jacekkluska interpretablefuzzymodelreferenceadaptivecontrollerforlinearmimosystemsmatlabimplementationandsimulationtoolkit |