Performance Portrait Method: Robust Design of Predictive Integral Controller
The performance portrait method (PPM) can be characterized as a systematized digitalized version of the trial and error method—probably the most popular and very often used method of engineering work. Its digitization required the expansion of performance measures used to evaluate the step responses...
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MDPI AG
2025-01-01
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| author | Mikulas Huba Pavol Bistak Jarmila Skrinarova Damir Vrancic |
| author_facet | Mikulas Huba Pavol Bistak Jarmila Skrinarova Damir Vrancic |
| author_sort | Mikulas Huba |
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| description | The performance portrait method (PPM) can be characterized as a systematized digitalized version of the trial and error method—probably the most popular and very often used method of engineering work. Its digitization required the expansion of performance measures used to evaluate the step responses of dynamic systems. Based on process modeling, PPM also contributed to the classification of models describing linear and non-linear dynamic processes so that they approximate their dynamics using the smallest possible number of numerical parameters. From most bio-inspired procedures of artificial intelligence and optimization used for the design of automatic controllers, PPM is distinguished by the possibility of repeated application of once generated performance portraits (PPs). These represent information about the process obtained by evaluating the performance of setpoint and disturbance step responses for all relevant values of the determining loop parameters organized into a grid. It can be supported by the implementation of parallel calculations with optimized decomposition in the high-performance computing (HPC) cloud. The wide applicability of PPM ranges from verification of analytically calculated optimal settings achieved by various approaches to controller design, to the analysis as well as optimal and robust setting of controllers for processes where other known control design methods fail. One such situation is illustrated by an example of predictive integrating (PrI) controller design for processes with a dominant time-delayed sensor dynamics, representing a counterpart of proportional-integrating (PI) controllers, the most frequently used solutions in practice. PrI controllers can be considered as a generalization of the disturbance–response feedback—the oldest known method for the design of dead-time compensators by Reswick. In applications with dominant dead-time and loop time constants located in the feedback (sensors), as those, e.g., met in magnetoencephalography (MEG), it makes it possible to significantly improve the control performance. PPM shows that, despite the absence of effective analytical control design methods for such situations, it is possible to obtain high-quality optimal solutions for processes that require working with uncertain models specified by interval parameters, while achieving invariance to changes in uncertain parameters. |
| format | Article |
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| issn | 2313-7673 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Biomimetics |
| spelling | doaj-art-08055bfbb5704f2982f74bc889cc93712025-08-20T02:44:40ZengMDPI AGBiomimetics2313-76732025-01-011027410.3390/biomimetics10020074Performance Portrait Method: Robust Design of Predictive Integral ControllerMikulas Huba0Pavol Bistak1Jarmila Skrinarova2Damir Vrancic3Institute of Automotive Mechatronics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Ilkovicova 3, 841 04 Bratislava, SlovakiaInstitute of Automotive Mechatronics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Ilkovicova 3, 841 04 Bratislava, SlovakiaDepartment of Computer Science, Faculty of Natural Sciences, Matej Bel University, Tajovskeho 40, 974 01 Banska Bystrica, SlovakiaDepartment of Systems and Control, J. Stefan Institute, Jamova cesta 39, 1000 Ljubljana, SloveniaThe performance portrait method (PPM) can be characterized as a systematized digitalized version of the trial and error method—probably the most popular and very often used method of engineering work. Its digitization required the expansion of performance measures used to evaluate the step responses of dynamic systems. Based on process modeling, PPM also contributed to the classification of models describing linear and non-linear dynamic processes so that they approximate their dynamics using the smallest possible number of numerical parameters. From most bio-inspired procedures of artificial intelligence and optimization used for the design of automatic controllers, PPM is distinguished by the possibility of repeated application of once generated performance portraits (PPs). These represent information about the process obtained by evaluating the performance of setpoint and disturbance step responses for all relevant values of the determining loop parameters organized into a grid. It can be supported by the implementation of parallel calculations with optimized decomposition in the high-performance computing (HPC) cloud. The wide applicability of PPM ranges from verification of analytically calculated optimal settings achieved by various approaches to controller design, to the analysis as well as optimal and robust setting of controllers for processes where other known control design methods fail. One such situation is illustrated by an example of predictive integrating (PrI) controller design for processes with a dominant time-delayed sensor dynamics, representing a counterpart of proportional-integrating (PI) controllers, the most frequently used solutions in practice. PrI controllers can be considered as a generalization of the disturbance–response feedback—the oldest known method for the design of dead-time compensators by Reswick. In applications with dominant dead-time and loop time constants located in the feedback (sensors), as those, e.g., met in magnetoencephalography (MEG), it makes it possible to significantly improve the control performance. PPM shows that, despite the absence of effective analytical control design methods for such situations, it is possible to obtain high-quality optimal solutions for processes that require working with uncertain models specified by interval parameters, while achieving invariance to changes in uncertain parameters.https://www.mdpi.com/2313-7673/10/2/74optimal controlrobust controlPID controlpredictive integral controlperformance measuresperformance portrait method |
| spellingShingle | Mikulas Huba Pavol Bistak Jarmila Skrinarova Damir Vrancic Performance Portrait Method: Robust Design of Predictive Integral Controller Biomimetics optimal control robust control PID control predictive integral control performance measures performance portrait method |
| title | Performance Portrait Method: Robust Design of Predictive Integral Controller |
| title_full | Performance Portrait Method: Robust Design of Predictive Integral Controller |
| title_fullStr | Performance Portrait Method: Robust Design of Predictive Integral Controller |
| title_full_unstemmed | Performance Portrait Method: Robust Design of Predictive Integral Controller |
| title_short | Performance Portrait Method: Robust Design of Predictive Integral Controller |
| title_sort | performance portrait method robust design of predictive integral controller |
| topic | optimal control robust control PID control predictive integral control performance measures performance portrait method |
| url | https://www.mdpi.com/2313-7673/10/2/74 |
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