FAST ADAPTIVE GENERALIZED PREDICTIVE CONTROL FOR SYSTEMS WITH VARIABLE PARAMETERS
This paper proposes a fast predictive control structure with online model update according to process parametric variations. The proposed controller is based on the Generalized Predictive Control (GPC) algorithm, but it integrates the recursive least squares identification method with a variable fo...
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| Format: | Article |
| Language: | English |
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Universidade Federal de Viçosa (UFV)
2019-12-01
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| Series: | The Journal of Engineering and Exact Sciences |
| Subjects: | |
| Online Access: | https://periodicos.ufv.br/jcec/article/view/9376 |
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| _version_ | 1832569685791473664 |
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| author | S. B. ROVEA RODOLFO FLESCH |
| author_facet | S. B. ROVEA RODOLFO FLESCH |
| author_sort | S. B. ROVEA |
| collection | DOAJ |
| description |
This paper proposes a fast predictive control structure with online model update according to process parametric variations. The proposed controller is based on the Generalized Predictive Control (GPC) algorithm, but it integrates the recursive least squares identification method with a variable forgetting factor to estimate at each iteration the parameters of a linear structure model used for multi-step ahead prediction. For a system with constraints on the process variables, the resulting optimization problem of GPC is solved using quadratic programming based on the Alternate Direction Method of Multipliers, which allows the control signal to be obtained with small computational effort. In order to validate the proposed algorithm an experimental case study that considers the speed control of a direct current motor and the proposed controller embedded in a microcontroller STM32F303K8T6 is presented. Experimental results use as baseline the GPC with fixed model parameters and show that the proposed fast adaptive predictive control structure is able to keep almost the same transient response for all the considered operating points of the motor, while GPC presents high oscillations at operating conditions far from the one used to obtain the nominal model. Even though the proposed controller needs to solve two optimization problems at each sampling instant, it can run about 60 times in a second in the microcontroller used in this study
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| format | Article |
| id | doaj-art-bbf55a5b256f487590800a45343bdc0e |
| institution | Kabale University |
| issn | 2527-1075 |
| language | English |
| publishDate | 2019-12-01 |
| publisher | Universidade Federal de Viçosa (UFV) |
| record_format | Article |
| series | The Journal of Engineering and Exact Sciences |
| spelling | doaj-art-bbf55a5b256f487590800a45343bdc0e2025-02-02T19:58:34ZengUniversidade Federal de Viçosa (UFV)The Journal of Engineering and Exact Sciences2527-10752019-12-015510.18540/jcecvl5iss5pp0408-0414FAST ADAPTIVE GENERALIZED PREDICTIVE CONTROL FOR SYSTEMS WITH VARIABLE PARAMETERSS. B. ROVEARODOLFO FLESCH This paper proposes a fast predictive control structure with online model update according to process parametric variations. The proposed controller is based on the Generalized Predictive Control (GPC) algorithm, but it integrates the recursive least squares identification method with a variable forgetting factor to estimate at each iteration the parameters of a linear structure model used for multi-step ahead prediction. For a system with constraints on the process variables, the resulting optimization problem of GPC is solved using quadratic programming based on the Alternate Direction Method of Multipliers, which allows the control signal to be obtained with small computational effort. In order to validate the proposed algorithm an experimental case study that considers the speed control of a direct current motor and the proposed controller embedded in a microcontroller STM32F303K8T6 is presented. Experimental results use as baseline the GPC with fixed model parameters and show that the proposed fast adaptive predictive control structure is able to keep almost the same transient response for all the considered operating points of the motor, while GPC presents high oscillations at operating conditions far from the one used to obtain the nominal model. Even though the proposed controller needs to solve two optimization problems at each sampling instant, it can run about 60 times in a second in the microcontroller used in this study https://periodicos.ufv.br/jcec/article/view/9376Generalized Predictive ControlAdaptive ControlDigital ControlEmbedded Systems |
| spellingShingle | S. B. ROVEA RODOLFO FLESCH FAST ADAPTIVE GENERALIZED PREDICTIVE CONTROL FOR SYSTEMS WITH VARIABLE PARAMETERS The Journal of Engineering and Exact Sciences Generalized Predictive Control Adaptive Control Digital Control Embedded Systems |
| title | FAST ADAPTIVE GENERALIZED PREDICTIVE CONTROL FOR SYSTEMS WITH VARIABLE PARAMETERS |
| title_full | FAST ADAPTIVE GENERALIZED PREDICTIVE CONTROL FOR SYSTEMS WITH VARIABLE PARAMETERS |
| title_fullStr | FAST ADAPTIVE GENERALIZED PREDICTIVE CONTROL FOR SYSTEMS WITH VARIABLE PARAMETERS |
| title_full_unstemmed | FAST ADAPTIVE GENERALIZED PREDICTIVE CONTROL FOR SYSTEMS WITH VARIABLE PARAMETERS |
| title_short | FAST ADAPTIVE GENERALIZED PREDICTIVE CONTROL FOR SYSTEMS WITH VARIABLE PARAMETERS |
| title_sort | fast adaptive generalized predictive control for systems with variable parameters |
| topic | Generalized Predictive Control Adaptive Control Digital Control Embedded Systems |
| url | https://periodicos.ufv.br/jcec/article/view/9376 |
| work_keys_str_mv | AT sbrovea fastadaptivegeneralizedpredictivecontrolforsystemswithvariableparameters AT rodolfoflesch fastadaptivegeneralizedpredictivecontrolforsystemswithvariableparameters |