Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine
Engine power, brake-specific fuel consumption, and emissions relate closely to air ratio (i.e., lambda) among all the engine variables. An accurate and adaptive model for lambda prediction is essential to effective lambda control for long term. This paper utilizes an emerging technique, relevance ve...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2012/731825 |
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| _version_ | 1832567509563211776 |
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| author | Hang-cheong Wong Pak-kin Wong Chi-man Vong |
| author_facet | Hang-cheong Wong Pak-kin Wong Chi-man Vong |
| author_sort | Hang-cheong Wong |
| collection | DOAJ |
| description | Engine power, brake-specific fuel consumption, and emissions relate closely to air ratio (i.e., lambda) among all the engine variables. An accurate and adaptive model for lambda prediction is essential to effective lambda control for long term. This paper utilizes an emerging technique, relevance vector machine (RVM), to build a reliable time-dependent lambda model which can be continually updated whenever a sample is added to, or removed from, the estimated lambda model. The paper also presents a new model predictive control (MPC) algorithm for air-ratio regulation based on RVM. This study shows that the accuracy, training, and updating time of the RVM model are superior to the latest modelling methods, such as diagonal recurrent neural network (DRNN) and decremental least-squares support vector machine (DLSSVM). Moreover, the control algorithm has been implemented on a real car to test. Experimental results reveal that the control performance of the proposed relevance vector machine model predictive controller (RVMMPC) is also superior to DRNNMPC, support vector machine-based MPC, and conventional proportional-integral (PI) controller in production cars. Therefore, the proposed RVMMPC is a promising scheme to replace conventional PI controller for engine air-ratio control. |
| format | Article |
| id | doaj-art-47b0dc76c37048c1901842c3d5b06e28 |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-47b0dc76c37048c1901842c3d5b06e282025-02-03T01:01:16ZengWileyJournal of Control Science and Engineering1687-52491687-52572012-01-01201210.1155/2012/731825731825Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector MachineHang-cheong Wong0Pak-kin Wong1Chi-man Vong2Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Taipa 999078, MacauDepartment of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Taipa 999078, MacauDepartment of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa 999078, MacauEngine power, brake-specific fuel consumption, and emissions relate closely to air ratio (i.e., lambda) among all the engine variables. An accurate and adaptive model for lambda prediction is essential to effective lambda control for long term. This paper utilizes an emerging technique, relevance vector machine (RVM), to build a reliable time-dependent lambda model which can be continually updated whenever a sample is added to, or removed from, the estimated lambda model. The paper also presents a new model predictive control (MPC) algorithm for air-ratio regulation based on RVM. This study shows that the accuracy, training, and updating time of the RVM model are superior to the latest modelling methods, such as diagonal recurrent neural network (DRNN) and decremental least-squares support vector machine (DLSSVM). Moreover, the control algorithm has been implemented on a real car to test. Experimental results reveal that the control performance of the proposed relevance vector machine model predictive controller (RVMMPC) is also superior to DRNNMPC, support vector machine-based MPC, and conventional proportional-integral (PI) controller in production cars. Therefore, the proposed RVMMPC is a promising scheme to replace conventional PI controller for engine air-ratio control.http://dx.doi.org/10.1155/2012/731825 |
| spellingShingle | Hang-cheong Wong Pak-kin Wong Chi-man Vong Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine Journal of Control Science and Engineering |
| title | Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine |
| title_full | Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine |
| title_fullStr | Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine |
| title_full_unstemmed | Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine |
| title_short | Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine |
| title_sort | model predictive engine air ratio control using online sequential relevance vector machine |
| url | http://dx.doi.org/10.1155/2012/731825 |
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