Nonlinear model predictive control algorithm with iterative nonlinear prediction and linearization for long short-term memory network models
In this paper, a practical nonlinear model predictive control with iterative nonlinear prediction and linearization is proposed, considering a long short-term memory (LSTM) artificial neural network (PNMPCi-LSTM) as process model for making the predictions. The prediction model is divided into two p...
Ausführliche Beschreibung
Autor*in: |
Schwedersky, Bernardo B. [verfasserIn] Flesch, Rodolfo C.C. [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Schlagwörter: |
Practical nonlinear model predictive control |
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Übergeordnetes Werk: |
Enthalten in: Engineering applications of artificial intelligence - Amsterdam [u.a.] : Elsevier Science, 1988, 115 |
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Übergeordnetes Werk: |
volume:115 |
DOI / URN: |
10.1016/j.engappai.2022.105247 |
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Katalog-ID: |
ELV00843008X |
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100 | 1 | |a Schwedersky, Bernardo B. |e verfasserin |4 aut | |
245 | 1 | 0 | |a Nonlinear model predictive control algorithm with iterative nonlinear prediction and linearization for long short-term memory network models |
264 | 1 | |c 2022 | |
336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
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520 | |a In this paper, a practical nonlinear model predictive control with iterative nonlinear prediction and linearization is proposed, considering a long short-term memory (LSTM) artificial neural network (PNMPCi-LSTM) as process model for making the predictions. The prediction model is divided into two portions, the base output prediction, obtained with the LSTM nonlinear model, and the incremental output prediction, obtained using a linearized version of the LSTM model. The base response and the dynamic matrix of the system, which is obtained using the linearized version, are used to find an optimal control effort by solving a quadratic programming problem. This procedure is performed iteratively by updating the base input with the candidate control effort until the incremental response term is small enough compared with the base response term. The advantages of the proposed method in terms of performance and computing times are illustrated using the control of a simulated nonlinear neutralization reactor. For the evaluated case study, the results show that by using the proposed iterative procedure the closed-loop performance measured using the integral absolute error is improved by 8% for a setpoint tracking scenario while keeping the computation times within reasonable levels. In addition, the results support the idea that the proposed PNMPCi-LSTM is an alternative to implement a nonlinear MPC with reasonable computation times. | ||
650 | 4 | |a Practical nonlinear model predictive control | |
650 | 4 | |a Long short-term memory | |
650 | 4 | |a Iterative nonlinear prediction | |
650 | 4 | |a Nonlinear control | |
700 | 1 | |a Flesch, Rodolfo C.C. |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Engineering applications of artificial intelligence |d Amsterdam [u.a.] : Elsevier Science, 1988 |g 115 |h Online-Ressource |w (DE-627)308447832 |w (DE-600)1502275-4 |w (DE-576)094752524 |x 0952-1976 |7 nnns |
773 | 1 | 8 | |g volume:115 |
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912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4393 | ||
936 | b | k | |a 50.23 |j Regelungstechnik |j Steuerungstechnik |
936 | b | k | |a 54.72 |j Künstliche Intelligenz |
951 | |a AR | ||
952 | |d 115 |
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2022 |
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50.23 54.72 |
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2022 |
allfields |
10.1016/j.engappai.2022.105247 doi (DE-627)ELV00843008X (ELSEVIER)S0952-1976(22)00317-7 DE-627 ger DE-627 rda eng 004 DE-600 50.23 bkl 54.72 bkl Schwedersky, Bernardo B. verfasserin aut Nonlinear model predictive control algorithm with iterative nonlinear prediction and linearization for long short-term memory network models 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a practical nonlinear model predictive control with iterative nonlinear prediction and linearization is proposed, considering a long short-term memory (LSTM) artificial neural network (PNMPCi-LSTM) as process model for making the predictions. The prediction model is divided into two portions, the base output prediction, obtained with the LSTM nonlinear model, and the incremental output prediction, obtained using a linearized version of the LSTM model. The base response and the dynamic matrix of the system, which is obtained using the linearized version, are used to find an optimal control effort by solving a quadratic programming problem. This procedure is performed iteratively by updating the base input with the candidate control effort until the incremental response term is small enough compared with the base response term. The advantages of the proposed method in terms of performance and computing times are illustrated using the control of a simulated nonlinear neutralization reactor. For the evaluated case study, the results show that by using the proposed iterative procedure the closed-loop performance measured using the integral absolute error is improved by 8% for a setpoint tracking scenario while keeping the computation times within reasonable levels. In addition, the results support the idea that the proposed PNMPCi-LSTM is an alternative to implement a nonlinear MPC with reasonable computation times. Practical nonlinear model predictive control Long short-term memory Iterative nonlinear prediction Nonlinear control Flesch, Rodolfo C.C. verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 115 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:115 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.23 Regelungstechnik Steuerungstechnik 54.72 Künstliche Intelligenz AR 115 |
spelling |
10.1016/j.engappai.2022.105247 doi (DE-627)ELV00843008X (ELSEVIER)S0952-1976(22)00317-7 DE-627 ger DE-627 rda eng 004 DE-600 50.23 bkl 54.72 bkl Schwedersky, Bernardo B. verfasserin aut Nonlinear model predictive control algorithm with iterative nonlinear prediction and linearization for long short-term memory network models 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a practical nonlinear model predictive control with iterative nonlinear prediction and linearization is proposed, considering a long short-term memory (LSTM) artificial neural network (PNMPCi-LSTM) as process model for making the predictions. The prediction model is divided into two portions, the base output prediction, obtained with the LSTM nonlinear model, and the incremental output prediction, obtained using a linearized version of the LSTM model. The base response and the dynamic matrix of the system, which is obtained using the linearized version, are used to find an optimal control effort by solving a quadratic programming problem. This procedure is performed iteratively by updating the base input with the candidate control effort until the incremental response term is small enough compared with the base response term. The advantages of the proposed method in terms of performance and computing times are illustrated using the control of a simulated nonlinear neutralization reactor. For the evaluated case study, the results show that by using the proposed iterative procedure the closed-loop performance measured using the integral absolute error is improved by 8% for a setpoint tracking scenario while keeping the computation times within reasonable levels. In addition, the results support the idea that the proposed PNMPCi-LSTM is an alternative to implement a nonlinear MPC with reasonable computation times. Practical nonlinear model predictive control Long short-term memory Iterative nonlinear prediction Nonlinear control Flesch, Rodolfo C.C. verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 115 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:115 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.23 Regelungstechnik Steuerungstechnik 54.72 Künstliche Intelligenz AR 115 |
allfields_unstemmed |
10.1016/j.engappai.2022.105247 doi (DE-627)ELV00843008X (ELSEVIER)S0952-1976(22)00317-7 DE-627 ger DE-627 rda eng 004 DE-600 50.23 bkl 54.72 bkl Schwedersky, Bernardo B. verfasserin aut Nonlinear model predictive control algorithm with iterative nonlinear prediction and linearization for long short-term memory network models 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a practical nonlinear model predictive control with iterative nonlinear prediction and linearization is proposed, considering a long short-term memory (LSTM) artificial neural network (PNMPCi-LSTM) as process model for making the predictions. The prediction model is divided into two portions, the base output prediction, obtained with the LSTM nonlinear model, and the incremental output prediction, obtained using a linearized version of the LSTM model. The base response and the dynamic matrix of the system, which is obtained using the linearized version, are used to find an optimal control effort by solving a quadratic programming problem. This procedure is performed iteratively by updating the base input with the candidate control effort until the incremental response term is small enough compared with the base response term. The advantages of the proposed method in terms of performance and computing times are illustrated using the control of a simulated nonlinear neutralization reactor. For the evaluated case study, the results show that by using the proposed iterative procedure the closed-loop performance measured using the integral absolute error is improved by 8% for a setpoint tracking scenario while keeping the computation times within reasonable levels. In addition, the results support the idea that the proposed PNMPCi-LSTM is an alternative to implement a nonlinear MPC with reasonable computation times. Practical nonlinear model predictive control Long short-term memory Iterative nonlinear prediction Nonlinear control Flesch, Rodolfo C.C. verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 115 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:115 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.23 Regelungstechnik Steuerungstechnik 54.72 Künstliche Intelligenz AR 115 |
allfieldsGer |
10.1016/j.engappai.2022.105247 doi (DE-627)ELV00843008X (ELSEVIER)S0952-1976(22)00317-7 DE-627 ger DE-627 rda eng 004 DE-600 50.23 bkl 54.72 bkl Schwedersky, Bernardo B. verfasserin aut Nonlinear model predictive control algorithm with iterative nonlinear prediction and linearization for long short-term memory network models 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a practical nonlinear model predictive control with iterative nonlinear prediction and linearization is proposed, considering a long short-term memory (LSTM) artificial neural network (PNMPCi-LSTM) as process model for making the predictions. The prediction model is divided into two portions, the base output prediction, obtained with the LSTM nonlinear model, and the incremental output prediction, obtained using a linearized version of the LSTM model. The base response and the dynamic matrix of the system, which is obtained using the linearized version, are used to find an optimal control effort by solving a quadratic programming problem. This procedure is performed iteratively by updating the base input with the candidate control effort until the incremental response term is small enough compared with the base response term. The advantages of the proposed method in terms of performance and computing times are illustrated using the control of a simulated nonlinear neutralization reactor. For the evaluated case study, the results show that by using the proposed iterative procedure the closed-loop performance measured using the integral absolute error is improved by 8% for a setpoint tracking scenario while keeping the computation times within reasonable levels. In addition, the results support the idea that the proposed PNMPCi-LSTM is an alternative to implement a nonlinear MPC with reasonable computation times. Practical nonlinear model predictive control Long short-term memory Iterative nonlinear prediction Nonlinear control Flesch, Rodolfo C.C. verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 115 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:115 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.23 Regelungstechnik Steuerungstechnik 54.72 Künstliche Intelligenz AR 115 |
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10.1016/j.engappai.2022.105247 doi (DE-627)ELV00843008X (ELSEVIER)S0952-1976(22)00317-7 DE-627 ger DE-627 rda eng 004 DE-600 50.23 bkl 54.72 bkl Schwedersky, Bernardo B. verfasserin aut Nonlinear model predictive control algorithm with iterative nonlinear prediction and linearization for long short-term memory network models 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a practical nonlinear model predictive control with iterative nonlinear prediction and linearization is proposed, considering a long short-term memory (LSTM) artificial neural network (PNMPCi-LSTM) as process model for making the predictions. The prediction model is divided into two portions, the base output prediction, obtained with the LSTM nonlinear model, and the incremental output prediction, obtained using a linearized version of the LSTM model. The base response and the dynamic matrix of the system, which is obtained using the linearized version, are used to find an optimal control effort by solving a quadratic programming problem. This procedure is performed iteratively by updating the base input with the candidate control effort until the incremental response term is small enough compared with the base response term. The advantages of the proposed method in terms of performance and computing times are illustrated using the control of a simulated nonlinear neutralization reactor. For the evaluated case study, the results show that by using the proposed iterative procedure the closed-loop performance measured using the integral absolute error is improved by 8% for a setpoint tracking scenario while keeping the computation times within reasonable levels. In addition, the results support the idea that the proposed PNMPCi-LSTM is an alternative to implement a nonlinear MPC with reasonable computation times. Practical nonlinear model predictive control Long short-term memory Iterative nonlinear prediction Nonlinear control Flesch, Rodolfo C.C. verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 115 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:115 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.23 Regelungstechnik Steuerungstechnik 54.72 Künstliche Intelligenz AR 115 |
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Nonlinear model predictive control algorithm with iterative nonlinear prediction and linearization for long short-term memory network models |
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Nonlinear model predictive control algorithm with iterative nonlinear prediction and linearization for long short-term memory network models |
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Schwedersky, Bernardo B. |
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10.1016/j.engappai.2022.105247 |
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nonlinear model predictive control algorithm with iterative nonlinear prediction and linearization for long short-term memory network models |
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Nonlinear model predictive control algorithm with iterative nonlinear prediction and linearization for long short-term memory network models |
abstract |
In this paper, a practical nonlinear model predictive control with iterative nonlinear prediction and linearization is proposed, considering a long short-term memory (LSTM) artificial neural network (PNMPCi-LSTM) as process model for making the predictions. The prediction model is divided into two portions, the base output prediction, obtained with the LSTM nonlinear model, and the incremental output prediction, obtained using a linearized version of the LSTM model. The base response and the dynamic matrix of the system, which is obtained using the linearized version, are used to find an optimal control effort by solving a quadratic programming problem. This procedure is performed iteratively by updating the base input with the candidate control effort until the incremental response term is small enough compared with the base response term. The advantages of the proposed method in terms of performance and computing times are illustrated using the control of a simulated nonlinear neutralization reactor. For the evaluated case study, the results show that by using the proposed iterative procedure the closed-loop performance measured using the integral absolute error is improved by 8% for a setpoint tracking scenario while keeping the computation times within reasonable levels. In addition, the results support the idea that the proposed PNMPCi-LSTM is an alternative to implement a nonlinear MPC with reasonable computation times. |
abstractGer |
In this paper, a practical nonlinear model predictive control with iterative nonlinear prediction and linearization is proposed, considering a long short-term memory (LSTM) artificial neural network (PNMPCi-LSTM) as process model for making the predictions. The prediction model is divided into two portions, the base output prediction, obtained with the LSTM nonlinear model, and the incremental output prediction, obtained using a linearized version of the LSTM model. The base response and the dynamic matrix of the system, which is obtained using the linearized version, are used to find an optimal control effort by solving a quadratic programming problem. This procedure is performed iteratively by updating the base input with the candidate control effort until the incremental response term is small enough compared with the base response term. The advantages of the proposed method in terms of performance and computing times are illustrated using the control of a simulated nonlinear neutralization reactor. For the evaluated case study, the results show that by using the proposed iterative procedure the closed-loop performance measured using the integral absolute error is improved by 8% for a setpoint tracking scenario while keeping the computation times within reasonable levels. In addition, the results support the idea that the proposed PNMPCi-LSTM is an alternative to implement a nonlinear MPC with reasonable computation times. |
abstract_unstemmed |
In this paper, a practical nonlinear model predictive control with iterative nonlinear prediction and linearization is proposed, considering a long short-term memory (LSTM) artificial neural network (PNMPCi-LSTM) as process model for making the predictions. The prediction model is divided into two portions, the base output prediction, obtained with the LSTM nonlinear model, and the incremental output prediction, obtained using a linearized version of the LSTM model. The base response and the dynamic matrix of the system, which is obtained using the linearized version, are used to find an optimal control effort by solving a quadratic programming problem. This procedure is performed iteratively by updating the base input with the candidate control effort until the incremental response term is small enough compared with the base response term. The advantages of the proposed method in terms of performance and computing times are illustrated using the control of a simulated nonlinear neutralization reactor. For the evaluated case study, the results show that by using the proposed iterative procedure the closed-loop performance measured using the integral absolute error is improved by 8% for a setpoint tracking scenario while keeping the computation times within reasonable levels. In addition, the results support the idea that the proposed PNMPCi-LSTM is an alternative to implement a nonlinear MPC with reasonable computation times. |
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Nonlinear model predictive control algorithm with iterative nonlinear prediction and linearization for long short-term memory network models |
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