Neural-network predictive control for nonlinear dynamic systems with time-delay
A new recurrent neural-network predictive feedback control structure for a class of uncertain nonlinear dynamic time-delay systems in canonical form is developed and analyzed. The dynamic system has constant input and feedback time delays due to a communications channel. The proposed control structu...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2003-03, Vol.14 (2), p.377-389 |
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| container_title | IEEE transaction on neural networks and learning systems |
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| creator | Jin-Quan Huang Lewis, F.L. |
| description | A new recurrent neural-network predictive feedback control structure for a class of uncertain nonlinear dynamic time-delay systems in canonical form is developed and analyzed. The dynamic system has constant input and feedback time delays due to a communications channel. The proposed control structure consists of a linearized subsystem local to the controlled plant and a remote predictive controller located at the master command station. In the local linearized subsystem, a recurrent neural network with on-line weight tuning algorithm is employed to approximate the dynamics of the time-delay-free nonlinear plant. No linearity in the unknown parameters is required. No preliminary off-line weight learning is needed. The remote controller is a modified Smith predictor that provides prediction and maintains the desired tracking performance; an extra robustifying term is needed to guarantee stability. Rigorous stability proofs are given using Lyapunov analysis. The result is an adaptive neural net compensation scheme for unknown nonlinear systems with time delays. A simulation example is provided to demonstrate the effectiveness of the proposed control strategy. |
| doi_str_mv | 10.1109/TNN.2003.809424 |
| format | Article |
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The dynamic system has constant input and feedback time delays due to a communications channel. The proposed control structure consists of a linearized subsystem local to the controlled plant and a remote predictive controller located at the master command station. In the local linearized subsystem, a recurrent neural network with on-line weight tuning algorithm is employed to approximate the dynamics of the time-delay-free nonlinear plant. No linearity in the unknown parameters is required. No preliminary off-line weight learning is needed. The remote controller is a modified Smith predictor that provides prediction and maintains the desired tracking performance; an extra robustifying term is needed to guarantee stability. Rigorous stability proofs are given using Lyapunov analysis. The result is an adaptive neural net compensation scheme for unknown nonlinear systems with time delays. 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The dynamic system has constant input and feedback time delays due to a communications channel. The proposed control structure consists of a linearized subsystem local to the controlled plant and a remote predictive controller located at the master command station. In the local linearized subsystem, a recurrent neural network with on-line weight tuning algorithm is employed to approximate the dynamics of the time-delay-free nonlinear plant. No linearity in the unknown parameters is required. No preliminary off-line weight learning is needed. The remote controller is a modified Smith predictor that provides prediction and maintains the desired tracking performance; an extra robustifying term is needed to guarantee stability. Rigorous stability proofs are given using Lyapunov analysis. The result is an adaptive neural net compensation scheme for unknown nonlinear systems with time delays. A simulation example is provided to demonstrate the effectiveness of the proposed control strategy.</description><subject>Communication channels</subject><subject>Communication system control</subject><subject>Delay effects</subject><subject>Feedback control</subject><subject>Linearity</subject><subject>Neural networks</subject><subject>Neurofeedback</subject><subject>Nonlinear dynamical systems</subject><subject>Predictive control</subject><subject>Recurrent neural networks</subject><subject>Robust stability</subject><subject>Studies</subject><issn>1045-9227</issn><issn>2162-237X</issn><issn>1941-0093</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0UFrFDEUwPEgFlurZw-CDD3oabYveZnMvKOUqoWyvdRzyGbeYurMZE1mLPvtTdmFggc9JZBfHiR_Id5JWEkJdHm_Xq8UAK46IK30C3EmScsagPBl2YNualKqPRWvc34AkLoB80qcyk5hBwrOxN2al-SGeuL5Maaf1S5xH_wcfnPl4zSnOFTbmKopTkOY2KWq309uDL7K-zzzmKvHMP-o5jBy3fPg9m_EydYNmd8e13Px_cv1_dW3-vbu683V59vaa9BzLb3vVYuGEPVGtk2rkEmCd63mTU_eaALgHiWDxI1Bg2j0tgfnwDBTg-fi02HuLsVfC-fZjiF7HgY3cVyyJZCtVNSY_8oWy8eRbqjIj_-UqtOtBOwKvPgLPsQlTeW9lhR0hjRiQZcH5FPMOfHW7lIYXdpbCfYpni3x7FM8e4hXbnw4jl02I_fP_lirgPcHEJj5-Vh2ZLDBP2lSnBk</recordid><startdate>20030301</startdate><enddate>20030301</enddate><creator>Jin-Quan Huang</creator><creator>Lewis, F.L.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The dynamic system has constant input and feedback time delays due to a communications channel. The proposed control structure consists of a linearized subsystem local to the controlled plant and a remote predictive controller located at the master command station. In the local linearized subsystem, a recurrent neural network with on-line weight tuning algorithm is employed to approximate the dynamics of the time-delay-free nonlinear plant. No linearity in the unknown parameters is required. No preliminary off-line weight learning is needed. The remote controller is a modified Smith predictor that provides prediction and maintains the desired tracking performance; an extra robustifying term is needed to guarantee stability. Rigorous stability proofs are given using Lyapunov analysis. The result is an adaptive neural net compensation scheme for unknown nonlinear systems with time delays. 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| subjects | Communication channels Communication system control Delay effects Feedback control Linearity Neural networks Neurofeedback Nonlinear dynamical systems Predictive control Recurrent neural networks Robust stability Studies |
| title | Neural-network predictive control for nonlinear dynamic systems with time-delay |
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