A Conjugate Gradient-Based BPTT-Like Optimal Control Algorithm With Vehicle Dynamics Control Application.

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Title: A Conjugate Gradient-Based BPTT-Like Optimal Control Algorithm With Vehicle Dynamics Control Application.
Authors: Kasac, Josip1, Deur, Joško1, Novakovic, Branko1, Kolmanovsky, Ilya V.2, Assadian, Francis3
Source: IEEE Transactions on Control Systems Technology. Nov2011, Vol. 19 Issue 6, p1587-1595. 9p.
Subjects: Motor vehicles, Heuristic algorithms, Conjugate gradient methods, Mathematical models, Artificial neural networks, Numerical calculations, Automatic control systems, Steering gear
Abstract: The paper presents a gradient-based algorithm for optimal control of nonlinear multivariable systems with control and state vectors constraints. The algorithm has a backward-in-time recurrent structure similar to the backpropagation-through-time algorithm, which is mostly used as a learning algorithm for dynamic neural networks. Other main features of the algorithm include the use of higher order Adams time-discretization schemes, numerical calculation of Jacobians, and advanced conjugate gradient methods for favorable convergence properties. The algorithm performance is illustrated on an example of off-line vehicle dynamics control optimization based on a realistic high-order vehicle model. The optimized control variables are active rear differential torque transfer and active rear steering road wheel angle, while the optimization tasks are trajectory tracking and roll minimization for a double lane change maneuver. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Control Systems Technology is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Conjugate Gradient-Based BPTT-Like Optimal Control Algorithm With Vehicle Dynamics Control Application.
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Control+Systems+Technology%22">IEEE Transactions on Control Systems Technology</searchLink>. Nov2011, Vol. 19 Issue 6, p1587-1595. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Motor+vehicles%22">Motor vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic+algorithms%22">Heuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Conjugate+gradient+methods%22">Conjugate gradient methods</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+calculations%22">Numerical calculations</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+control+systems%22">Automatic control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Steering+gear%22">Steering gear</searchLink>
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  Label: Abstract
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  Data: The paper presents a gradient-based algorithm for optimal control of nonlinear multivariable systems with control and state vectors constraints. The algorithm has a backward-in-time recurrent structure similar to the backpropagation-through-time algorithm, which is mostly used as a learning algorithm for dynamic neural networks. Other main features of the algorithm include the use of higher order Adams time-discretization schemes, numerical calculation of Jacobians, and advanced conjugate gradient methods for favorable convergence properties. The algorithm performance is illustrated on an example of off-line vehicle dynamics control optimization based on a realistic high-order vehicle model. The optimized control variables are active rear differential torque transfer and active rear steering road wheel angle, while the optimization tasks are trajectory tracking and roll minimization for a double lane change maneuver. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IEEE Transactions on Control Systems Technology is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1109/TCST.2010.2084088
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      – Code: eng
        Text: English
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        PageCount: 9
        StartPage: 1587
    Subjects:
      – SubjectFull: Motor vehicles
        Type: general
      – SubjectFull: Heuristic algorithms
        Type: general
      – SubjectFull: Conjugate gradient methods
        Type: general
      – SubjectFull: Mathematical models
        Type: general
      – SubjectFull: Artificial neural networks
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      – SubjectFull: Numerical calculations
        Type: general
      – SubjectFull: Automatic control systems
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      – SubjectFull: Steering gear
        Type: general
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      – TitleFull: A Conjugate Gradient-Based BPTT-Like Optimal Control Algorithm With Vehicle Dynamics Control Application.
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            NameFull: Kasac, Josip
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            NameFull: Novakovic, Branko
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            NameFull: Kolmanovsky, Ilya V.
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              M: 11
              Text: Nov2011
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