A Conjugate Gradient-Based BPTT-Like Optimal Control Algorithm With Vehicle Dynamics Control Application
IEEE Transactions on Control System Technology, Vol. 19, No. 6, pp. 1587-15952011The 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. automotive applications; conjugate gradient methods; optimal control; road vehicle control; vehicle dynamics
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IEEE Transactions on Control System Technology, Vol. 19, No. 6, pp. 1587-1595
2011
Cited by 26
▾
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[1] Control of Robot Motion in Radial Mass Density Field🔗WSEAS transactions on systems and control, 2023
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[2] Trajectory Planning for an Autonomous Vehicle with Conflicting Moving Objects Along a Fixed Path – An Exact Solution Method🔗Transportation Research Part B: Methodological, 2023
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[3] A Model of Spatial Cell Development in Rat Hippocampus Based on Artificial Neural Network🔗Journal of Healthcare Engineering, 2021
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[5] Two-phase A*: A real-time global motion planning method for non-holonomic unmanned ground vehicles🔗Proceedings of the Institution of mechanical engineers. Part D, journal of automobile engineering, 2020
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[7] Any Curve Path Following of Snake-like Robots🔗IEEE International Conference on Robotics and Biomimetics, 2019
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[8] Long-term time series prediction based on deep denoising recurrent temporal restricted Boltzmann machine network🔗ACM Cloud and Autonomic Computing Conference, 2017
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[10] Global convergence properties of conjugate gradient methods for optimal control problems🔗Cybersecurity and Cyberforensics Conference, 2016
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[11] An analytical fuzzy-based approach to -gain optimal control of input-affine nonlinear systems using Newton-type algorithm🔗International Journal of Systems Science, 2015
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[13] Sliding-mode and proportional-derivative-type motion control with radial basis function neural network based estimators for wheeled vehicles🔗International Journal of Systems Science, 2014
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[14] Sliding-mode control of a wheeled vehicle using neural network estimator🔗Asian Control Conference, 2013
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[16] A numerical algorithm for nonlinear L2-gain optimal control with application to vehicle yaw stability control🔗IEEE Conference on Decision and Control, 2012
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[17] Numerical algorithm for nonlinear state feedback ℌ∞ optimal control problem🔗2012 20th Mediterranean Conference on Control & Automation (MED), 2012
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[18] Initial conditions optimization of nonlinear dynamic systems with applications to output identification and control🔗2012 20th Mediterranean Conference on Control & Automation (MED), 2012
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[19] A comparison of feed-forward and recurrent neural networks in time series forecasting🔗IEEE Conference on Computational Intelligence for Financial Engineering & Economics, 2012
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[21] A Conjugate Gradient-Based BPTT-Like Optimal Control Algorithm With Vehicle Dynamics Control Application🔗IEEE Transactions on Control Systems Technology, 2009