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A Conjugate Gradient-Based BPTT-Like Optimal Control Algorithm With Vehicle Dynamics Control Application
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DOI：10.1109/TCST.2010.2084088.png)
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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.
Keyword:
Automotive applications
conjugate gradient methods
optimal control
road vehicle control
vehicle dynamics
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ADVANCED SUPERVISED LEARNING IN MULTILAYER PERCEPTRONS - FROM BACKPROPAGATION TO ADAPTIVE LEARNING ALGORITHMS多层感知器中的高级监督学习-从反向传播到自适应学习算法
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