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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Hamiltonian-Driven Adaptive Dynamic Programming for Continuous Nonlinear Dynamical Systems
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Hamiltonian-Driven Adaptive Dynamic Programming for Continuous Nonlinear Dynamical Systems

机译：连续非线性动力系统的哈密顿驱动自适应动态规划

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摘要

This paper presents a Hamiltonian-driven framework of adaptive dynamic programming (ADP) for continuous time nonlinear systems, which consists of evaluation of an admissible control, comparison between two different admissible policies with respect to the corresponding the performance function, and the performance improvement of an admissible control. It is showed that the Hamiltonian can serve as the temporal difference for continuous-time systems. In the Hamiltonian-driven ADP, the critic network is trained to output the value gradient. Then, the inner product between the critic and the system dynamics produces the value derivative. Under some conditions, the minimization of the Hamiltonian functional is equivalent to the value function approximation. An iterative algorithm starting from an arbitrary admissible control is presented for the optimal control approximation with its convergence proof. The implementation is accomplished by a neural network approximation. Two simulation studies demonstrate the effectiveness of Hamiltonian-driven ADP.
机译：本文提出了一种用于连续时间非线性系统的哈密顿驱动的自适应动态规划（ADP）框架，该框架包括对可允许控制的评估，两个不同的可允许策略相对于相应的性能函数的比较以及对性能的改进。可接受的控件。结果表明，哈密顿量可以作为连续时间系统的时间差。在汉密尔顿驱动的ADP中，训练批评器网络以输出值梯度。然后，评论家与系统动力学之间的内在产物产生价值导数。在某些情况下，汉密尔顿函数的最小值等于值函数的近似值。提出了一种从任意容许控制出发的迭代算法，用于具有收敛性的最优控制近似。该实现通过神经网络逼近来实现。两项仿真研究证明了哈密顿驱动的ADP的有效性。

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