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Incremental Model-Based Global Dual Heuristic Programming for Flight Control ?

机译:基于增量模型的飞行控制全局双重启发式编程

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This paper proposes a novel adaptive dynamic programming method, called Incremental model-based Global Dual Heuristic Programming, to generate a self-learning adaptive controller, in the absence of sufficient prior knowledge of system dynamics. An incremental technique is employed for online model identification, instead of the artificial neural networks commonly used in conventional Global Dual Heuristic Programming. The incremental model has the capability of tackling nonlinearity and uncertainty of the plant, but can also guarantee high precision of online identification without the requirement of offline training. On the basis of the identified model, two neural networks are adopted to facilitate the implementation of the self-learning controller, by approximating the cost-to-go and its derivatives and the control policy, respectively. Both methods are applied to a tracking control problem of a nonlinear aerospace system and the results show that the proposed method outperforms conventional Global Dual Heuristic Programming in online learning speed, tracking precision and robustness to variation of initial system states and network weights.
机译:本文提出了一种新颖的自适应动态规划方法,称为基于增量模型的全局双重启发式编程,以在缺乏足够的系统动力学先验知识的情况下生成自学习自适应控制器。增量技术用于在线模型识别,而不是常规的全局双重启发式编程中常用的人工神经网络。增量模型具有处理工厂非线性和不确定性的能力,但也可以保证在线识别的高精度,而无需离线培训。在识别出的模型的基础上,采用了两个神经网络,分别通过估算成本和衍生工具的成本以及控制策略,来促进自学习控制器的实现。两种方法都适用于非线性航空航天系统的跟踪控制问题,结果表明,该方法在在线学习速度,跟踪精度以及对初始系统状态和网络权重变化的鲁棒性方面均优于常规的全局双重启发式编程。

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