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首页> 外文会议>Mediterranean Conference on Control and Automation >Learning to extrapolate an optimal tracking control behavior towards new tracking tasks in a hierarchical primitive-based framework*
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Learning to extrapolate an optimal tracking control behavior towards new tracking tasks in a hierarchical primitive-based framework*

机译：学习在基于分层原语的框架中推出新的跟踪任务的最佳跟踪控制行为

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摘要

The proposed hierarchical learning framework induces a generalized optimal tracking behavior for a control system. The L1 learning level ensures indirect closed-loop system (CLS) linearization using nonlinear state-feedback control with neural networks, based on a virtual state constructed from input-output samples of the assumed observable underlying controlled system. The linearized CLS behavior is learned to match a linear output reference model and prepares the subsequent L2 level learning of reference input–controlled output pairs called primitives, using an experiment-driven Iterative Learning Control. The learned primitives serve as a database of previous experiences which allow for the final L3 level to predict the optimal reference input which ensures that the CLS output perfectly tracks a new unseen trajectory, without relearning. With unknown dynamics of the underlying system, and being able to generalize a tracking behavior, the proposed hierarchical learning is validated on a multivariable hybrid electrical-software system. It shows traits of learning, memorization, generalizable behavior from past experience and adaptation for complex nonlinear systems, as some of the intelligent features required by Industry 4.0’s standards.
机译：所提出的分层学习框架为控制系统引起广泛的最佳跟踪行为。 L1学习级别根据从假定的可观察底层控制系统的输入输出样本构成的虚拟状态，确保使用非线性状态反馈控制的间接闭环系统（CLS）线性化。学习线性化的CLS行为以匹配线性输出参考模型，并准备使用实验驱动的迭代学习控制来匹配名为基元的参考输入控制输出对的后续L2电平学习。学习的基元作为先前体验的数据库，其允许最终的L3级别来预测最佳参考输入，这确保了CLS输出完全跟踪新的看不见的轨迹而不复制。由于底层系统的未知动态，并且能够概括跟踪行为，所提出的分层学习在多变量混合电气软件系统上验证。它显示了从过去经验和适应复杂非线性系统的学习，记忆，概括行为的特征，作为行业4.0标准所需的一些智能功能。

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