Learning-based Optimal Control of Constrained Switched Linear Systems using Neural Networks

Lukas Markolf, O. Stursberg
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引用次数: 1

Abstract

This work considers (deep) artificial feed-forward neural networks as parametric approximators in optimal control of discrete-time switched linear systems with controlled switching. The proposed approach is based on approximate dynamic programming and allows the fast computation of (sub-)optimal discrete and continuous control inputs, either by approximating the optimal cost-to-go functions or by approximating the optimal discrete and continuous input policies. An important property of the approach is the satisfaction of polytopic state and input constraints, which is crucial for ensuring safety, as required in many control applications. A numeric example is provided for illustration and evaluation of the approaches.
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基于学习的约束切换线性系统的神经网络最优控制
本文将(深度)人工前馈神经网络作为参数逼近器应用于具有控制开关的离散时间切换线性系统的最优控制。所提出的方法基于近似动态规划,通过逼近最优成本函数或逼近最优离散和连续输入策略,可以快速计算(次)最优离散和连续控制输入。该方法的一个重要特性是满足多面体状态和输入约束,这对于确保安全性至关重要,在许多控制应用中都是如此。给出了一个数值例子来说明和评价这些方法。
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