论文标题
通过输出范围分析，基于神经网络的控制器的稳定性和可行性
Stability and feasibility of neural network-based controllers via output range analysis
论文作者
论文摘要
神经网络可以用作几种复杂控制方案（例如模型预测控制）的近似值。我们在本文中表明，它具有带有整流器线性单元的深层神经网络，因为激活功能需要满足以保证闭环系统的约束满意度和渐近稳定性。为此，我们介绍了神经网络控制器的参数描述，并使用混合构成线性编程公式来执行神经网络的输出范围分析。我们还提出了一种新的方法来修改神经网络控制器，以便它在平衡周围的区域中在LQR意义上发挥最佳性能。提出的方法可以通过模拟结果来说明神经网络控制器的分析和设计。
Neural networks can be used as approximations of several complex control schemes such as model predictive control. We show in this paper which properties deep neural networks with rectifier linear units as activation functions need to satisfy to guarantee constraint satisfaction and asymptotic stability of the closed-loop system. To do so, we introduce a parametric description of the neural network controller and use a mixed-integer linear programming formulation to perform output range analysis of neural networks. We also propose a novel method to modify a neural network controller such that it performs optimally in the LQR sense in a region surrounding the equilibrium. The proposed method enables the analysis and design of neural network controllers with formal safety guarantees as we illustrate with simulation results.
