Imitation Learning With Stability and Safety Guarantees
Imitation Learning With Stability and Safety Guarantees
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DOI：
10.1109/lcsys.2021.3077861
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发表时间：
2020-12
影响因子：
3
通讯作者：
He Yin;P. Seiler;Ming Jin;M. Arcak
中科院分区：
文献类型：
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作者：
He Yin;P. Seiler;Ming Jin;M. Arcak
提出了一种通过模仿学习（IL）来学习具有稳定性和安全性的神经网络（NN）控制器的方法。通过将Lyapunov理论与局部二次约束结合以限制NN中的激活函数，通过将Lyapunov理论合并为NN控制器的线性时间变变系统来得出凸的稳定性和安全条件。这些条件纳入IL过程，该过程可最大程度地减少IL损失，并同时使与NN控制器相关的吸引区域的体积最大化。提出了一种基于乘数算法的交替方向方法来解决IL问题。该方法在车辆横向控制示例上进行了说明。
A method is presented to learn neural network (NN) controllers with stability and safety guarantees through imitation learning (IL). Convex stability and safety conditions are derived for linear time-invariant systems with NN controllers by merging Lyapunov theory with local quadratic constraints to bound the activation functions in the NN. These conditions are incorporated in the IL process, which minimizes the IL loss, and maximizes the volume of the region of attraction associated with the NN controller simultaneously. An alternating direction method of multipliers based algorithm is proposed to solve the IL problem. The method is illustrated on a vehicle lateral control example.