论文标题
神经Lyapunov控制
Neural Lyapunov Control
论文作者
论文摘要
我们提出了用于学习控制策略的新方法和神经网络Lyapunov功能，用于非线性控制问题，并证明了稳定性的保证。该框架由一个试图找到控制和Lyapunov功能的学习者组成，以及一个发现反描述以快速指导学习者实现解决方案的伪造者。当伪造者发现未发现反例时，该过程将终止，在这种情况下，受控的非线性系统被证明是稳定的。该方法大大简化了Lyapunov控制设计的过程，提供端到端的正确性保证，并且可以比LQR和SOS/SDP等现有方法获得更大的吸引力区域。我们展示了有关新方法如何获得高质量解决方案的实验。
We propose new methods for learning control policies and neural network Lyapunov functions for nonlinear control problems, with provable guarantee of stability. The framework consists of a learner that attempts to find the control and Lyapunov functions, and a falsifier that finds counterexamples to quickly guide the learner towards solutions. The procedure terminates when no counterexample is found by the falsifier, in which case the controlled nonlinear system is provably stable. The approach significantly simplifies the process of Lyapunov control design, provides end-to-end correctness guarantee, and can obtain much larger regions of attraction than existing methods such as LQR and SOS/SDP. We show experiments on how the new methods obtain high-quality solutions for challenging control problems.
