论文标题
神经ODE作为非线性最佳控制的反馈政策
Neural ODEs as Feedback Policies for Nonlinear Optimal Control
论文作者
论文摘要
神经普通微分方程（神经ODE）定义了使用神经网络的连续时间动态系统。最近对其建模应用的兴趣引发了跨越混合系统识别问题和时间序列分析的兴趣。在这项工作中，我们建议使用能够满足状态和控制约束的神经控制政策来解决非线性最佳控制问题。控制策略优化被视为神经ode问题，以有效利用动态系统模型的可用性。我们在两个受约束的系统中展示了这种确定性神经政策的功效：受控的范德尔系统和生物反应器控制问题。这种方法代表了非线性控制问题的顽固性闭环解决方案的实用近似。
Neural ordinary differential equations (Neural ODEs) define continuous time dynamical systems with neural networks. The interest in their application for modelling has sparked recently, spanning hybrid system identification problems and time series analysis. In this work we propose the use of a neural control policy capable of satisfying state and control constraints to solve nonlinear optimal control problems. The control policy optimization is posed as a Neural ODE problem to efficiently exploit the availability of a dynamical system model. We showcase the efficacy of this type of deterministic neural policies in two constrained systems: the controlled Van der Pol system and a bioreactor control problem. This approach represents a practical approximation to the intractable closed-loop solution of nonlinear control problems.
