论文标题
具有安全保证的可区分预测控制：控制屏障功能方法
Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach
论文作者
论文摘要
我们根据控制屏障功能开发了一种具有安全性和鲁棒性保证的新型可区分预测控制（DPC）。 DPC是一种基于学习的基于学习的方法，用于获得近似解决方案，以解决明确的模型预测控制（MPC）问题。在DPC中，神经网络参数参数的预测控制策略是通过自动差异通过MPC问题获得的直接策略梯度来优化的。所提出的方法利用了一种新形式的采样数据屏障功能来在DPC设置中执行离线和在线安全要求，同时仅中断安全集合边界附近的基于神经网络的控制器。在模拟中证明了拟议方法的有效性。
We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees based on control barrier functions. DPC is an unsupervised learning-based method for obtaining approximate solutions to explicit model predictive control (MPC) problems. In DPC, the predictive control policy parametrized by a neural network is optimized offline via direct policy gradients obtained by automatic differentiation of the MPC problem. The proposed approach exploits a new form of sampled-data barrier function to enforce offline and online safety requirements in DPC settings while only interrupting the neural network-based controller near the boundary of the safe set. The effectiveness of the proposed approach is demonstrated in simulation.
