Worst-case Satisfaction of STL Specifications Using Feedforward Neural Network Controllers: A Lagrange Multipliers Approach
Worst-case Satisfaction of STL Specifications Using Feedforward Neural Network Controllers: A Lagrange Multipliers Approach
复制标题
使用前馈神经网络控制器满足 STL 规范的最坏情况：拉格朗日乘子方法
DOI：
10.1109/ita50056.2020.9244969
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Georgios Fainekos
中科院分区：
文献类型：
--
作者：
Shakiba Yaghoubi;Georgios Fainekos
提出了一种用于非线性系统反馈神经网络控制器设计的强化学习方法。给定一个信号时序逻辑（STL）规范，需要由系统满足一组初始条件，神经网络参数进行调整，以最大限度地满足STL公式。该框架是基于STL公式的鲁棒性的最大-最小制定。最大化是通过一个拉格朗日乘数法解决的，而最小化对应于一个证伪问题。我们提出了我们的研究结果的车辆和四旋翼模型，并证明我们的方法减少了训练时间超过50%的基线方法相比。
In this paper, a reinforcement learning approach for designing feedback neural network controllers for nonlinear systems is proposed. Given a Signal Temporal Logic (STL) specification which needs to be satisfied by the system over a set of initial conditions, the neural network parameters are tuned in order to maximize the satisfaction of the STL formula. The framework is based on a max-min formulation of the robustness of the STL formula. The maximization is solved through a Lagrange multipliers method, while the minimization corresponds to a falsification problem. We present our results on a vehicle and a quadrotor model and demonstrate that our approach reduces the training time more than 50 percent compared to the baseline approach.