论文标题
将系统识别与基于增强学习的MPC相结合
Combining system identification with reinforcement learning-based MPC
论文作者
论文摘要
在本文中，我们提出并比较在数据驱动的模型预测控制（MPC）的背景下，在组合系统识别（SYSID）和增强学习（RL）的方法。假设受控系统的已知模型结构，并考虑参数MPC，同时提出的方法：a）使用RL了解MPC的参数以优化性能，b）使用Sysid适合观察到的模型行为。使用简单的线性系统提出并评估了避免两个优化目标之间发生冲突的六种方法。基于仿真结果，分层，并行投影，零Space投影和奇异值投影达到了最佳性能。
In this paper we propose and compare methods for combining system identification (SYSID) and reinforcement learning (RL) in the context of data-driven model predictive control (MPC). Assuming a known model structure of the controlled system, and considering a parametric MPC, the proposed approach simultaneously: a) Learns the parameters of the MPC using RL in order to optimize performance, and b) fits the observed model behaviour using SYSID. Six methods that avoid conflicts between the two optimization objectives are proposed and evaluated using a simple linear system. Based on the simulation results, hierarchical, parallel projection, nullspace projection, and singular value projection achieved the best performance.
