论文标题
对预测控制的学习：双重高斯过程方法
Learning For Predictive Control: A Dual Gaussian Process Approach
论文作者
论文摘要
基于模型的控制设计中的一个重要问题是，系统的准确动态模型通常是非线性，复杂且昂贵的。这限制了实践中可实现的控制性能。基于高斯过程（GP）的系统模型估计是直接从输入/输出数据中学习未知动态的有效工具。但是，常规的基于GP的控制方法通常会忽略与系统操作过程中累积数据相关的计算成本以及如何处理连续适应中的遗忘。在本文中，我们提出了一种基于新颖的双高斯流程（DGP）模型预测控制（MPC）策略，该策略可以有效利用基于在线学习的预测控制，而不会造成灾难性遗忘的危险。由生物启发的DGP结构是长期GP和短期GP的组合，其中长期GP用于保持记忆中的学习知识，并采用短期GP来迅速补偿在线操作期间未知动态。此外，提出了一种新颖的递归在线更新策略，以在线操作期间连续改善学习模型。通过数值模拟证明了拟议策略的有效性。
An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control (MPC) strategy that enables efficient use of online learning based predictive control without the danger of catastrophic forgetting. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation. Effectiveness of the proposed strategy is demonstrated via numerical simulations.
