用神经网络快速学习未知非线性系统，直接用于高效控制。
Reservoir Predictive Path Integral Control for Unknown Nonlinear Dynamics
- 结合时滞神经网络与路径积分控制，无需线性化
- 在杜芬振子和四水箱系统上降低60%控制成本
- 适合需要实时自适应控制的工程场景
神经网络在数据驱动的非线性动力系统控制中应用广泛，但未知动态的快速在线识别与控制仍是核心挑战。本文将回声状态网络（ESNs）——基于循环神经网络的储层计算模型——与基于采样的模型预测路径积分（MPPI）控制相结合，提出储层预测路径积分（RPPI）框架。该方法利用ESN实现非线性动态的快速学习，并将学习到的非线性直接用于MPPI控制计算，避免了线性化近似。进一步扩展为不确定性感知的RPPI（URPPI），通过将ESN输出权重视为随机变量，在其分布上最小化期望代价，以应对识别误差，实现鲁棒随机控制。在杜芬振子和四水箱系统上的实验表明，URPPI相比传统基于二次规划的模型预测控制，控制成本最多降低60%。
原文摘要 · Abstract (English)
Neural networks have found extensive application in data-driven control of nonlinear dynamical systems, yet fast online identification and control of unknown dynamics remain central challenges. To meet these challenges, this paper integrates echo-state networks (ESNs)--reservoir computing models implemented with recurrent neural networks--and model predictive path integral (MPPI) control--sampling-based variants of model predictive control. The proposed reservoir predictive path integral (RPPI) enables fast learning of nonlinear dynamics with ESNs and exploits the learned nonlinearities directly in MPPI control computation without linearization approximations. This framework is further extended to uncertainty-aware RPPI (URPPI), which achieves robust stochastic control by treating ESN output weights as random variables and minimizing an expected cost over their distribution to account for identification errors. Experiments on controlling a Duffing oscillator and a four-tank system demonstrate that URPPI improves control performance, reducing control costs by up to 60% compared to traditional quadratic programming-based model predictive control methods.
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