用递归网络提升小数据下的系统建模与预测控制性能
Reservoir computing for system identification and predictive control with limited data
- 采用回声状态网络替代传统RNN建模系统动态
- 预测时长更久，计算开销更低，控制代价下降30%以上
- 适合数据稀缺场景的工业控制系统优化
模型预测控制（MPC）依赖准确的系统动力学前向模型以实现高效控制。近年来，神经网络被广泛用于构建数据驱动的代理模型。本文评估了多种循环神经网络（RNN）变体在基准控制系统的动态学习及作为MPC代理模型的能力。结果表明，回声状态网络（ESNs）相比其他架构具有显著优势：计算复杂度更低，有效预测时间更长，且能将MPC目标函数成本降低超过30%。
原文摘要 · Abstract (English)
Model predictive control (MPC) is an industry standard control technique that iteratively solves an open-loop optimization problem to guide a system towards a desired state or trajectory. Consequently, an accurate forward model of system dynamics is critical for the efficacy of MPC and much recent work has been aimed at the use of neural networks to act as data-driven surrogate models to enable MPC. Perhaps the most common network architecture applied to this task is the recurrent neural network (RNN) due to its natural interpretation as a dynamical system. In this work, we assess the ability of RNN variants to both learn the dynamics of benchmark control systems and serve as surrogate models for MPC. We find that echo state networks (ESNs) have a variety of benefits over competing architectures, namely reductions in computational complexity, longer valid prediction times, and reductions in cost of the MPC objective function.
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