We propose a model-agnostic stochastic predictive control (MASMPC) algorithm for dynamical systems. The proposed approach first discovers \textit{interpretable} governing differential equations from data using a novel algorithm and blends it with a model predictive control algorithm. One salient feature of the proposed approach resides in the fact that it requires no input measurement (external excitation); the unknown excitation is instead treated as white noise, and a stochastic differential equation corresponding to the underlying system is identified. With the novel stochastic differential equation discovery framework, the proposed approach is able to generalize; this eliminates the repeated retraining phase -- a major bottleneck with other machine learning-based model agnostic control algorithms. Overall, the proposed MASMPC (a) is robust against measurement noise, (b) works with sparse measurements, (c) can tackle set-point changes, (d) works with multiple control variables, and (e) can incorporate dead time. We have obtained state-of-the-art results on several benchmark examples. Finally, we use the proposed approach for vibration mitigation of a 76-storey building under seismic loading.
翻译:我们提议对动态系统采用模型-不可知的预测控制(MAMPC)算法。拟议方法首先发现\ textit{解释性}对使用新算法的数据产生的差异方程式进行管理,并将它与模型预测控制算法混在一起。拟议方法的一个突出特征是,它不需要输入测量(外部引力);未知的刺激被作为白色噪音处理,并确定了一个与基本系统相对应的随机差异方程式。随着新颖的随机差异方程式发现框架,拟议方法能够概括化;这消除了重复的再培训阶段 -- -- 与其他基于机器学习的模型的不可知性控制算法相比,这是一个主要的瓶颈。总体而言,拟议的MASMPC(a) 能够抵御测量噪音,(b) 与稀疏测量一起工作,(c) 能够处理定点变化,(d) 与多个控制变量合作,以及(e) 能够纳入死时。我们在若干基准例子中取得了最新技术结果。最后,我们使用了拟议的76层地震楼的地震压压压制方法。