Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

Artificial neural networks are black-box models that can be used to model nonlinear
dynamical systems. This article presents a synthesis method for full dynamic state feedback …

The progress on intelligent edge and intelligent cloud has made manufacturing company
much more autonomy. The edge device and the public cloud provider become a new hybrid …

The control of manufacturing processes must satisfy high quality and efficiency requirements
while meeting safety requirements. A broad spectrum of monitoring and control strategies …

Voluminous process data are available with the paradigm shift toward smart manufacturing.
However, most historical data are observational, containing noncausal correlations due to …

Providing stability guarantees for controllers that use neural networks can be challenging.
Robust control theoretic tools are used to derive a framework for providing nominal stability …

Trained deep reinforcement learning (DRL) based controllers can effectively control
dynamic systems where classical controllers can be ineffective and difficult to tune …

Abstract Model predictive control requires the real-time solution of an optimal control
problem, which can be challenging on computationally limited systems. Approximating the …

This paper presents a deep neural network (DNN) based method to estimate approximate
Lyapunov functions and their orbital derivatives, which are key to the stability of the system …

化學工廠平時運作, 最重要除了操作安全之外, 產品品質及成本將會影響客戶採購的意願.
以往確認品質需要取樣至實驗室, 使用價格昂貴的分析儀器分析, 或安裝線上分析儀器 …