13 citations · 13 across the 4 of their papers we have counts for
5 papers
IRNN: Innovation-driven Recurrent Neural Network for Time-Series Data Modeling and Prediction
Yifan Zhou, Yibo Wang, Chao Shang
Many real-world datasets are time series that are sequentially collected and contain rich temporal information. Thus, a common interest in practice is to capture dynamics of time s…
On Min-Max Robust Data-Driven Predictive Control Considering Non-Unique Solutions to Behavioral Representation
Yibo Wang, Qingyuan Liu, Chao Shang
Direct data-driven control methods are known to be vulnerable to uncertainty in stochastic systems. In this paper, we propose a new robust data-driven predictive control (DDPC) fra…
Causality-Informed Data-Driven Predictive Control
Malika Sader, Yibo Wang, Dexian Huang +2
As a useful and efficient alternative to generic model-based control scheme, data-driven predictive control is subject to bias-variance trade-off and is known to not perform desira…
Data-Driven Predictive Control Using Closed-Loop Data: An Instrumental Variable Approach
Yibo Wang, Yiwen Qiu, Malika Sader +2
Current data-driven predictive control (DDPC) methods heavily rely on data collected in open-loop operation with elaborate design of inputs. However, due to safety or economic conc…
Data-Driven Output Prediction and Control of Stochastic Systems: An Innovation-Based Approach
Yibo Wang, Keyou You, Dexian Huang +1
Recent years have witnessed a booming interest in data-driven control of dynamical systems. However, the implicit data-driven output predictors are vulnerable to uncertainty such a…