7 papers
Data-informativity conditions for structured linear systems with implications for dynamic networks
Paul M. J. Van den Hof, Shengling Shi, Stefanie J. M. Fonken +3
When estimating a single subsystem (module) in a linear dynamic network with a prediction error method, a data-informativity condition needs to be satisfied for arriving at a consi…
Approximate Model Predictive Control for Microgrid Energy Management via Imitation Learning
Changrui Liu, Shengling Shi, Anil Alan +2
Efficient energy management is essential for reliable and sustainable microgrid operation amid increasing renewable integration. In this paper, an imitation learning-based framewor…
Robust Adaptive Discrete-Time Control Barrier Certificate
Changrui Liu, Anil Alan, Shengling Shi +1
This work develops a robust adaptive control strategy for discrete-time systems using Control Barrier Functions (CBFs) to ensure safety under parametric model uncertainty and distu…
Certainty-Equivalence Model Predictive Control: Stability, Performance, and Beyond
Changrui Liu, Shengling Shi, Bart De Schutter
Handling model mismatch is a common challenge in model predictive control (MPC). While robust MPC is effective, its conservatism often makes it less desirable. Certainty-equivalenc…
From learning to safety: A Direct Data-Driven Framework for Constrained Control
Kanghui He, Shengling Shi, Ton van den Boom +1
Ensuring safety in the sense of constraint satisfaction for learning-based control is a critical challenge, especially in the model-free case. While safety filters address this cha…
Predictive control barrier functions for piecewise affine systems with non-smooth constraints
Kanghui He, Anil Alan, Shengling Shi +2
Obtaining control barrier functions (CBFs) with large safe sets for complex nonlinear systems and constraints is a challenging task. Predictive CBFs address this issue by using an…