3 citations · 5 across the 12 of their papers we have counts for
10 papers · 1 filter
Asynchronous Model Predictive Control Under Model Mismatch: Stability and Performance Guarantees
Changrui Liu, Anil Alan, Shengling Shi +1
Certainty-equivalence model predictive control (CE-MPC) is widely used for its simplicity and efficiency, but theoretical guarantees under asynchronous feedback remain limited. Thi…
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…
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…
Learning State-Action Control Barrier Functions for Model-Free Control under Constraints
Kanghui He, Shengling Shi, Ton van den Boom +1
Ensuring constraint satisfaction for learning-based control is a critical challenge, especially in the model-free case. While safety filters address this challenge in the model-bas…
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…
State-action control barrier functions: Imposing safety on learning-based control with low online computational costs
Kanghui He, Shengling Shi, Ton van den Boom +1
Learning-based control with safety guarantees usually requires real-time safety certification and modifications of possibly unsafe learning-based policies. The control barrier func…
Regret Analysis of Learning-Based Linear Quadratic Gaussian Control with Additive Exploration
Archith Athrey, Othmane Mazhar, Meichen Guo +2
In this paper, we analyze the regret incurred by a computationally efficient exploration strategy, known as naive exploration, for controlling unknown partially observable systems…
A Behavioral Perspective on Models of Linear Dynamical Networks with Manifest Variables
Shengling Shi, Zhiyong Sun, Bart De Schutter
Networks of dynamical systems play an important role in various domains and have motivated many studies on the control and analysis of linear dynamical networks. For linear network…
Approximate Dynamic Programming for Constrained Linear Systems: A Piecewise Quadratic Approximation Approach
Kanghui He, Shengling Shi, Ton van den Boom +1
Approximate dynamic programming (ADP) faces challenges in dealing with constraints in control problems. Model predictive control (MPC) is, in comparison, well-known for its accommo…