8 papers
On the Design of Rational Polynomial State Feedback Controllers
Matthew Newton, Zuxun Xiong, Han Wang +1
One of the desirable objectives in feedback control design is to formulate and solve the design problem as an optimisation problem that is convex, so that an optimal solution can b…
Model Predictive Control with Multiple Constraint Horizons
Allan Andre do Nascimento, Han Wang, Antonis Papachristodoulou +1
In this work we propose a Model Predictive Control (MPC) formulation that splits constraints in two different types. Motivated by safety considerations, the first type of constrain…
Predictive Control Barrier Functions: Bridging model predictive control and control barrier functions
Jingyi Huang, Han Wang, Kostas Margellos +1
In this paper, we establish a connection between model predictive control (MPC) techniques and Control Barrier Functions (CBFs). Recognizing the similarity between CBFs and Control…
Learning Neural Controllers with Optimality and Stability Guarantees Using Input-Output Dissipativity
Han Wang, Keyan Miao, Diego Madeira +1
Deep learning methods have demonstrated significant potential for addressing complex nonlinear control problems. For real-world safety-critical tasks, however, it is crucial to pro…
Synthesis of safety certificates for discrete-time uncertain systems via convex optimization
Marta Fochesato, Han Wang, Antonis Papachristodoulou +1
We study the problem of co-designing control barrier functions and linear state feedback controllers for discrete-time linear systems affected by additive disturbances. For disturb…
Data-Enabled Predictive Control for Nonlinear Systems Based on a Koopman Bilinear Realization
Zuxun Xiong, Zhenyi Yuan, Keyan Miao +3
This paper extends the Willems' Fundamental Lemma to nonlinear control-affine systems using the Koopman bilinear realization. This enables us to bypass the Extended Dynamic Mode De…