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math.OC2025

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…

math.OC2025

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…

math.OC2025

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…

math.OC2025

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…

math.OC2024

Data-Driven Stable Neural Feedback Loop Design

Zuxun Xiong, Han Wang, Liqun Zhao +1

This paper proposes a data-driven approach to design a feedforward Neural Network (NN) controller with a stability guarantee for plants with unknown dynamics. We first introduce da…