1 citations · 1 across the 12 of their papers we have counts for
12 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
We propose a Model Predictive Control (MPC) formulation for nonlinear systems without terminal penalty or dedicated stabilizing terminal set, in which state constraints are enforce…
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…
Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)
Keyan Miao, Liqun Zhao, Han Wang +2
Designing controllers that achieve task objectives while ensuring safety is a key challenge in control systems. This work introduces Opt-ODENet, a Neural ODE framework with a diffe…
Constraint Horizon in Model Predictive Control
Allan Andre Do Nascimento, Han Wang, Antonis Papachristodoulou +1
In this work, we propose a Model Predictive Control (MPC) formulation incorporating two distinct horizons: a prediction horizon and a constraint horizon. This approach enables a de…
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…
Safe and Stable Filter Design Using a Relaxed Compatibitlity Control Barrier -- Lyapunov Condition
Han Wang, Kostas Margellos, Antonis Papachristodoulou
In this paper, we propose a quadratic programming-based filter for safe and stable controller design, via a Control Barrier Function (CBF) and a Control Lyapunov Function (CLF). Ou…
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…
Convex Co-Design of Control Barrier Function and Safe Feedback Controller Under Input Constraints
Han Wang, Kostas Margellos, Antonis Papachristodoulou +1
We study the problem of co-designing control barrier functions (CBF) and linear state feedback controllers for continuous-time linear systems. We achieve this by means of a single…
Model-Free Verification for Neural Network Controlled Systems
Han Wang, Zuxun Xiong, Liqun Zhao +1
Neural network controllers have shown potential in achieving superior performance in feedback control systems. Although a neural network can be trained efficiently using deep and r…