5 papers · 1 filter
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…
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…
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…
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…