Author

B. de Schutter

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Preprint Oct 2026

Distributed Motion Planning for Multi-Robot Systems under Topological Constraints

Efficient and distributed coordination of mobile robots is one of the main challenges in multi-robot systems. Topological constraints, often expressed as topological braids, are a popular tool to encode complex coordination patterns between multiple mobile robots, as they offer a compact and abstract representation of...

Gianpietro Battocletti, Dimitris Boskos, D. Dimarogonas et al. · 0 citations

Preprint Aug 2026

On the Value Function of Infinite-Horizon Optimal Control of Piecewise Affine Systems

In this paper, we study the structure of the value function in constrained infinite-time optimal control (CITOC) problems of piecewise affine (PWA) systems, with $\ell_1$ or $\ell_\infty$ stage cost. Existing works, such as [1], establish that the resulting value function is PWA in the state. However, existing results...

Francesco Cordiano, Kang-Hui He, B. de Schutter · 0 citations

Preprint Sep 2026

Asynchronous Model Predictive Control Under Model Mismatch: Stability and Performance Guarantees

Stability and performance guarantees for asynchronous CE-MPC of input-constrained nonlinear systems of input-constrained nonlinear systems are established, clarifying tradeoffs between feedback frequency, model accuracy, and horizon length, guiding asynchronous MPC design using approximate or learned models.

Chang-Rui Liu, Anil Alan, Sheng-Ling Shi et al. · 0 citations

#machine learning Preprint Sep 2026

Composite-Gradient Learning for Shared Control Authority Between Deep Reinforcement Learning and Model Predictive Control

A novel composite-gradient learning (CGL) method is proposed that integrates the MPC controller into the learning process by representing the DRL and MPC control inputs as a joint action and accounting for their interaction when updating the DRL agent during training.

Giray Önür, A. Dabiri, B. de Schutter · 0 citations

Preprint Aug 2026

Learning-Based Stochastic Optimal Control with Infinite-Horizon Probabilistic Constraints

A dual-ascent algorithm is proposed to solve the original problem as a constrained Markov decision process and it is proved that this formulation enjoys strong duality, thereby enabling it to reformulate the problem as an equivalent unconstrained one in the Lagrange dual framework.

Francesco Cordiano, Kang-Hui He, B. de Schutter · 0 citations

Preprint Aug 2026

Sharing the Control Authority Between Deep Reinforcement Learning and Model Predictive Control: Application to Multi-Class Transportation Networks

A novel DRL-MPC framework for multi-class transportation networks that divides control authority between DRL and MPC is proposed, combining DRL's fast online computation and model independence with MPC's built-in optimization and constraint-handling capabilities.

G. Onur, A. Dabiri, B. de Schutter · 1 citation