Model Predictive Supervisory Control for Hierarchical and Distributed UAS Traffic Management

Matheus P. Loures; Guilherme V. Raffo; Patrícia N. Pena

eess.SYAug 18, 2026

Model Predictive Supervisory Control for Hierarchical and Distributed UAS Traffic Management

Authors: Matheus P. Loures, Guilherme V. Raffo, Patrícia N. Pena

Organizations: Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil · Department of Electronics Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

Abstract

This work proposes a hierarchical Model Predictive Supervisory Control (MPSC) framework for multi-agent systems with shared resources. MPSC integrates receding-horizon cost-optimal control with Supervisory control theory (SCT) based supervision that enforces safety, nonblockingness, and resource exclusivity. Scalability arises from hierarchical and scalable supervisor and automaton templates, enabling distributed execution without monolithic synthesis. Using this framework, this work develops an urban Unmanned aircraft system Traffic Management (UTM) model. The model supports pickup-and-delivery missions under time-varying demand efficiently.

Figures & tables

Panel of Figure 1 — Figure 1: Example 1 : automaton $G$ and the matrices $A$ , $B$ , and $C$ .

Figure 2: Hierarchical and scalable supervisory architecture.

Figure 3: Single layer representation of the delivery scenario.

Panel of Figure 4 — Figure 4: UAV subplants

Panel of Figure 5 — Figure 5: UAV specifications

Panel of Figure 6 — Figure 6: Example 4 : (a) Plant with vertex blocking related events; (b) Block vertex; (c) Mutual exclusion.

Panel of Figure 7 — Figure 7: Urban airspace topology for scenarios R1 and R2.

Metric	Hierarchical	Centralized exact
Supervisor states	570	444,816
Supervisor transitions	1,676	3,828,328
Centralized synthesis time (s)	–	117.70

Table 1: DES metrics for R1.

Metric	R1	R2
UAVs / nodes / corridors	2 / 9 / 30	4 / 19 / 68
Completed / assigned missions	4 / 4	5 / 6
Mean mission time (s)	69.11	44.42
Maximum mission time (s)	84.42	53.25
Throughput (missions/min)	1.29	2.46
Accepted UTM grants	32	205

Table 2: Experimental metrics.

Multi-Agent Control

Multi-agent control coordinates interacting autonomous agents to achieve shared goals while respecting safety, communication, and resource constraints.

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Temporal Cascading of Planning and Control for Quadrotor MPC

Rudolf Reiter, Chao Qin, Leonard Bauersfeld, Davide Scaramuzza

Robotics and Perception Group, Department of Informatics, University of Zurich, Andreasstrasse 15, 8050 Zurich, Switzerland · Institute for Aerospace Studies, University of Toronto, 4925 Dufferin Street, Toronto, ON M3H 5T6, Canada

Robot Trajectory Tracking Aerial Robotics Model Predictive Control UAV Trajectory Optimization Quadrotor Control Safe Motion Planning

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Many aerial tasks involving quadrotors demand both instant reactivity and long-horizon planning for obstacle avoidance, energy efficiency, or trajectory tracking. High-fidelity models enable accurate control but are too slow for long horizons. Low-fidelity planners scale but cannot directly control the system, necessitating cascaded architectures. Prevailing hierarchical approaches plan with a simplified model and use a high-fidelity controller for tracking, yet this decomposition is inherently suboptimal. The controller is limited by the coarse plan, and conventional MPC alternatives shorten the horizon to stay real-time feasible. We present UNIQUE, an MPC architecture that replaces this hierarchical stacking with temporal cascading. The planning problem is formulated as the second-tail horizon of a single multi-phase MPC, rather than being solved separately. We align costs across horizons, derive feasibility constraints for the point-mass planning model, and introduce transition constraints that convert high-fidelity states into meaningful low-fidelity states. Parallel point-mass and mixed-integer solvers address nonconvexities while incorporating progressive 3D obstacle smoothing over the planning horizon. In simulations and real flights, under equal computational budgets, UNIQUE improves closed-loop tracking by up to 75% compared with standard MPC and hierarchical baselines. Ablations and Pareto analyses confirm performance gains across variations in horizon, constraint approximations, and smoothing schedules.

Abstract

Multi-Agent Control

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