Optimization and Control for Unmanned Systems and Intelligent Logistics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2288

Special Issue Editors


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Guest Editor
Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai 200030, China
Interests: dynamics; optimization and control theory; intelligent logistics systems

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Guest Editor
School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Interests: dynamics; hydrodynamics; control system; nonlinear system; control theory

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to the special issue titled "Optimization and Control for Unmanned Systems and Intelligent Logistics", which highlights the critical role of theoretical innovation and technological integration in advancing the autonomy and reliability of unmanned systems and its applications in intelligent logistics. This platform aims to showcase cutting-edge research that bridges fundamental modeling with practical optimization and control methodologies, addressing the most pressing challenges in modern unmanned systems engineering and logistics applications.

This Special Issue aims to foster interdisciplinary dialogue by focusing on the deep convergence of optimization theory, control algorithms, sensing-actuating, logistics system modeling technologies. It seeks to explore how mathematical frameworks—ranging from differential geometry to optimization theory—can underpin the design of robust optimization and control strategies for unmanned systems operating in complex, uncertain logistics environments. By emphasizing both model-driven precision and data-driven adaptability, the issue targets breakthroughs that enhance the safety, efficiency, and scalability of unmanned systems across diverse applications.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Dynamics modeling for nonlinear, underactuated, or multi-agent unmanned systems (e.g., drones, autonomous vehicles, robotic swarms)
  • Development of stochastic optimization, robust optimization, adaptive control, and model predictive control strategies for objective optimization, trajectory tracking, and collaborative task execution
  • Integration of multi-modal sensing (inertial navigation, computer vision, lidar) with state estimation algorithms (Kalman filtering, graph optimization) for real-time environmental perception
  • Optimization techniques for vehicle routing, path planning, energy management, and formation control in single or multi-robot systems, including linear/nonlinear programming, stochastic optimization, and swarm intelligence algorithms
  • Synergies between classical control theory and modern machine learning (deep reinforcement learning, adaptive neural networks) for autonomous decision-making in unstructured environments
  • Robustness analysis and fault-tolerant control strategies to ensure reliability under hardware failures or environmental disturbances

I look forward to receiving your contributions. Whether theoretical investigations, technical innovations, or application-oriented studies, your work will help shape the future of unmanned systems by advancing their capabilities in autonomy, adaptability, and collective intelligence. Submissions should adhere to the journal’s guidelines and highlight the novelty and impact of your research in addressing the dynamics and control challenges of unmanned systems.

Prof. Dr. Dali Zhang
Dr. Dongfang Li
Guest Editors

Manuscript Submission Information

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Keywords

  • unmanned systems
  • optimization and control theory
  • intelligent logistics systems
  • optimization techniques
  • autonomous systems

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Published Papers (3 papers)

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Research

21 pages, 5419 KB  
Article
Residual Low-Order Phase-Error Estimation and Compensation for Post-Autofocus UAV K-Band Multi-Baseline InSAR
by Yaxuan Li, Bin Wen and Xiao Zhou
Mathematics 2026, 14(5), 772; https://doi.org/10.3390/math14050772 - 25 Feb 2026
Viewed by 502
Abstract
This study examines residual low-order (linear and constant) phase errors in interferometric synthetic aperture radar (InSAR) when compact, high-frequency radar sensors are mounted on commercial uncrewed aerial vehicles (UAVs). Although higher carrier frequencies and shorter standoff ranges enable fine-resolution interferometry, the same characteristics—together [...] Read more.
This study examines residual low-order (linear and constant) phase errors in interferometric synthetic aperture radar (InSAR) when compact, high-frequency radar sensors are mounted on commercial uncrewed aerial vehicles (UAVs). Although higher carrier frequencies and shorter standoff ranges enable fine-resolution interferometry, the same characteristics—together with UAV platform instability—make the system highly vulnerable to motion-induced phase errors, which can significantly degrade or even invalidate DEM reconstruction. This paper first quantifies the admissible motion-error bounds for reliable multi-baseline phase-gradient estimation, and then introduces a post-autofocus correction scheme that estimates the residual linear term from the interferometric fringe frequency and refines it via an FFT-based correlation objective, while the constant term is calibrated using ground control points (GCPs). The method is validated through simulations of a 24 GHz UAV demonstrator. To the best of our knowledge, this work provides the first post-autofocus demonstration of linear-and-constant residual-error mitigation for UAV-based high-frequency multi-baseline InSAR. In the considered K-band setting, the proposed approach reduces the DEM error from 42 m to 0.2 m (≈98% improvement). Full article
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21 pages, 1036 KB  
Article
An Attention-Based Learning Approach for Joint Optimization of Storage Selection and Order Picking Paths in Mobile Shelving Systems
by Jiawei Zhang, Li Wang, Pinyan Lai, Ye Shao and Sixiang Zhao
Mathematics 2026, 14(3), 559; https://doi.org/10.3390/math14030559 - 4 Feb 2026
Viewed by 659
Abstract
This research introduces an advanced attention-driven model designed to optimize mobile shelf warehouse order-picking. Our model incorporates an enhanced masking mechanism and context-aware decoder, streamlining the order-picking process. In essence, our model presents an attention model based heuristic solution to the long-standing problem [...] Read more.
This research introduces an advanced attention-driven model designed to optimize mobile shelf warehouse order-picking. Our model incorporates an enhanced masking mechanism and context-aware decoder, streamlining the order-picking process. In essence, our model presents an attention model based heuristic solution to the long-standing problem of order-picking optimization, leveraging the latest in attention-based deep learning techniques. The attention model is combined with Apriori and the Adaptive Large Neighborhood Search (ALNS) algorithm to solve the bilevel combinatorial optimization model for mobile shelves. Compared to existing methods, our innovative model shows superior performance, offering significant potential in warehousing solutions. Full article
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14 pages, 1539 KB  
Article
Optimal Control of Orbit Rendezvous with Low-Thrust on Near-Circular Orbits Using Pontryagin’s Maximum Principle
by Xiao Zhou, Hongbin Deng, Yaxuan Li and Yigao Gao
Mathematics 2026, 14(2), 294; https://doi.org/10.3390/math14020294 - 13 Jan 2026
Viewed by 711
Abstract
This paper investigates the optimal control problem of orbital rendezvous for spacecraft in near-circular orbits with a low-thrust propulsion system. Two optimality criteria are considered: time-optimal and motor-time-optimal control. A linearized mathematical model of relative motion between the active and passive spacecraft is [...] Read more.
This paper investigates the optimal control problem of orbital rendezvous for spacecraft in near-circular orbits with a low-thrust propulsion system. Two optimality criteria are considered: time-optimal and motor-time-optimal control. A linearized mathematical model of relative motion between the active and passive spacecraft is employed, which is formulated in dimensionless variables that characterize secular, periodic, and lateral motion components of the relative motion. By applying Pontryagin’s Maximum Principle, the equations governing the optimal relative motion of the spacecraft are derived. To address the discontinuities associated with the bang–bang switching function inherent in the motor-time-optimal problem, and the lack of a suitable initial guess, a homotopy method is adopted, in which the solution to the rendezvous time-optimal problem is used as an initial guess and is gradually deformed into the motor-time-optimal control. Considering the errors introduced by the linearization of the relative motion model, the obtained control law is validated via numerical simulations based on the original nonlinear dynamics of the system. Simulation results demonstrate that the proposed trajectory optimization methodology achieves high success rates and rapid convergence, providing valuable theoretical support and practical guidance for mission scenarios with similar trajectory design requirements. Full article
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