Computation, Modeling and Algorithms for Control Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "C2: Dynamical Systems".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 699

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Guest Editor
Facultad de Ingeniería, Universidad Autónoma del Estado de México, Instituto Literario No. 100 Ote., Toluca 50130, Estado de México, Mexico
Interests: modeling; control theory; robotics; medical robotics; UAVs; mathematical modeling of dynamical systems

Special Issue Information

Dear Colleagues,

This Special Issue of Mathematics focuses on autonomous robotics systems that rely on advanced computational algorithms and control strategies to develop intelligent robots capable of identifying, planning, and executing tasks without human intervention. These systems can integrate sensing (LiDAR, cameras, IMU), real-time processing (edge computing, GPU/TPU), or decision-making (AI, SLAM, reinforcement learning) elements to operate in complex environments under the supervision of specialized control laws. Real-time computation constraints, sensor fusion, uncertainty handling, energy efficiency, and safety in human–robot interactions are some of the challenges that the articles in this Special Issue will be addressing.

We look forward to hearing from authors around the world, and welcome submissions on topics including, but not limited to, the following: autonomous vehicles and drones, search and rescue robots, medical robotics, soft robotics, cooperative robots, space exploration systems, and industrial automation.

Dr. J. C. Avila-Vilchis
Guest Editor

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Keywords

  • perception and sensor fusion
  • path planning and navigation
  • robot kinematics and dynamics
  • control systems
  • edge artificial intelligence

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Published Papers (1 paper)

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Research

29 pages, 3798 KB  
Article
Hybrid Adaptive MPC with Edge AI for 6-DoF Industrial Robotic Manipulators
by Claudio Urrea
Mathematics 2025, 13(19), 3066; https://doi.org/10.3390/math13193066 - 24 Sep 2025
Viewed by 610
Abstract
Autonomous robotic manipulators in industrial environments face significant challenges, including time-varying payloads, multi-source disturbances, and real-time computational constraints. Traditional model predictive control frameworks degrade by over 40% under model uncertainties, while conventional adaptive techniques exhibit convergence times incompatible with industrial cycles. This work [...] Read more.
Autonomous robotic manipulators in industrial environments face significant challenges, including time-varying payloads, multi-source disturbances, and real-time computational constraints. Traditional model predictive control frameworks degrade by over 40% under model uncertainties, while conventional adaptive techniques exhibit convergence times incompatible with industrial cycles. This work presents a hybrid adaptive model predictive control framework integrating edge artificial intelligence with dual-stage parameter estimation for 6-DoF industrial manipulators. The approach combines recursive least squares with a resource-optimized neural network (three layers, 32 neurons, <500 KB memory) designed for industrial edge deployment. The system employs innovation-based adaptive forgetting factors, providing exponential convergence with mathematically proven Lyapunov-based stability guarantees. Simulation validation using the Fanuc CR-7iA/L manipulator demonstrates superior performance across demanding scenarios, including precision laser cutting and obstacle avoidance. Results show 52% trajectory tracking RMSE reduction (0.022 m to 0.012 m) under 20% payload variations compared to standard MPC, while achieving sub-5 ms edge inference latency with 99.2% reliability. The hybrid estimator achieves 65% faster parameter convergence than classical RLS, with 18% energy efficiency improvement. Statistical significance is confirmed through ANOVA (F = 24.7, p < 0.001) with large effect sizes (Cohen’s d > 1.2). This performance surpasses recent adaptive control methods while maintaining proven stability guarantees. Hardware validation under realistic industrial conditions remains necessary to confirm practical applicability. Full article
(This article belongs to the Special Issue Computation, Modeling and Algorithms for Control Systems)
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