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Machines
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Innovative Applications and Challenges of Intelligent Automation and Control in...
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Innovative Applications and Challenges of Intelligent Automation and Control in Smart Machines

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 5973

Editors

Dr. Karim Ahmadi

E-Mail Website
Guest Editor
School of Arch, Tech and Eng, University of Brighton, Brighton BN2 4AT, UK
Interests: flight dynamics and control; fault-tolerant control; adaptive control; aerial robotics; nonlinear control; implementing control algorithms
Dr. Seyed Yaser Nabavi Chashmi

E-Mail Website
Guest Editor
Aeronautical Engineering Department, Aerospace Faculty, Gebze Technical University, 01250 Kocaeli, Turkey
Interests: flight dynamics and control; design and optimization; dynamic modeling and simulation; space robotics

Special Issue Information

Dear Colleagues,

The increasing complexity of modern machines demands advanced control strategies to ensure optimal performance, robustness, and adaptability. This Special Issue focuses on nonlinear and adaptive control methods for smart machines, addressing challenges in stability, efficiency, and real-time adaptability.

This Special Issue invites contributions to nonlinear control methods, adaptive algorithms, fault-tolerant control, and the real-time implementation of nonlinear control. We welcome original research and review articles addressing theoretical advancements, practical applications, and emerging trends in controlling smart machines, including robotics, UAVs, industrial automation, and autonomous vehicles.

This Special Issue aims to advance control methodologies for next-generation smart machines, fostering innovation in intelligent automation.

Dr. Karim Ahmadi
Dr. Seyed Yaser Nabavi Chashmi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • adaptive control
  • intelligent automation
  • nonlinear control
  • smart machines

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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27 pages, 2037 KB  
Article
Development of a Robust-Adaptive Fault-Tolerant Control Algorithm Enhanced by Data-Driven Actuator Fault Estimation for a Multirotor UAV
by Karim Ahmadi Dastgerdi and Seyed-Yaser Nabavi-Chashmi
Machines 2026, 14(6), 639; https://doi.org/10.3390/machines14060639 - 1 Jun 2026
Viewed by 496
Abstract
Existing fault-tolerant control methods for multicopter UAVs often exhibit degraded performance under actuator faults and modeling uncertainties; therefore, this paper presents a robust-adaptive control algorithm for multicopter UAVs operating under actuator fault conditions. A data-driven approach based on an Artificial Neural Network (ANN) [...] Read more.
Existing fault-tolerant control methods for multicopter UAVs often exhibit degraded performance under actuator faults and modeling uncertainties; therefore, this paper presents a robust-adaptive control algorithm for multicopter UAVs operating under actuator fault conditions. A data-driven approach based on an Artificial Neural Network (ANN) is employed to estimate actuator fault using IMU measurements. The ANN is trained using data generated from a closed-loop system controlled by a robust-adaptive control, rather than an open-loop configuration, improving its ability to capture realistic fault dynamics. To mitigate limitations in training data coverage, an adaptive mechanism is incorporated to enhance robustness under varying operating conditions. In addition of inherent fault-tolerant control characteristics of the robust-adaptive control, the estimated fault signals are used for motor speed compensation to enhance the robustness of the algorithm. The inner-loop controller is designed based on a robust-adaptive algorithm, ensuring system stability and robustness against model uncertainties and fault estimation errors, even the actuator faults change both system dynamics and actuation. The outer-loop Proportional–Integral–Derivative (PID) controller is employed to achieve accurate trajectory tracking. For validation and benchmarking, a standalone robust-adaptive controller and a model-based recursive least squares (RLS) estimator are also implemented. Simulation results demonstrate that the proposed ANN-based approach provides accurate fault estimation and effective compensation, resulting in improved tracking performance under actuator fault conditions. Furthermore, the proposed framework contributes to the development of a fault-tolerant UAV systems by integrating robust-adaptive control, ANN-based fault estimation, and actuator compensation into a unified architecture, thereby enhancing reliability, robustness, and tracking performance in the presence of actuator faults and modeling uncertainties. Full article
(This article belongs to the Special Issue Innovative Applications and Challenges of Intelligent Automation and Control in Smart Machines)
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19 pages, 1040 KB  
Systematic Review
A Systematic Review on Risk Management and Enhancing Reliability in Autonomous Vehicles
by Ali Mahmood and Róbert Szabolcsi
Machines 2025, 13(8), 646; https://doi.org/10.3390/machines13080646 - 24 Jul 2025
Cited by 7 | Viewed by 4878
Abstract
Autonomous vehicles (AVs) hold the potential to revolutionize transportation by improving safety, operational efficiency, and environmental impact. However, ensuring reliability and safety in real-world conditions remains a major challenge. Based on an in-depth examination of 33 peer-reviewed studies (2015–2025), this systematic review organizes [...] Read more.
Autonomous vehicles (AVs) hold the potential to revolutionize transportation by improving safety, operational efficiency, and environmental impact. However, ensuring reliability and safety in real-world conditions remains a major challenge. Based on an in-depth examination of 33 peer-reviewed studies (2015–2025), this systematic review organizes advancements across five key domains: fault detection and diagnosis (FDD), collision avoidance and decision making, system reliability and resilience, validation and verification (V&V), and safety evaluation. It integrates both hardware- and software-level perspectives, with a focus on emerging techniques such as Bayesian behavior prediction, uncertainty-aware control, and set-based fault detection to enhance operational robustness. Despite these advances, this review identifies persistent challenges, including limited cross-layer fault modeling, lack of formal verification for learning-based components, and the scarcity of scenario-driven validation datasets. To address these gaps, this paper proposes future directions such as verifiable machine learning, unified fault propagation models, digital twin-based reliability frameworks, and cyber-physical threat modeling. This review offers a comprehensive reference for developing certifiable, context-aware, and fail-operational autonomous driving systems, contributing to the broader goal of ensuring safe and trustworthy AV deployment. Full article
(This article belongs to the Special Issue Innovative Applications and Challenges of Intelligent Automation and Control in Smart Machines)
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