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Intelligent Automatic Control Systems
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Intelligent Automatic Control Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1386

Special Issue Editors

Prof. Dr. Yaojung Shiao

E-Mail Website
Guest Editor
Department of Vehicle Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: magneto-rheological devices and applications
Dr. Tan Linh Huynh

E-Mail Website
Guest Editor Assistant
Department of Vehicle Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: deep learning image recognition; MR damper

Special Issue Information

Dear Colleagues,

Automatic control systems play a foundational role in modern engineering by ensuring stability, efficiency, and reliability across dynamic and complex processes. Intelligent automatic control systems are becoming a core element of forward-looking automation by enabling the integration of new sensing technologies, sensing data processing and fusion, image-based information, and real-time decision-making. Recent developments demonstrate a strong convergence of intelligent control theory with advanced sensing data analytics, sensor fusion techniques, sensor-based state estimation, and image-derived information. This integration enhances system stability, fault detection, predictive capability, operational efficiency, energy utilization, cost reduction, environmental sustainability, and even human health protection.

This Special Issue invites original research that explores the intersection between emerging sensing technologies and intelligent automatic control systems. Submissions may include innovative sensing methods, algorithmic developments, experimental studies, and real-world applications. Topics of interest include, but are not limited to, the following:

  • Intelligent control and automation;
  • Intelligent Autonomous Systems;
  • Machine learning and deep learning integration with sensor-driven control;
  • Intelligent sensor-based control strategies;
  • Smart sensors;
  • Multi-sensor fusion, sensor integration, and perception algorithms;
  • Intelligent fault diagnosis;
  • Optimization-based intelligent control strategies;
  • Sensor-oriented applications;
  • Industrial applications of AI-driven automation and intelligent control.

Prof. Dr. Yaojung Shiao
Guest Editor

Dr. Tan Linh Huynh
Guest Editor Assistant

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • intelligent control
  • intelligent automation
  • autonomous systems
  • smart sensors
  • sensor fusion
  • intelligent fault diagnosis
  • AI-driven industrial automation

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Published Papers (2 papers)

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Research

25 pages, 5684 KB  
Article
Wavelet-Based Health Monitoring Approach for Train Door Actuation Using Motor Current Analysis
by Yaojung Shiao, Premkumar Gadde and Manichandra Bollepelly
Sensors 2026, 26(9), 2898; https://doi.org/10.3390/s26092898 - 6 May 2026
Viewed by 490
Abstract
Train door actuation systems are critical safety components in railway vehicles, where early fault detection is essential for safe operation and reduced service disruptions. Conventional monitoring approaches often rely on additional sensors such as infrared detectors or vision systems, which increase system complexity [...] Read more.
Train door actuation systems are critical safety components in railway vehicles, where early fault detection is essential for safe operation and reduced service disruptions. Conventional monitoring approaches often rely on additional sensors such as infrared detectors or vision systems, which increase system complexity and cost. To overcome these limitations, this study proposes a wavelet-based health monitoring structure for detecting electrical and mechanical faults using motor current signal analysis. A dynamic model of the train door actuation mechanism, including a DC motor, gearbox, and lead screw, was developed in MATLAB/Simulink to simulate conditions such as armature electrical faults, brush wear, increased friction, and lead screw misalignment. Motor current signals were analyzed using the Discrete Wavelet Transform with a Daubechies (db10) mother wavelet to extract diagnostic features based on the L1-norms of wavelet coefficients at levels W8 and W9 along with the motor starting current peak. Experimental validation using a LabVIEW-based test platform demonstrated fault detection accuracy above 96% with a response time below 0.3 s, confirming the effectiveness of the proposed approach for predictive maintenance of railway door systems. Full article
(This article belongs to the Special Issue Intelligent Automatic Control Systems)
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19 pages, 1755 KB  
Article
Secondary Cooling Water System Control Method Based on Deep Reinforcement Learning
by Jin Xu, Yu Cheng, Cheng Shen and Qingxin Zhang
Sensors 2026, 26(9), 2783; https://doi.org/10.3390/s26092783 - 29 Apr 2026
Viewed by 623
Abstract
The secondary cooling water system is difficult to control because of loop coupling, thermal inertia, and strict actuator constraints. In addition, when conventional proximal policy optimization (PPO) uses Gaussian action sampling with clipping, the mismatch between sampled and executed actions may degrade learning [...] Read more.
The secondary cooling water system is difficult to control because of loop coupling, thermal inertia, and strict actuator constraints. In addition, when conventional proximal policy optimization (PPO) uses Gaussian action sampling with clipping, the mismatch between sampled and executed actions may degrade learning and control smoothness near actuator limits. To address these issues, this paper develops a Beta-policy and PID-inspired augmented-state proximal policy optimization framework, termed BPAS-PPO, for the secondary cooling water system. The framework augments the state with proportional, integral, and derivative error features, adopts a Beta-distribution policy for bounded continuous-action generation, and uses a piecewise dense reward for the dual-loop tracking task. Simulation studies on an identified linear two-input two-output (TITO) model within the selected operating region show that the complete PID-augmented state yields the most effective training representation among the tested alternatives. Compared with PID, Fuzzy-PID, and Gauss-PPO, BPAS-PPO shows lower overshoot, shorter settling time, better setpoint tracking and disturbance rejection, and smoother control actions near actuator limits. The proposed framework is effective for the studied system within the selected operating region, while its performance beyond this region requires further validation. Full article
(This article belongs to the Special Issue Intelligent Automatic Control Systems)
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