Design and Real Time Implementation of Intelligent Control Schemes for Actuators

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "A:Physics".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 515

Special Issue Editor


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Guest Editor
Department of System Engineering and Automation, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country, UPV/EHU, Leioa, Spain
Interests: intelligent control; advanced control; robust control; adaptive control; wind turbine systems; PV systems; wireless control schemes; wireless sensor networks; smart sensors and actuators
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Special Issue Information

Dear Colleagues,

An actuator is a device that moves or controls certain mechanisms by turning a control signal into mechanical action, such as an electric motor. Actuators may be based on hydraulic, pneumatic, electric, thermal, or mechanical means and powered by electric current, hydraulic fluid, or pneumatic pressure. However, control algorithms are being increasingly executed over microcontrollers or digital signal processors to drive these actuators. Therefore, a key element in increasing the reliability and improving the performance of these actuators is the control system that should be implemented through software. The limitations of traditional techniques to cope with real control problems have motivated the invention of new schemes to improve control performance. Therefore, due to the nonlinear dynamics and uncertainties usually present in actuators, the reliability and performance of these systems can be enhanced by employing advanced control schemes and/or using artificial intelligence. This Special Issue on actuators is aimed at addressing the challenges in control design and implementation in order to increase the reliability of these actuator systems. Original submissions focusing on novel control techniques and the practical implementation of these new control schemes, which would be useful for increasing our knowledge of the promising applications of actuator systems, based on one or more of the following topics, are welcome in this Special Issue. The Issue will include (but is not limited to) the following: Adaptive control schemes; artificial intelligence applied to control systems; fuzzy-logic-based control schemes; neural-network-based control schemes; observer-based control schemes; optimization-based control design.

Prof. Dr. Oscar Barambones
Guest Editor

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Keywords

  • adaptive control
  • robust control
  • artificial intelligence
  • fuzzy logic
  • neural networks
  • real time control

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Published Papers (2 papers)

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Research

14 pages, 1040 KiB  
Article
Sensorless Impedance Control of Micro Finger Using Coprime Factorization
by Yuuki Morohoshi and Mingcong Deng
Micromachines 2025, 16(5), 510; https://doi.org/10.3390/mi16050510 - 27 Apr 2025
Viewed by 100
Abstract
Soft robots are attracting attention as next-generation robots because they enable flexible movement. The micro finger is a soft robot that can bend and is small and can grasp objects of various shapes, so it is expected to be applied to surgical robots. [...] Read more.
Soft robots are attracting attention as next-generation robots because they enable flexible movement. The micro finger is a soft robot that can bend and is small and can grasp objects of various shapes, so it is expected to be applied to surgical robots. However, because it is small, sensors cannot be attached, making it difficult to measure force. This paper proposes impedance control of the tip of a micro finger by estimating the tip force with an observer. The control system is designed using coprime factorization and Youla–Kucera parameterization by operator theory. The effectiveness of the proposed method is confirmed through experiments. Full article
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16 pages, 3089 KiB  
Article
Backstepping Controller for Nanopositioning in Piezoelectric Actuators with ANN Hysteresis Compensation
by Asier del Rio, Oscar Barambones, Eneko Artetxe, Jokin Uralde and Isidro Calvo
Micromachines 2025, 16(4), 469; https://doi.org/10.3390/mi16040469 - 15 Apr 2025
Viewed by 232
Abstract
Piezoelectric actuators (PEAs) are widely used in high-precision applications but suffer from nonlinear hysteresis effects that degrade positioning accuracy. To address this challenge, this study presents a backstepping controller with an Artificial Neural Network (ANN)-based feedforward compensation scheme to enhance trajectory tracking performance. [...] Read more.
Piezoelectric actuators (PEAs) are widely used in high-precision applications but suffer from nonlinear hysteresis effects that degrade positioning accuracy. To address this challenge, this study presents a backstepping controller with an Artificial Neural Network (ANN)-based feedforward compensation scheme to enhance trajectory tracking performance. The ANN compensates for the hysteresis effects, while the backstepping strategy ensures robust reference tracking. The proposed controller is validated through real-time experiments using a piezoelectric actuator system. Comparative analysis with a conventional PID controller demonstrates the superiority of the backstepping approach, achieving significantly lower tracking errors across different reference signals and frequencies. Error metrics have been employed to confirm the improved accuracy and robustness of the proposed method. These findings highlight the effectiveness of the proposed ANN-enhanced backstepping control in overcoming hysteresis-related challenges in precision positioning applications. Full article
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