Mechatronics Technology
ISSN: 2959-376X (Print)
ISSN: 2959-3778 (Online)
CODEN: MTEEEV
About This Journal
Mechatronics in industry 4.0 and 5.0: advancing synergy, innovations, sustainability, and challenges
Received
19 Jun 2024
Accepted
31 Jan 2025
Published
21 Mar 2025
As an interdisciplinary science and engineering paradigm, Mechatronics synergizes diverse engineering and non-engineering disciplines to inspire new ideas, accelerate innovation, and create integrated solutions for complex technological challenges in modern industry and daily life. This paper highlights the critical role of mechatronics in both Industry 4.0 and Industry 5.0, focusing on smart manufacturing systems, sustainable development, and human-machine collaboration. It highlights and discusses significant advancements, including autonomous systems, renewable energy integration, and bioinspired designs, demonstrating notable improvements in efficiency, adaptability, reliability, and interdisciplinary collaboration, reflecting the transformative potential of mechatronics. Practical applications such as AI-driven collaborative robots (Cobots), predictive maintenance systems, and cyber-physical architectures in manufacturing environments illustrate the real-world impact of these innovations. These innovations support more sustainable and efficient operations while reshaping industrial practices. The paper critically evaluates ongoing challenges, including the scalability of autonomous systems, limitations in human-centric technology development, and the integration of sustainability metrics into mechatronic systems. It proposes strategic pathways to address these issues, emphasizing enhanced real-time intelligent decision-making, modular system design, and advanced AI applications. Future opportunities in sustainable technological development are highlighted, driven by breakthroughs in AI, robotics, and smart materials. Guided by the mechatronics philosophy of integrated thinking and planning, this paper outlines a roadmap for future innovations. It also highlights the emergence of Eco-mechatronics, an evolution of mechatronics that prioritizes environmental sustainability alongside technological advancement. By integrating Eco-conscious practices with cutting-edge engineering, Eco-Mechatronics presents a transformative approach to addressing global challenges, shaping a more efficient, eco-friendly industrial landscape, fostering sustainable industrial ecosystems, and significantly enhancing human quality of life.
Underflow concentration soft sensing for cone thickener system based on a direct data-driven quantile regression forecasting
Received
22 Nov 2024
Accepted
25 Jan 2025
Published
18 Mar 2025
Paste-filling is a crucial process in the mining industry. Traditional sensor devices often overlook model errors and struggle to measure the certainty of key quality variables. This study addresses these challenges by proposing a novel efficient data-driven forecasting framework to predict the concentration of deep cone thickeners (DDQRF). Conventional methods rely on normal regression models, minimizing residual mean square error to estimate underflow concentration, resulting in inaccuracies due to residual error accumulation in recursive strategies. Specifically, complex high-quality feature representation is essential for accurate prediction models, particularly for multiple horizon predictions necessary for hierarchical optimal control. Our framework introduces direct data-driven regression prediction, leveraging temporal machine learning models to extract features effectively. Unlike probabilistic Bayesian models, our approach offers efficient implementation and deployment, utilizing prediction intervals to quantify forecast uncertainty. In addition, the proposed model directly predicts multiple horizons, contrasting with traditional recursive single-point forecasting, offering enhanced training and memory efficiency. These characteristics are validated through industrial experiments on a deep cone thickener, comprehensively comparing performance with state-of-the-art counterparts.
Adaptive learning-based energy management for HEVs using soft actor-critic DRL algorithm
Received
13 Nov 2024
Accepted
17 Dec 2024
Published
31 Dec 2024
In this work, we design an energy management strategy (EMS) for hybrid electric vehicles (HEVs) using a deep reinforcement learning (DRL) algorithm. Specifically, this paper introduces a soft actor-critic (SAC)-based EMS, tailored for devising optimal energy distribution for HEVs. The proposed SAC-based approach is useful for addressing inherent drawbacks that exist in many DRL methods such as slower convergence rate, discretization error, as well as suboptimal solutions. The designed SAC algorithm presents a self-adaptive efficiency in executing continuous decision-making policies through the balance of exploration and exploitation using an entropy-based action selection method and an entropy-added reward function. Extensive experiments are carried out to demonstrate the merits of the adaptive SAC algorithm over the widely adopted Q-learning (QL), deep-Q-network (DQN), and deep deterministic policy gradient (DDPG) approaches on fuel economy and battery charge sustainability. An unknown driving cycle is also employed to show the adaptability feature of the proposed scheme, revealing fuel savings of 6.26%, 3.01%, and 2.03% over the QL-based, DQN-based, and DDPG-based methods, respectively.
Horizontal vibration and control methods in high-speed elevator car systems: a review
Received
25 Sep 2024
Accepted
05 Dec 2024
Published
25 Dec 2024
Horizontal vibration in high-speed elevator car systems has been a serious problem affecting the riding feeling, and it may threaten the safety and stability of the elevator system. This review aims to provide a comprehensive overview of the causes of horizontal vibration, focusing on factors such as the car and guide system, aerodynamic characteristics, and the performance during starting and stopping processes. Subsequently, various models of high-speed elevator car systems were discussed in detail, taking into account structural excitations and aerodynamic effects. Finally, a thorough analysis of vibration suppression methods, addressing both passive and active controls, was presented. Compared to passive control, active damping control technology has been shown to offer a more flexible and efficient approach to vibration suppression by leveraging real-time feedback and dynamic adjustments of control forces. To enhance the suppression of horizontal vibration, further research into intelligent control strategies with self-learning capabilities, as well as the integration of intelligent materials, appears promising.
Recent advances in hand movement rehabilitation system and related strategies
Received
31 May 2023
Accepted
20 Jul 2023
Published
11 Dec 2023
Hand movement disorders caused by neurological diseases like brachial plexus injuries significantly impact daily activities of patients. Compared with the upper-limb rehabilitation that is focused on the large movements of joints, the rehabilitation of the hand movements that are dexterous remains challenging due to its exceptional flexibility. This article aims to reviewing the latest research on the system and related strategies for hand movement rehabilitation. Firstly, the development on the cutting-edge sensing technologies, actuator-driven rehabilitation equipment and hand movement pattern recognition algorithms, all contributing to the design of the hand movement rehabilitation system, are introduced. Secondly, the various rehabilitation strategies, including the active rehabilitation, passive rehabilitation, and guided rehabilitation that are tailored for patients with different disability levels at varying rehabilitation stages, are reviewed. Furthermore, the limitations of current methods and techniques are discussed and future research directions are put forward.
Improved adaptive-critic-based dynamic event-triggered control for non-affine systems
Received
19 Jun 2023
Accepted
07 Aug 2023
Published
17 Aug 2023
In this paper, by employing a recurrent neural network and a critic neural network (CNN), we design an improved dynamic event-triggered controller for a class of non-affine continuous-time nonlinear systems. To address the transformation of the robust-optimal control problem, an additional utility function reflecting the disturbance is introduced. Besides, a system identifier is utilized for reconstructing the non-affine dynamics to generate an affine model. For reducing the waste of communication resources, a dynamic event-triggered control strategy is developed to replace the traditional time-based structure and improve static event-triggered control design. In addition, we develop an enhanced CNN weight updating law, which allows for greater flexibility in the process of weight selection compared to the conventional approach. The dynamic event-triggered controller is designed by using the CNN framework. Finally, a simulation of a modified torsional pendulum system is performed to demonstrate the effectiveness of the constructed method.
Data-Driven Modeling and Control Strategies for Robotic Manipulators
Special Issue Editor
AI-enhanced Human-Machine Interaction Systems: Current Achievements and Beyond
Special Issue Editor