IIRJET

Design and Development of a Conventional Mechatronic System with Deep Learning–Based Industrial Applications

2026-02-03T11:16:56+00:00

The significance of incorporating deep learning methods into diverse fields has grown because of their transformative influence on resolving complex issues, improving efficiency, and unlocking new capabilities. By integrating deep learning, mechatronic devices and systems can become more intelligent, adaptive, and effective in their operations. Deep learning techniques enable these systems and devices to learn from data, identify patterns, and make decisions in real time, thereby improving their ability to adapt to changing environments and maximize performance. The proposed system combines mechanical components, electronic hardware, sensors, actuators, and embedded control units with advanced deep learning models to achieve intelligent decision-making and improved operational efficiency. Deep learning techniques are employed to analyze complex, high-dimensional sensor data for tasks such as system state estimation, performance optimization, fault detection, and predictive analysis in industrial environments. The developed framework supports adaptive learning capabilities, allowing the system to respond dynamically to changing operational conditions. The paper delineates the promising prospect for deep learning integration in mechatronics, emphasizing collaborative efforts among academia, industry, and regulators to ensure responsible deployment of these technologies. This paper serves as a guiding framework for researchers, engineers, and policymakers, facilitating the effective integration of deep learning methodologies in mechatronics devices and systems.

Data-Driven Identification of Friction Dynamics in Mechatronic Systems using Machine Learning

2026-02-03T11:24:57+00:00

High-performance control and precise functioning of mechatronic systems depend on accurate modelling of friction dynamics; nevertheless, non-linear, time-varying, and system- specific friction effects are frequently missed by traditional friction models. In this paper, a data-driven model for machine learning-based friction dynamics identification in mechatronic systems is presented. The suggested method learns friction behaviour from the measured system data, allowing for an accurate depiction of complicated non-linear dynamics without the need for explicit theoretical friction formulations. A variety of neural network-based models, such as feedforward neural networks (FNNs), convolutional neural networks (CNNs), long short-term memory (LSTM) networks, as well as transformers, as well as cutting-edge machine learning methods like physics-informed neural networks (PINNs) and sparse identification of nonlinear dynamics (SINDy), are included in the framework. The integration of these methods to real-world systems is the main focus. The efficacy of the FNN, CNN, LSTM, transformers, SINDy, & PINN methods for data-driven friction modelling and system identification is assessed using a geared DC motor as a case study. The findings show that for this traditional nonlinear dynamical system, all machine learning techniques under consideration provide excellent predictive performance. Furthermore, compared to solely data-driven black-box models, the SINDy and PINN models provide improved interpretability. The comparison analysis reveals each approach's advantages and disadvantages with regard to computing complexity, interpretability, and forecast accuracy. Potential uses and future research paths are explored, and the suggested models offer a versatile basis for friction-aware modelling as well as control of mechatronic systems.

Deep Learning–Based Adaptive Flight Control for Nonlinear Aerospace Systems

2026-02-03T11:28:00+00:00

Deep learning (DL) has emerged as a fast expanding field of study in recent decades, redefining the state-of-the-art in a variety of methods, including speech recognition and object detection. Many projects in the fields of aircraft design, behaviour, and control rely on the extensive data-driven approach. These projects include the development of flight control systems, intelligent sensing, fusion-based prognosis and health management, and airliner flight safety monitoring. Aerodynamic nonlinearities, outside influences, and parameter fluctuations result in highly nonlinear, unpredictable, and time-varying dynamics for modern aerospace vehicles. Traditional robust and adaptive control methods frequently rely on permanent structures and simple models, which might restrict performance in situations where flying conditions change quickly. A deep learning-based adaptive flight control structure for nonlinear aircraft systems that learns and adjusts for unknown dynamic in real time is presented in this research. While an adaptive control rule guarantees closed-loop safety and trajectory tracking performance, a deep neural network is used to simulate modelling uncertainties and unmodeled nonlinearities. A nonlinear aeroplane model is used to test the suggested method under various aerodynamic circumstances and outside disruptions. The potential of machine learning for next-generation smart flight control systems is highlighted by simulation findings that show increased tracking accuracy, resilience, and flexibility when compared to conventional model-based adaptive controllers.

Quantum Computing for Multi-Omics Data Integration in Bioengineering Applications

2026-02-03T11:30:29+00:00

Integrating the various omics to conduct a thorough research of biological systems is known as multi-omics. It enables a comprehensive comprehension of the intricate relationships and dynamics that exist within an organism. Understanding complicated biological systems and developing bioengineering applications like disease modelling, metabolism engineering, & precision medicine depend on integrating of multi-omics data. However, traditional computational methods are severely hampered by the high complexity, heterogeneity, & nonlinear interactions among genomes, genomics, proteomics, and metabolomics data. In order to effectively describe intricate cross-omics interactions, this research proposes a quantum computing-based framework for multiple-omics data integration that makes use of the concepts of quantum juxtaposition and entanglement. A hybrid cognitive–classical architecture is suggested, wherein classical optimisation methods are used for training and multi-omics features are converted into states of matter and processed utilising variational quantum circuits. Phenotype prediction and route analysis are two downstream bioengineering tasks that make use of the integrated quantum representations. In comparison to classical approaches, the suggested strategy delivers better integration efficiency and prediction performance, as demonstrated by experimental assessments utilising simulated quantum settings. The findings demonstrate quantum computing's promise as a potent instrument for precise and scalable multi-omics data integration, opening the door for applications in systems biology and bioengineering of the future.

Quantum Deep Learning for Structural Health Monitoring of Bridges and Buildings

2026-02-03T11:33:18+00:00

Environmental impacts can cause brace damage, cracking, loss of stiffness, and other damage to buildings, bridges, and frame structures. By identifying damage early on, Structural Health Monitoring (SHM) technologies could avert catastrophic disasters. Deep Learning (DL), which has advanced quickly in recent years, has been used in SHM to efficiently extract features in order to identify, locate, and assess various damages. In order to guarantee the stability and long-term serviceability, structural health monitoring (SHM) of buildings and bridges is crucial. However, traditional deep learning approaches encounter difficulties with enormous amounts of sensing data, computational expense, and scaling in large infrastructure structures. This research introduces a quantum deep learning (QDL) paradigm that takes advantage of the representational and parallelism benefits of quantum computing for structural health monitoring. The suggested method combines quantum feature encoding, deep variational quantum circuits, and classical data from sensors preprocessing for damage detection and architectural condition evaluation. In order to improve feature learning performance while lowering model complexity, a hybrid fundamental–classical architecture is created in which quantum layers are integrated into deep neural networks. Benchmark vibration and strain datasets from viaduct and construction structures under various damage scenarios are used to test the framework. According to experimental findings, the suggested quantum deep neural network model performs better than traditional deep learning techniques in terms of computational efficiency, resistance to noise, and detection accuracy. The results show that next-generation autonomous monitoring systems for structural health in civil construction have a promising future thanks to quantum deep learning.