Search Results (508)

This review presents a comprehensive study of adaptive control techniques for nonlinear systems influenced by complex nonlinearities and system faults. Nonlinear systems are categorized into general, stochastic, and switched classes, with a focus on their modeling and control challenges. Common nonlinearities such as input saturation, dead-zone, and backlash-like hysteresis, along with actuator and sensor faults, are examined due to their critical impact on system performance. Fuzzy logic systems and neural networks are explored as effective function approximators capable of handling system uncertainties and complex dynamics. Their design methodologies, advantages, and implementation issues are discussed in detail. The review also highlights recent developments in fault-tolerant adaptive control using these intelligent approximators. Finally, the paper outlines open challenges and future research directions, including the integration of adaptive learning frameworks with real-time control and enhanced fault detection strategies for practical nonlinear systems. Full article

To meet the practical application requirements of underwater biomimetic robots, this paper presents the design of a flexible pectoral fin with integrated sensing and actuation capabilities, based on a “material-structure-function” integrated approach. The sensor film is embedded into the pectoral fin via an embedded cast-molding method, ensuring synchronized deformation and long-term cyclic stability. Experimental results demonstrate that the integrated pectoral fin can accurately perceive its own bending deformation and external environmental disturbances, enabling corresponding obstacle avoidance maneuvers in a manta robot prototype. This design strategy endows the manta robot with environmental adaptability for real-world applications and offers a novel paradigm for the intelligent design of other underwater equipment. Full article

This study presents a nanosensor network system for autonomous microclimate optimization in precision horticulture, leveraging a field-programmable gate array (FPGA)-based control architecture that is integrated with an edge-level machine learning inference. Unlike the conventional greenhouse automation systems, which exhibit thermal and hygroscopic hysteresis often exceeding 32 °C and 78% relative humidity, the proposed framework embeds a random forest regression (RFR) model directly within the Altera DE2-115 FPGA fabric to enable predictive environmental regulation. The model achieved an R² of 0.985 and root mean square error (RMSE) of 0.28 °C, allowing proactive compensation for the thermodynamic disturbances from the high-intensity light-emitting diode (LED) lighting with a 120 s predictive horizon. The real-time monitoring and remote supervision were supported via a NodeMCU-based IoT gateway, achieving a 140 ms mean communication latency and a 99.8% packet delivery reliability. The preliminary validation using lettuce (Lactuca sativa) optimized the environmental parameters, while the subsequent experiments with pepper (Capsicum annuum), a commercially important and environmentally sensitive crop, demonstrated system performance under real-world conditions. The control system maintained a temperature and humidity within ±0.3 °C and ±1.2% of the setpoints, respectively, and outperformed the baseline rule-based control with a 28% increase in fresh biomass, a 22% improvement in dry matter accumulation, a 25% reduction in actuator duty-cycle switching, and an 18% decrease in overall energy consumption. These results highlight the efficacy of FPGA-integrated edge intelligence combined with low-latency IoT telemetry as a scalable, energy-efficient, and high-fidelity solution for sub-degree environmental control in next-generation, controlled-environment, and vertical farming systems. Full article

Ensuring experimental reproducibility and reliable isolation of crop responses remain critical challenges in vertical farming and controlled-environment horticulture, where minor microclimatic fluctuations can mask treatment effects and compromise comparability across experiments. This study presents an intelligent, low-cost IoT-based climate management system designed as a methodological framework to stabilize environmental conditions and support reproducible crop responses in vertical horticulture. The system integrates real-time multi-sensor monitoring of temperature, relative humidity, atmospheric pressure, and CO₂ concentration with automated high-power actuation for lighting and ventilation within a unified control framework. The platform was validated using lettuce (Lactuca sativa L. cv. Ofelia) cultivated under controlled vertical farming conditions, where environmental stability enabled the reliable detection of plant responses to contrast light spectra. Crop performance was evaluated through biomass accumulation, morphological traits, and nutritional quality parameters. The intelligent control system maintained environmental setpoints within narrow ranges throughout the cultivation cycle, minimizing microclimatic variability across vertical tiers. As a result, observed differences in plant growth and biochemical composition were less likely to be confounded by environmental drift. By shifting the role of IoT technologies from simple automation tools to experimental enablers, this work illustrates how intelligent climate control can support reproducibility, scalability, and methodological robustness in vertical horticulture research. The proposed open, modular architecture provides a transferable framework for reproducible crop experimentation and production in controlled-environment systems. Full article

Nanocellulose materials (CNMs), encompassing cellulose nanocrystals (CNCs), cellulose nanofibrils (CNFs), and bacterial nanocellulose (BNC), have emerged as a versatile and sustainable class of bio-based nanomaterials with significant promise for applications in mechanical engineering. This review systematically examines the processing of nanocellulose via mechanical, chemical, and enzymatic routes, alongside surface modification strategies that enhance performance and address scalability challenges. A principal advantage of CNMs lies in their exceptional mechanical properties, including superior strength, stiffness, and toughness, which position them as high-performance, sustainable reinforcement agents for advanced composites. Beyond mechanical reinforcement, CNMs exhibit a suite of functional properties critical for engineering design, such as thermal stability, tunable conductivity, effective gas/moisture barrier performance, and improved tribological behavior. These characteristics enable their use in diverse high-value applications, including lightweight composites, protective coatings, energy storage devices, sensors, actuators, and intelligent material systems. Furthermore, the inherent renewability, biodegradability, and recyclability of nanocellulose align closely with the principles of a circular economy and green engineering. However, the successful integration of CNMs into mainstream manufacturing requires overcoming key challenges. These include the energy intensity of certain production processes, inherent moisture sensitivity, long-term stability under operational conditions, and compatibility with established industrial techniques. Life-cycle analyses reveal important environmental trade-offs that must be navigated. Overall, nanocellulose represents a renewable, multi-functional material platform whose unique combination of mechanical performance, functional versatility, and environmental benefits is poised to drive innovation in next-generation engineering materials. Full article

Underwater vehicles (UVs) are pivotal for ocean exploration, yet their effectiveness is fundamentally constrained by acoustic performance in noisy and dynamic seas. Self-noise, non-stationary interference, and extreme conditions not only degrade sensing, navigation, and stealth but also cascade into losses in propulsion efficiency, actuation reliability, and control precision. This review provides a system-performance-oriented synthesis of advances across four key areas: bioinspired and intelligent noise reduction materials/structures, active noise control and adaptive signal processing, noise-robust navigation and collaborative localization, and deep learning-enhanced acoustic perception. Key findings indicate that bioinspired surfaces reduce flow noise by ≈5 dB, adaptive filtering improves $\mathrm{SNR}$ by up to 20 dB, and distributed robust filtering ensures multi-AUV consistency under uncertainty. These developments collectively establish acoustic performance not as a parallel metric, but as a fundamental enabler and critical bottleneck for the integrated propulsion-actuation-control stack of next-generation UVs. Consequently, this review outlines viable pathways toward high-performance acoustic–mechanical integration. Full article

Linear ultrasonic motors (LUSMs) occupy an important position in the field of high-precision actuation due to their advantages of simple structure, high control accuracy and direct linear motion generation. This review first classifies LUSMs according to wave modes into traveling wave linear ultrasonic motors (TWLUSMs) and standing wave linear ultrasonic motors (SWLUSMs). Among them, TWLUSMs include the straight beam type and the annular beam type, while SWLUSMs consist of the single-foot type and the multi-foot type. In addition, the working principles of TWLUSMs and SWLUSMs are elaborated. The structural characteristics and performance parameters of different types of ultrasonic motors (USMs) are sorted out, and the analysis shows that SWLUSMs are significantly superior to TWLUSMs in terms of output speed and output force. This review summarizes the application status of LUSMs in fields such as biomedicine, deep-sea exploration, aerospace and precision manufacturing, and finally outlines the development trends of LUSMs from the aspects of miniaturization and lightweighting, extreme environment adaptability and intelligent upgrade. This review provides a comprehensive reference for the structural design, performance improvement and application expansion of LUSMs. Full article

The vibration signals of Electro-Hydrostatic Actuators (EHAs) exhibit strong non-linearity and non-stationarity, particularly under complex coupling mechanisms, making the extraction of intrinsic fault features computationally challenging. Conventional deep learning approaches often lack mathematical interpretability and struggle to decouple superimposed fault signatures from incomplete datasets. To address these issues, this paper proposes the Enhanced Continuous Wavelet Transform Capsule Network (ECWTCN), an intelligent decoupled diagnosis framework designed for multiscale signal analysis. The architecture integrates a wavelet-kernel convolution layer to extract physically interpretable time–frequency features across multiple scales, effectively capturing transient impulses associated with incipient faults. Furthermore, a novel maximized aggregation routing algorithm is introduced to optimize the dynamic routing process, enhancing global feature aggregation. A distinct advantage of the ECWTCN is its capability to generalize distinct fault patterns, enabling the identification of unseen compound faults by training exclusively on normal and single-fault samples. Comparative experiments show that the proposed method delivers strong multi-label classification performance under operating condition A, achieving a Subset Accuracy of 93.7% and a Label Ranking Average Precision of 0.998. Complexity analysis further confirms the method’s efficiency in terms of FLOPs and parameter size. This work presents a robust, lightweight, and mathematically interpretable solution for the analysis of complex signals in high-reliability equipment. Full article

The flexible upper-limb exoskeleton robot (exosuit) is composed of fabrics, soft actuators and compliant force-transmitting structures, which provides assistance or rehabilitation training for the shoulders, elbows, wrists and hands. By realizing human–robot collaboration, this kind of system has the advantages of comfort, light weight and portability, thus promoting motor function recovery and neural plasticity. This review establishes a classification and comparison framework for flexible upper-limb exoskeletons based on the actuation modalities and systematically summarizes the research progress under different actuation modalities. The relevant literature published from 2015 to 2025 was retrieved from the EI, IEEE Xplore, PubMed and Web of Science databases. After screening according to the preset inclusion and exclusion criteria, a total of 64 original research papers meeting the criteria were finally included for analysis. According to the actuation modalities, the flexible upper-limb exoskeleton robot is classified, and all kinds of systems are summarized and compared. Motor–cable/tendon actuation and pneumatic/hydraulic actuation have advanced substantially and are approaching technical maturity for flexible upper-limb exoskeletons. Meanwhile, designs based on passive/hybrid mechanisms (e.g., elastic energy storage elements and clutches) and new intelligent material actuations are showing a diversified development trend. In the future, the development is expected to further focus on lightweight and compliance, and by integrating multimodal sensing and feedback control, motion intention recognition and human–robot interaction theories, actuation systems will be developed towards modularization, intelligence and high-power density, in order to achieve more comfortable, lighter and more effective flexible upper-limb exoskeleton systems. Full article

This paper proposes a hierarchical control framework designed to enhance the path tracking accuracy of intelligent electric vehicles under diverse operating conditions. For lateral control, an improved model predictive control strategy is developed, utilizing a fuzzy inference system for parameter initialization and a Deep Deterministic Policy Gradient algorithm for online adaptive tuning. For longitudinal control, a proportional–integral–derivative controller is optimized via a hybrid genetic algorithm–particle swarm optimization method. Co-simulations conducted in CarSim/Simulink under straight-line, double-lane-change, and double-sine-wave maneuvers demonstrate that the proposed framework significantly reduces lateral deviation and heading error while ensuring smoother actuator response. Compared to conventional MPC and PID controllers, the proposed method reduces maximum lateral error by over 50% and settling time by 60%, confirming its effectiveness and robustness in complex tracking scenarios. Full article

Urban traffic congestion leads to longer travel times, economic losses, and increased pollution. Recent advances in the Internet of Things (IoT) provide detailed real-time traffic data, yet testing adaptive control strategies directly on live networks remains costly and risky. To address this challenge, we propose an IoT-driven digital twin framework for the design and evaluation of AI-based traffic management systems. The framework is implemented in the Simulation of Urban MObility (SUMO) and uses its Python 3.14.2 API to emulate a dense network of IoT sensors that stream real-time information on vehicle density, queue lengths, and waiting times. This simulated IoT data feeds an AI agent that adapts traffic signal control in real time. The agent is trained with a composite reward function to jointly minimise vehicle waiting times and emissions. Its performance is compared with fixed-time and vehicle-actuated control under varying traffic demand scenarios. Results demonstrate the effectiveness of combining IoT-based simulation with AI control, providing a safe and scalable pathway towards the real-world deployment of intelligent traffic management systems. Full article

Human movement emerges from complex interactions between neural processes, musculoskeletal dynamics, and environmental constraints, resulting in behavior that is inherently nonlinear. Therefore, nonlinear dynamical systems approaches have been widely used to characterize variability, stability, and coordination in motor behavior. However, despite their conceptual value, these methods are often applied post hoc and remain limited in their ability to support prediction, control, and integration of high-dimensional multimodal data. Artificial intelligence (AI) provides a complementary modeling framework capable of addressing these limitations. Yet many current AI applications treat motor signals primarily as feature sets for classification or regression, leaving the underlying dynamical structure of movement underexplored. This review synthesizes recent research that integrates AI with nonlinear motor control analysis to model, interpret, and control human movement across neural, biomechanical, and behavioral domains. We organize related studies according to the type of nonlinear motor control problem addressed, including input–output mappings, temporal dynamics, and adaptive control policies under conditions of partial observability and nonstationarity. Across these examples, we show that AI becomes scientifically informative when constrained and evaluated by nonlinear dynamical constructs such as attractors, phase relationships, manifolds, and stability structures. Finally, we discuss current limitations and outline future directions toward theory-informed, explainable, and closed-loop AI models for motor control and human–actuator interaction. Full article

This study examines the pull-out behaviour of power-actuated fasteners (PAFs) in concrete through a combined analytical, experimental, and probabilistic approach. An experimental programme with 40 power-actuated fasteners (PAFs) installed in plain concrete was carried out to investigate how penetration depth, curvature, and surface damage influence pull-out resistance. Image-based analysis of the concrete surface provided quantitative geometric indicators that were correlated with the measured capacities. The analytical model originally proposed by Gerber was recalibrated using these parameters, resulting in improved agreement with the experimental results. In addition, a probabilistic model was developed to describe the likelihood of insufficient pull-out capacity as a function of measurable installation parameters. The combined framework links geometric characteristics and material response to the reliability of PAF anchorage and highlights the potential of measurable post-installation data for the intelligent, data-driven assessment of fastening performance. Full article

Modern agriculture has to alleviate extremes in water demand and/or water waste. In this regard, this work showcases how soil moisture instruments can be combined with low-end microcontrollers, energy-efficient communication protocols, single-board computers, flow and pressure sensors, and purpose-built actuators to form a synergistic platform able to generate and study realistic irrigation scenarios. These scenarios, potentially emulating anomalies such as clogged emitters or pipe leaks with a satisfactory time granularity of a few minutes, provide valuable data that pave the way for the creation of intelligent models intercepting water misuse events and/or irrigation failures. The proposed system utilizes widely available, well-documented, low-cost components to form a functioning whole which is optimized for outdoor, low-power, low-maintenance and long-term operation and is accessible remotely via typical end-user devices. Two drip irrigation points were set up, each having a TEROS 12 and a TEROS 10 instrument placed at different depths, while a prototype water flow/pressure control and report system was developed. All modules sent data in real time, via LoRa, to a central node implemented using a Raspberry Pi for further processing and to make them widely available via common network infrastructures, also provisioning for remote scenario invocation. The system does not claim to achieve specific irrigation water savings, but it contributes to maintaining/increasing the benefits of modern irrigation practices (such as drip irrigation). This goal is served by emulating a wide variety of irrigation events and by gathering and studying the corresponding data. These multimodal data are collected at a frequency of a few minutes, reflecting key irrigation-specific parameters with an accuracy better than or equal to 3%. The exact steps for specific hardware and software component interoperation are clearly explained, allowing other teams of researchers and/or university educators worldwide to be inspired and benefit from platform replication. Full article