This paper addresses experimental PID tuning for DC motor actuators when an accurate plant model is unavailable or impractical to obtain. Controller tuning is formulated as a constrained closed-loop optimization process in which each PID gain set is...
Tribology, the science of friction, wear, and lubrication, governs the reliability of nearly every mechanical system. Tribological contacts account for approximately 23% of global energy consumption, including 20% used to overcome friction and 3% ass...
The cathode material design is a persistent challenge in the development of next-generation rechargeable batteries. The cathode performance is critically influenced by certain key parameters, i.e., composition, crystal structures, ion transport, and...
Closed-loop autonomous driving increasingly combines model-centric architectural development with data-centric learning. These two directions are complementary: model-centric methods improve representation, prediction, planning, and control capabilit...
As visual simultaneous localization and mapping (vSLAM) is easy disturbed by the changes of camera viewpoint and scene appearance when building a globally consistent map, the robustness and real-time performance of key frame image selections cannot m...
Free space optical (FSO) communication, which has the potential to meet the demand for high-data-rate communications between satellites and ground stations, requires accurate alignment between the transmitter and receiver to establish a line-of-sight...
Typically, the current and speed loop closure of servo motor of the parallel platform is accomplished with incremental PI regulation. The control method has strong robustness, but the parameter tuning process is cumbersome, and it is difficult to ach...
Aimed at the poor recognition effect of current educational robots on objects with complex shapes and colors and the single design of related experiments, this paper proposes a robot teaching instrument. The robot adopts a servo motor with an encoder...
Artificial intelligence (AI) is transforming lithium-ion battery (LIB) research by linking heterogeneous data, predictive modeling, mechanistic interpretation, and experimental validation within a closed-loop paradigm. AI does not uniquely enable non...
Soft robotic manipulators consisting of serially stacked segments combine actuation and structure in an integrated design. This design can be miniaturised while providing suitable actuation for potential applications that may include endoluminal surg...
In order to protect the environment, an increasing number of people are paying attention to the recycling and remanufacturing of EOL (End-of-Life) products. Furthermore, many companies aim to establish their own closed-loop supply chains, encouraging...
Safety signs in innovative manufacturing environments fail to match dynamic risks due to the separation of perception, semantics, and decision-making. Existing methods lack closed-loop integration of IoT sensor streams, knowledge graph reasoning, and...
Robotic-assisted systems have gained significant traction in post-stroke therapies to support rehabilitation, since these systems can provide high-intensity and high-frequency treatment while allowing accurate motion-control over the patient’s progre...
To address the residual lateral coil misalignment after an unmanned aerial vehicle (UAV) lands on a fixed wireless-charging platform, this study proposes a graph-attention-based knowledge distillation method for embedded self-alignment localization....
With the growing demand for trustworthy multi-party data sharing, federated learning has demonstrated broad potential in cross-entity collaborative modeling. However, it still faces challenges such as insufficient participant engagement, inaccurate c...
Peen forming is a dieless manufacturing process used to shape large, thin aerospace panels through controlled shot impacts that induce residual stresses and curvature. Despite long-standing industrial use, process monitoring still depends largely on...
In this article, an adaptive neural network (ANN) controller based on feature augmentation (FA) is designed for quadrotors. The proposed controller consists of two components: a position sub-controller and an attitude sub-controller. We use the ANN t...
Animal Physiology Laboratory requires students to integrate physiological knowledge, experimental procedures, animal handling, real-time signal interpretation, and post-class reflection. To address the fragmentation of conventional laboratory teachin...
This paper presents a novel trajectory-tracking technique for servo systems treating only the position measurement as the output subject to practical concerns: system parameter and load uncertainties. There are two main contributions: (a) the use of...
Unmanned Aerial Vehicles (UAVs), particularly multirotor drones, require rigorous structural monitoring to ensure safe and reliable operation. Visual inspections are often inefficient and may miss early signs of damage. Even when faults are detected...
To address the tracking control problem of the periodic motion fast tool servo system (FTS), we propose a control method that combines adaptive sliding mode control with closed-loop iterative learning control. Adaptive sliding mode control enhances t...
Lean implementations often deploy tools in isolation, leaving gaps in how abnormalities are exposed, resolved at the root cause, escalated when needed, and converted into organisational learning. This study proposes a five-stage closed-loop routine f...
Neural ordinary differential equations (Neural ODEs) describe the feature evolution of deep networks by continuous-time dynamical systems and enable end-to-end learning through differentiable numerical solvers. Nevertheless, in closed-loop rolling pr...
To enable autonomous driving in real-world environments that involve a diverse range of geographic variations and complex traffic regulations, it is essential to investigate Deep Reinforcement Learning (DRL) algorithms capable of policy learning in h...
This study developed a frequency-domain fault-tolerant iterative learning control (ILC) strategy for spatially interconnected systems subject to external disturbances and actuator faults. An equivalent one-dimensional system model was established by...
Biochar water treatment stands at a decisive crossroads, where the promise of large-scale application meets the reality of laboratory trial-and-error. This study contends that the fundamental bottleneck to progress lies in the field’s persisten...
This study presents a modular battery charging system based on a DC–DC buck converter with proportional–integral (PI) control, developed to support hands-on learning in power electronics education. In response to the need for flexible exp...
Chalcogenides challenge materials artificial intelligence (AI) because their properties depend on defects, disorder, metastability, interfaces, processing history, and operating conditions, as well as composition and structure. This review examines A...
Additive engineering plays a crucial role in enhancing the performance of perovskite solar cells (PSCs), yet identifying suitable additives within the vast chemical space remains a significant challenge. This paper describes a paradigm shift in addit...
To address the challenges of acquiring precise dynamic models for electromechanical servo systems and the susceptibility of system state information to noise, a backstepping command filter controller based on reinforcement learning is proposed. This...
Advancements in bioelectronic sensors and actuators have paved the way for real-time monitoring and control of the progression of wound healing. Real-time monitoring allows for precise adjustment of treatment strategies to align them with an individu...
This article presents the synthesis, implementation, and experimental study of a PI-based adaptive actor–critic displacement volume controller of an axial-piston pump intended for open-loop circuit hydraulic drive systems. The proposed control...
Hydrogel additive manufacturing underpins soft tissue models, biointerfaces, and soft robotics. The coupled choices of formulation, rheology, and process conditions limit the progress. This review maps how artificial intelligence links composition to...
Short-term traffic flow forecasting, an essential enabler for intelligent transportation systems, is a fundamental and challenging task for dramatically changing traffic flow over time. In this paper, we present a gravitational search optimized kerne...
With the advancement of smart education and carbon neutrality goals, optimizing Indoor Environmental Quality (IEQ) while minimizing energy consumption is critical. Traditional PID or rule-based strategies struggle with the strong non-linearity and ti...
In adaptive optics systems, the precision wavefront sensor determines the closed-loop correction effect. The accuracy of the wavefront sensor is severely reduced when light energy is weak, while the real-time performance of wavefront sensorless adapt...
Energy materials development remains experimentally intensive because relevant knowledge is dispersed across heterogeneous sources, material formulas and processing parameters are strongly coupled, and experimental data are often incomplete. Although...
This paper presents a human-in-the-loop interactive framework for skeleton-based posture recognition, designed to support model training and artistic education. A total of 4870 labeled images are used for training and validation, and 500 images are r...
The continuous path of a manipulator is often discretized into a series of independent action poses during path tracking, and the inverse kinematic solution of the manipulator’s poses is computationally challenging and yields inconsistent resul...
Against the backdrop of the digital transformation in engineering education, this study developed and implemented a Learning Analytics (LA)-driven and Artificial Intelligence (AI)-supported blended learning model to address structural challenges in t...
With increasing renewable energy integration, electricity spot markets exhibit high volatility and uncertainty, making it difficult to balance profit and risk. To address this challenge, this paper proposes Joint (Version 1.0), a multi-agent closed-l...
In fiber Bragg grating (FBG) sensing demodulation systems, high-precision peak detection is a core requirement for demodulation algorithms. However, practical spectral signals are often susceptible to environmental noise interference, which leads to...
The deep integration and application of artificial intelligence to organic chemistry are propelling the development of organic chemistry synthesis laboratories toward an intelligent automated laboratory model characterized by “hardware + softwa...
Programmable quantum simulators based on Rydberg atom arrays provide a versatile platform for data-driven quantum simulation of strongly correlated systems, combinatorial optimization problems, and artificial quantum materials. In this review, we pre...
Accurate and robust simultaneous localization and mapping (SLAM) systems are crucial for autonomous underwater vehicles (AUVs) to perform missions in unknown environments. However, directly applying deep learning-based SLAM methods to underwater envi...
This paper proposes a solution to the problem of mobile robot navigation and trajectory interpolation in dynamic environments with large scenes. The solution combines a semantic laser SLAM system that utilizes deep learning and a trajectory interpola...
Smart agriculture and smart breeding are evolving from isolated sensing toward geographically distributed, long-term, data-driven closed-loop management. Dispersed farms and breeding sites, UAV and remote-sensing phenotyping, real-time field control,...
General large language models (LLMs) often suffer from semantic misinterpretation, information redundancy, and hallucinated content when applied to educational question-answering tasks. These issues hinder their effectiveness in supporting students&r...
High-entropy alloys have demonstrated significant application potential in many industrial fields due to their outstanding comprehensive properties. However, the complex multi-component compositions pose challenges for traditional design approaches....
This paper presents a novel learning-based control algorithm for three-phase AC/DC converters, which are key components in DC microgrids, for reliable power conversion. In contrast with conventional model-based nonlinear controllers that rely on deta...
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