Search Results (357)

The Voith Schneider Propeller (VSP) operates with blades undergoing an approximately sinusoidal periodic motion along a circular path. Hydrodynamically, the continuous significant variation in the angle of attack between the blades and incoming flow, together with additional inertial effects caused by accelerated rotation, complicates the computation and measurement of hydrodynamic performance. To investigate the unsteady hydrodynamic behavior resulting from this coupled motion, a numerical model incorporating adaptive mesh refinement was developed to simulate VSP performance. Based on insights into the interaction between blade motion and hydrodynamics, an experimental platform was designed using servo motors to achieve precise synchronized blade control, enabling mutual validation between numerical simulations and transient hydrodynamic measurements. Results demonstrate that the coupled blade motion induces nonlinear variations in hydrodynamic forces. Rotational power loss limits VSP efficiency, and a negative thrust regime occurs at high advance coefficients. Rapid blade flipping leads to flow separation, identified as the primary cause of nonlinear lateral forces. The consistency between numerical and experimental results provides reliable data supporting theoretical studies. These findings offer valuable insights for optimizing motion control strategies in cycloidal propeller applications. Full article

This study proposes a novel framework to enhance inner race fault features in servo motor bearings by acquiring rotary encoder-derived instantaneous angular speed (IAS) signals, which are obtained from a servo motor encoder without requiring additional external sensors. However, such signals are often obscured by strong periodic interferences from motor pole-pair and shaft rotation order components. To address this issue, three key improvements are introduced within the cyclic blind deconvolution (CYCBD) framework: (1) a comb-notch filtering strategy based on rotation domain synchronous averaging (RDA) to suppress dominant periodic interferences; (2) an adaptive fault order estimation method using the autocorrelation of the squared envelope spectrum (SES) for robust localization of the true fault modulation order; and (3) an improved envelope harmonic product (IEHP), based on the geometric mean of harmonics, which optimizes the deconvolution filter length. These combined enhancements enable the proposed improved CYCBD (ICYCBD) method to accurately extract weak fault-induced cyclic impulses under complex interference conditions. Experimental validation on a test rig demonstrates the effectiveness of the approach in enhancing and extracting the fault-related features associated with the inner race defect. Full article

Although electro-hydrostatic actuators (EHAs) hold broad application prospects in more-electric aircraft and high-end equipment, they face a difficult trade-off between dynamic response and energy efficiency. To simultaneously enhance the dynamic response and energy efficiency of the EHA, this paper designs an innovative variable pump displacement and variable motor speed (VPVM) configuration that utilizes an electro-hydraulic servo valve for active displacement control. To address the flow mismatch problem associated with traditional asymmetric single-rod cylinders without reducing the power density of EHA, this paper also designs an innovative symmetric single-rod cylinder configuration. Based on the above two innovative configurations, this paper further develops a corresponding EHA prototype with a rated power density of 0.72 kW/kg. Simulation and experimental results demonstrate that compared to the traditional EHA with the fixed pump displacement and variable motor speed configuration (FPVM-EHA), the EHA with the proposed VPVM configuration (VPVM-EHA) not only improves energy efficiency and reduces motor heat generation under low-speed and heavy-load conditions, but also achieves a dynamic response close to that of the FPVM-EHA under fast dynamic response conditions. Full article

This paper introduces a new quadruped spider robot platform specializing in environmental reconnaissance and mapping. The robot measures 180 mm × 180 mm × 95 mm and weighs 385 g, including the battery, providing a compact yet capable platform for reconnaissance missions. The robot consists of an ESP32 microcontroller and eight servos that are disposed in a biomimetic layout to achieve the biological gait of an arachnid. One of the major design revolutions is in the power distribution network (PDN) of the robot, in which two DC-DC buck converters (LM2596M) are used to isolate the power domains of the computation and the mechanical subsystems, thereby enhancing reliability and the lifespan of the robot. The theoretical analysis demonstrates that this dual-domain architecture reduces computational-domain voltage fluctuations by 85.9% compared to single-converter designs, with a measured voltage stability improving from 0.87 V to 0.12 V under servo load spikes. Its proprietary Bluetooth protocol allows for both the sending and receiving of controls and environmental data with fewer than 120 ms of latency at up to 12 m of distance. The robot’s mapping system employs a novel motion-compensated probabilistic algorithm that integrates ultrasonic sensor data with IMU-based motion estimation using recursive Bayesian updates. The occupancy grid uses 5 cm × 5 cm cells with confidence tracking, where each cell’s probability is updated using recursive Bayesian inference with confidence weighting to guide data fusion. Experimental verification in different environments indicates that the mapping accuracy (92.7% to ground-truth measurements) and stable pattern of the sensor reading remain, even when measuring the complex gait transition. Long-range field tests conducted over 100 m traversals in challenging outdoor environments with slopes of up to 15° and obstacle densities of 0.3 objects/m² demonstrate sustained performance, with 89.2% mapping accuracy. The energy saving of the robot was an 86.4% operating-time improvement over the single-regulator designs. This work contributes to the championing of low-cost, high-performance robotic platforms for reconnaissance tasks, especially in search and rescue, the exploration of hazardous environments, and educational robotics. Full article

Improving energy efficiency in buildings is critical for supporting sustainable growth in the construction sector. In this context, the implementation of passive solar solutions in the building envelope plays an important role. Trombe wall is a passive solar system that presents great potential for passive solar heating purposes. However, its performance can be enhanced when the Internet of Things is applied. This study employs a multi-domain smart system based on Matter-enabled IoT technology for maximizing Trombe wall functionality using appropriate 3D-printed ventilation grids. The system includes ESP32-C6 microcontrollers with temperature sensors and ventilation grids controlled by actuated servo motors. The system is automated with a Raspberry Pi 5 running Home Assistant OS with Matter Server. The integration of the Matter protocol provides end-to-end interoperability and secure communication, avoiding traditional systems based on MQTT. This work demonstrates the technical feasibility of implementing smart ventilation control for Trombe walls using a Matter-enabled infrastructure. The system proves to be capable of executing real-time vent management based on predefined temperature thresholds. This setup lays the foundation for scalable and interoperable thermal automation in passive solar systems, paving the way for future optimizations and addicional implementations, namely in order to improve indoor thermal comfort in smart and more efficient buildings. Full article

With the rapid advancement of intelligent technologies, permanent magnet synchronous motor (PMSM) servo systems have seen increasing applications in industrial fields, accompanied by continuously rising control performance demands. Moreover, the adjustment of controller parameters is pivotal for the performance optimization of servo systems. This paper presents an optimization method for PMSM servo systems based on the coupling technique of the neural network surrogate model and intelligent optimization algorithm. A hybrid model is constructed by the proposed method, integrating a mathematical model based on transfer functions with an artificial neural network surrogate model, which is employed to compensate for the discrepancies between the mathematical model and the actual measured values. The accuracy and superiority of the hybrid model are comprehensively validated through training and validation loss analysis, fitting plot construction, and ablation experiments. Subsequently, based on the hybrid model, the qualitative and quantitative comparative analysis of the Pareto fronts of five commonly used multi-objective intelligent optimization algorithms is conducted. The optimal algorithm is determined through experimental validation of the optimization results to obtain the optimal result. The optimal result demonstrates that, compared to the initial result before optimization, the overshoot is reduced by 89.7%, and the settling time is reduced by 80.1%. Additionally, several other non-dominated solutions are available for selection, and all optimized results are superior to the initial result. This study provides a novel idea and method for the performance optimization of PMSM servo systems, contributing to the field with a robust and effective approach to enhance system control performance. Full article

This study explores a novel approach of using large language models (LLMs) in the real-time Proportional–Integral–Derivative (PID) control of a physical system, the Quanser QUBE-Servo 2. We investigated whether LLMs, used with an Artificial Intelligence (AI) agent workflow platform, can participate in the live tuning of PID parameters through natural language instructions. Two AI agents were developed: a control agent that monitors the system performance and decides if tuning is necessary, and an Optimizer Agent that updates PID gains using either a guided system prompt or a self-directed free approach within a safe parameter range. The LLM integration was implemented through Python programming and Flask-based communication between the AI agents and the hardware system. Experimental results show that LLM-based tuning approaches can effectively reduce standard error metrics, such as IAE, ISE, MSE, and RMSE. This study presents one of the first implementations of real-time PID tuning powered by LLMs, and it has the potential to become a novel alternative to classical control, as well as machine learning or reinforcement learning-based approaches. The results are promising for using agentic AI in heuristic-based tuning and the control of complex physical systems, marking the shift toward more human-centered, explainable, and adaptive control engineering. Full article

Autonomous ground vehicles (AGVs) are of major importance in exploration missions since they perform difficult tasks in changing or harmful environments. Mapping and exploration is crucial in hazardous areas, or areas inaccessible to humans, demanding autonomous navigation. This paper proposes a lightweight, low-cost AGV platform, which will be used in resource-constrained situations and aimed at scenarios like exploration missions (e.g., cave interiors, biohazard environments, or fire-stricken buildings) where there are serious security threats to humans. The proposed system relies on simple ultrasonic sensors when navigating and applied traversal algorithms (e.g., BFS, DFS, or $A^{*}$) during path planning. Since on-board microcomputers have limited memory, the traversal data and direction decisions are stored in a file located on an SD card, which supports long-term, energy-saving navigation and risk-free backtracking. A fish-eye camera set on a servo motor captures three photos ordered from left to right and stores them on the SD card for further off-line processing, integrating each frame into a low-frame-rate video. Moreover, when the battery level falls below 50%, the exploration path does not extend further and the AGV returns to the base station, thus combining a secure backtracking procedure with energy-efficient decisions. The resultant platform is low-cost, modular, and efficient at augmenting; thus it is suitable for exploring missions with applications in search and rescue, educational robotics, and real-time applications in low-infrastructure environments. Full article

We developed and evaluated a monitoring and control system based on the controller area network (CAN) bus with a microprocessor of Arduino UNOs and a logic analyzer as auxiliary tools. We implemented a CAN bus communication system using Arduino UNO to control servo movements and collected data from ultrasonic sensors, infrared (IR) sensors, or DHT11 sensors that measure temperature and humidity. The CAN node received the data to control the servo motor and to display the information on the liquid crystal display. While the IR sensor detects an object, the ultrasonic measurement is stopped, and the servo is set to the home position at 0°. The CAN bus communication operated effectively, enabling real-time control of the servo motor following the command from sensor data. Full article

The ironless tubular permanent magnet synchronous linear motor (TPMSLM) is in high demand for high-precision servo control applications due to its advantages of having zero cogging effect and high dynamic response. However, its electromagnetic field analysis model has not yet been perfected. This paper aims to accurately predict the magnetic field distribution and electromagnetic performance parameters of an ironless TPMSLM. Taking the axially magnetized ironless TPMSLM as an example, and disregarding the influence of the armature magnetic field on the air gap magnetic field, a simplified analytical model of the TPMSLM is established in the cylindrical coordinate system based on the equivalent magnetization current method (EMC), and the analytical formula for the air gap magnetic flux density is then derived. Subsequently, by applying electromagnetic field theory and the analytical formula for the magnetic flux density in the air gap, analytical expressions for the back electromotive force (back EMF) and thrust are derived, reducing analytical complexity while maintaining accuracy. The accuracy and practicality of the proposed analytical formulas are validated through comparisons with finite element analysis (FEA) and experimental prototypes. This analytical approach facilitates the optimization of linear motor parameters and the study of thrust fluctuation suppression, thereby laying the foundation for high-precision servo control of linear motors. Full article

In industrial scenarios, the control of permanent magnet synchronous servo motors is mostly achieved with proportional–integral controllers, which require manual adjustment of control parameters. At the same time, the performance of the servo system is usually disturbed by internal characteristic changes, load changes, and external factors. Therefore, preset control parameters may not achieve the desired optimal performance. Many scholars use intelligent algorithms, such as neural networks, to adaptively tune control parameters. However, the offline pre-training of neural networks is often time- and resource-consuming. Due to the lack of a model pre-training process in the neural network online self-tuning process, randomly setting the initial network weight seriously affects the position tracking performance of the servo control system in the start-up phase. In this paper, the physical model and the traditional frequency domain-tuning method of the three-closed-loop permanent magnet synchronous servo system are analyzed. Combined with the neural network PI control parameter self-tuning method and physical symmetry, a physical information-driven optimization framework is proposed. To demonstrate its superiority, the neural network PI controller and the proposed optimization framework are used to control the single-axis sine wave trajectory. The results show that the optimization framework proposed can effectively improve the position tracking control performance of the servo control system in the start-up phase by setting the threshold of the servo control parameters, reduce the position tracking control error to 0.75 rads in the start-up phase, and reduce the position tracking drop caused by a sudden load by 25%. This method achieves the independent optimization adjustment of control parameters under position tracking control, providing a reference for the intelligent control of permanent magnet synchronous servo motors. Full article

Sliding mode control (SMC) provides robustness and disturbance rejection in permanent magnet synchronous motor (PMSM) control but faces the challenge of speed degradation during sudden load disturbance changes without a load observer. This paper proposes a backpropagation neural network-adjusted improved SMC (BPNN-ISMC). A simplified PMSM model is established by ignoring the disturbance term. An improved arrival law is developed by optimizing the constant-speed approach term of the traditional exponential arrival law and embedding an adaptive term. A BPNN is designed with performance metrics including speed error, its derivative, and maximum error to improve training efficiency. Speed/position estimation combines a sliding mode observer with an extended Kalman filter to suppress jitter. Simulation results demonstrate the significant advantages of the BPNN-ISMC method: in set-point control, overshoot suppression is evident, and the relative error during the stable phase after sudden load disturbance increases is reduced by 93.62% compared to the ISMC method and by 99.80% compared to the SMC method. Compared to the ADRC method, although the steady-state errors are the same, the BPNN-ISMC method exhibits smaller speed fluctuations during sudden changes. In servo control, the root mean square error of speed tracking is reduced by 18.83% compared to the ISMC method, by 89.70% compared to the SMC method, and by 37.14% compared to the ADRC method. This confirms the dynamic performance improvement achieved through adaptive adjustment of neural network parameters. Full article

The primary objective of the presented study is to develop a comprehensive system for the acquisition of surface electromyographic (sEMG) data and to perform time–frequency analysis aimed at extracting discriminative features for the classification of hand gestures intended for the control of a simplified bionic hand prosthesis. The proposed system is designed to facilitate precise finger gesture execution in both prosthetic and robotic hand applications. This article outlines the methodology for multi-channel sEMG signal acquisition and processing, as well as the extraction of relevant features for gesture recognition using artificial neural networks (ANNs) and other well-established machine learning (ML) algorithms. Electromyographic signals were acquired using a prototypical LPCXpresso LPC1347 ARM Cortex M3 (NXP, Eindhoven, Holland) development board in conjunction with surface EMG sensors of the Gravity OYMotion SEN0240 type (DFRobot, Shanghai, China). Signal processing and feature extraction were carried out in the MATLAB 2024b environment, utilizing both the Fourier transform and the Hilbert–Huang transform to extract selected time–frequency characteristics of the sEMG signals. An artificial neural network (ANN) was implemented and trained within the same computational framework. The experimental protocol involved 109 healthy volunteers, each performing five predefined gestures of the right hand. The first electrode was positioned on the brachioradialis (BR) muscle, with subsequent channels arranged laterally outward from the perspective of the participant. Comprehensive analyses were conducted in the time domain, frequency domain, and time–frequency domain to evaluate signal properties and identify features relevant to gesture classification. The bionic hand prototype was fabricated using 3D printing technology with a PETG filament (Spectrum, Pęcice, Poland). Actuation of the fingers was achieved using six MG996R servo motors (TowerPro, Shenzhen, China), each with an angular range of ${180}^{\circ }$, controlled via a PCA9685 driver board (Adafruit, New York, NY, USA) connected to the main control unit. Full article

This study explores the application of 3D printing as a strategic tool in engineering education and robotics development. An embedded control system for a 3D-printed MK2 manipulator is implemented, including an Arduino microcontroller, servo motors, an analog joystick interface, and an LCD, with software developed in Arduino IDE. The design uses PLA material and a modular architecture for flexibility and extensibility. The platform is applied in laboratory training to develop algorithmic thinking and engineering creativity, demonstrating the potential of 3D printing as an integrated educational and engineering tool. Full article

This article investigates the problem of adaptive fault-tolerant trajectory tracking control for a differential-driven unmanned surface vehicle (USV) with propeller faults. A new USV control system considering a propeller servo loop is established, which is composed of kinematics, kinetics including unhealthy surge force and yaw moment, and propeller motor shaft speed dynamics. Firstly, the control design of the kinematic level derives the virtual surge speed and yaw rate, which can accurately guide the tracking design of the kinetic level. Secondly, by estimating the bound of the unknown propeller fault parameters, the virtual fault-tolerant control laws are constructed in the kinetic level, which can generate the desired motor angular shaft speeds with an active compensation feature. Thirdly, in the control design of the propeller servo loop, the command duty cycles are designed to force the actual motor shaft speeds to track the desired signals produced from the kinetic level. It can be proven that tracking errors are semiglobally ultimately uniformly bounded based on Lyapunov stability theory. Finally, simulations considering single propeller and twin propeller faults prove the validity of the developed method. Full article