In this letter, a new capacitor boost converter for a switched reluctance motor is proposed. The proposed converter can boost the motor voltage without additional switching components for an asymmetric half-bridge converter. The basic operation of the proposed converter is verified by comparison with a previously proposed converter.

In biomedical engineering and microelectronics, microgrippers have been widely used for micromanipulation. Various reports have been made on the gripping of objects using a piezo-driven microgripper. In these studies, the gripping operation was performed by switching the controller before and after contact with the object. However, the force transmission was not achieved using these conventional methods. Therefore, these conventional methods may result in damage to the object. For the realization of safety micromanipulation, perceiving the force feedback of the microgripper is one of the important technologies. Therefore, this study proposed a micro–macro bilateral control using a piezo-driven microgripper. For the bilateral control in the microgripper, the position and contact force were estimated using strain gauges. Additionally, this study developed the hysteresis compensator based on an observer theory. The hysteresis characteristics caused by the piezoelectric actuator were compensated as part of the disturbance. The usefulness of the bilateral control in the microgripper was confirmed from the experimental results.

Task planning for robots often involves defining a wide range of possible actions, which makes the process both timeconsuming and labor-intensive. This study presents an automated task-planning framework for teleoperated grasping robots, leveraging large language models (LLMs). Task instructions are provided via voice input, while object detection is performed using the No-Label Detection System (NLDS), which integrates YOLOv8 for coordinate detection and GPT-4o for semantic labeling. This configuration allows the system to flexibly recognize previously unseen objects and align visual outputs with natural language commands. The proposed framework comprises three main stages: (1) task planning based on operator instructions, (2) object detection and extraction, and (3) grasp position estimation. Experiments conducted on a physical robotic system demonstrate the framework’s capability to interpret ambiguous commands and manage overlapping objects, achieving robust performance in complex, real-world scenarios.

Lithography systems for semiconductor and display manufacturing increasingly require high-speed, high-precision positioning. Based on a scanning-stage architecture driven by iron-core linear motors, controlled magnetic attraction is leveraged to provide lateral actuation without additional actuators. Because the same attraction introduces state-dependent thrust ripple, a feedforward framework is developed that couples iterative learning control (ILC) with Gaussian process regression (GPR) to generalize ILC-refined inputs to previously unseen trajectories. To reduce the burden of time-consuming ILC experiments and the rapid growth of GPR computation with training-set size, a trajectory optimization method is proposed that trades off GP predictive variance against computational cost, enabling accurate prediction with a compact training set. The proposed method is validated on a three-degree-of-freedom experimental platform, in which scanning is performed along the primary axis while lateral and yaw offsets are held constant. Experiments consistently reduce tracking errors across a range of scan conditions, supporting the proposed approach for high-performance lithography stages.

This paper presents a key module of a newly proposed low-cost in-pipe inspection robot (IPIR) designed for aging pipeline infrastructure with varying diameters and complex geometries, including T-junctions. Conventional IPIRs, typically optimized for pipes with fixed diameters, exhibit limited performance in such environments. To address this limitation, a module enabling hybrid control of travel speed and clamping force for posture stabilization is introduced. The proposed module employs a two-link end-point drive mechanism, incorporating active wheels at the link ends and passive wheels at the joint. End-point wheel actuation regulates joint angles and movement velocity, while differential motor torque generates the required clamping force at the joint. Experimental results demonstrate stable operation and effective adaptation to a wide range of pipe diameters without the use of force sensors. Eliminating force sensors reduces system cost and structural complexity. Despite its simple structure, the module autonomously accommodates diverse pipe diameters using the link mechanism. These features support the development of a scalable IPIR platform capable of navigating complex and aging pipeline networks.

In this paper, the authors proposed the circuit model for obliquely arranged cables. The cables were divided into short segments, which were parallel to the horizontal and vertical conductor planes as reference ground, and these segments were connected in series to construct the overall model. The model parameters of each segment were determined via electromagnetic field simulation using quasi-static approximation and data interpolation. To validate the proposed model, an obliquely arranged transmission line was measured using time domain reflectometry (TDR), and the results were compared with the circuit simulation results obtained using the finite difference time domain (FDTD) method. The results showed that the variation in line constants attributed to the oblique arrangement could be determined through TDR measurements. The line constants obtained using the circuit model agreed with the TDR measurement results, which confirmed that the proposed circuit model was appropriate. The frequency-dependent behavior of the circuit model was verified by comparing the measured reflection of the common mode with the calculated results using the circuit model operated in the frequency range of 0.15 MHz–500 MHz. The calculated results were similar to the measured results, confirming the validity of the proposed circuit model.

We developed a method to compute emergency train timetables that minimize the total energy consumption. This approach supports the movement of trains during emergencies to adjacent stations using stationary energy storage systems during power failures. Determining the order for initiating emergency operations with minimum energy consumption is difficult because the potential number of combinations increases in proportion to the factorial of the number of trains. Therefore, we developed a computation method for determining the order of initiating emergency operations based on simple equations. An evaluation of the proposed method confirmed that it obtained a result that matched the optimum solution obtained achieved using the brute-force method in shorter time than brute-force method.

In factories that manufacture round bars, an accurate count of the number of bars is required before shipment. Manual counting is time-consuming and labor-intensive, and the larger the number of bars, the less accurate is the count. Therefore, we automated the counting process using semantic segmentation. The method introduces a novel idea to reduce the effort of annotating the training images and generating the ground truth. Specifically, points are manually annotated on each end face of a round bar, and ground truth is automatically generated from the points. The segmentation model was trained using the generated ground truth to extract each end face from the target image. The round bars were counted by applying labeling after removing salt-and-pepper noise. To confirm the effectiveness of our method, an image dataset was created. In experiments, the performance of DeepLabV3+ and Unet++ with conflicting features were compared. The results showed that Unet++, which considers local information, performed better.

This paper proposes an auxiliary resonant commutated pole (ARCP) converter for wireless power transfer (WPT) systems to suppress low-order harmonics in radiated emissions. To further reduce low-order harmonics, selective harmonic elimination pulse width modulation (SHE-PWM) is applied to the ARCP converter. The switching angles for SHE-PWM are determined through numerical analysis, and the switching patterns for 3- and 7-pulse PWM are derived. The effectiveness of the proposed system in reducing radiated emissions is validated using a mini model. The results show that the ARCP converter combined with 3-pulse PWM successfully suppresses third- and fifth-order current harmonics and achieves zero-voltage switching (ZVS) for all switching events. Furthermore, radiated emission measurements confirm reductions of 21 and 24 dB in the third and fifth harmonic components, respectively.

Electrically- and hybrid-excited synchronous motors (EESMs/HESMs) are increasingly being favored over permanent magnet alternating current (PMAC) motors for high-performance applications due to their superior field weakening capacity, enhanced overload capacity, and reduced reliance on permanent magnets (PMs). However, EESMs/HESMs require controlled electrical excitation for their field winding in the rotor. Traditional brush and slip ring-based excitation methods are inefficient, require frequent maintenance, and suffer from reliability issues, including wear and tear. Although existing brushless excitation systems (BESs) incorporate auxiliary machines or modify motor designs to eliminate brushes and slip rings, most rely on controlled switches placed in the rotor to reverse the polarity of the field current, necessitating complex wireless communication between the stator and rotor. To address these challenges, this study introduces a novel BES for EESMs/HESMs. The proposed system can control bipolar field current (BFC) entirely from the stator without any rotor-side controllers or wireless communication. First, the operating principle of the proposed BES, which comprises a stationary-side series resonant converter (SRC) in conjunction with a rotary transformer (RT) having stator-side primary, and two rotor-side secondary windings, is explained. Secondly, a selective multiple excitation frequency control method is presented, which utilizes selectively tuned resonant tanks in each of the secondary windings to achieve bipolar field current control entirely from the stator. This approach obviates the need for any communication between the stator and the rotor. Finally, the performance of the proposed system for a 105 kW, 6000 rpm segmented-rotor HESM is evaluated using a co-simulation framework in which the RT and HESM are modeled in ANSYS Maxwell, whereas the BFC converter is modeled in the MATLAB/Simulink and ANSYS Twin Builder. The proposed method also helps enhance the torque density of HESMs.

Using multiple transmitters is an attractive option to supply power to receivers scattered over a large area in wireless power transfer systems based on resonant inductive coupling. However, multiple transmitters often suffer from changes in the amplitude and phase of the transmitter currents due to the influence of other transmitters and receivers, a phenomenon known as cross-interference. The change in the amplitude of the transmitter current can decrease the output power of each receiver and generate strong magnetic fields that do not comply with regulatory standards. In addition, the change in the phase of the transmitter current may reduce the power factor or cause hard switching of an inverter in the transmitter. This paper proposes novel multiple transmitters to solve the problems caused by cross-interference. The input voltage of each transmitter is controlled using a DC-DC converter to maintain the amplitude of the transmitter current at a constant value. Furthermore, a simple switching circuit is installed in each transmitter to automatically cancel out unwanted electromotive forces and variations in the self-inductance of the transmitter coil that affect the phase of transmitter currents. The results of an experimental evaluation validated the effectiveness and appropriateness of the proposed method by demonstrating that the system was able to transfer power without cross-interference.

Active gate drivers (AGDs) enhance the trade-off between switching losses and voltage surges by actively controlling switching transitions. However, conventional AGDs, that vary gate resistance to adjust switching behavior, are limited in their capability to operate at high speed. This paper proposes a novel AGD architecture that combines a current source for the turn-on transition and an active gate resistor for the turn-off phase. The current source enables faster switching compared to resistance-based control and the active gate resistor employed during the turn-off phase is similar to conventional AGDs. The results of double-pulse testing show that the proposed AGD reduced turn-on losses by up to 32.4% compared to traditional gate drivers and by 6.55% compared to existing AGDs, while maintaining similar turn-off losses.

The fast response, compact form, and high torque capacity of electromagnetic clutches have enabled their widespread use in industrial applications. However, their torque characteristics exhibit significant nonlinearities, such as dead zones and hysteresis. In addition, as torque is transmitted through friction, friction material wear alters the air gap between the friction surface and armature over time. These factors make the precise torque control of electromagnetic friction clutches challenging, and relatively few studies have addressed this issue. This study proposes a surface deflection model that accounts for installation misalignment and axial elastic deformation. Experimental results reveal that axial elasticity is the primary reason for the dead zone in transmitted torque. The proposed model contributes to a better understanding of the nonlinear behavior of electromagnetic friction clutches and provides a foundation for the development of new methods to control torque.

This paper proposes a controller design method to improve track-following accuracy in a dual-stage actuator hard disk drive (HDD). The proposed approach employs a sensitivity decoupling structure and convex optimization based on the evaluation of the 𝐻₂ norm using frequency response and disturbance data. Unlike conventional dual-stage actuator HDD design methods, which generate piezoelectric (PZT) actuator and voice coil motor (VCM) controllers with a common denominator, the proposed method independently tunes the denominators of the PZT and VCM controllers. In addition, constraints are introduced to ensure closed-loop stability in the presence of approximation errors in the decoupling filter. The effectiveness of the proposed method is demonstrated through time-domain response simulations using a dual-stage actuator HDD benchmark problem.

In today's business environment, companies must balance cost efficiency with customer satisfaction through effective supply chain network design. The Facility Location Problem (FLP) addresses this challenge, but becomes computationally intractable for large-scale instances involving multiple product categories and heterogeneous warehouse sizes. To address these limitations, we propose a mathematical model and an efficient solution approach that substantially reduces computational time while preserving optimal facility location decisions. The proposed method integrates relaxation techniques to improve computational efficiency, jointly determining warehouse locations, capacities, and product assignments under storage constraints. Unlike classical LP-based relaxation methods and LP-guided variable selection approaches used in capacitated and step-cost FLPs, our approach exploits the structural properties of warehouse–product interactions, enabling tractable solutions without compromising modeling fidelity. A European case study demonstrates that the proposed approach significantly reduces computational time without loss of accuracy. This research provides a practical decision-support tool for enhancing supply chain resilience and efficiency.

This paper presents performance comparisons among three bearingless motor topologies of homopolar, consequent-pole, and multi-monopole bearingless motors. These topologies have a two-pole suspension winding to generate a radial suspension force. The number of poles of the suspension winding does not depend on the number of rotor poles, and the suspension force can be controlled without rotor rotational angular positions. Representative bearingless motor performances are compared using three-dimensional finite element analysis, including the drive torques, suspension forces, and force error angles. The stator has an irregular distribution of stator slots to reduce the force error angle. Additionally, the stator has a combined winding that integrates the motor and the suspension winding. The results verify that the multi-monopole bearingless motor has high torque density with good force density.

The progression of a malignant tumor in the gastrointestinal tract is usually determined by an endoscopist based on endoscopic images. In such cases, the diameter of the tumor is the most important factor in determining the degree of progression. In this paper, we introduce a new image processing technique to model tumors on the gastric surface in three dimensions from endoscopic images and to measure the diameter of the tumor from the data. Further, a new reflection model based on the two-point light source model is used. Finally, we demonstrate the effectiveness of the proposed method through experiments using endoscopic images of actual cancer patients.

Many industrial motion systems require performing a variety of tasks with high precision and safety. Iterative learning control (ILC) is a method with convergent update laws, generally classified into: 1) parametrized learning approach for achieving task-flexibility against varying tasks; or 2) signal-based learning approach which can achieve perfect tracking-performance for repeating tasks. The aim of this study is to join the distinct ILC frameworks, achieving all desirable properties in a single framework. Specifications on convergence, tracking-performance and task-flexibility of the developed joint parametrized/signal-based ILC are theoretically derived, confirmed with experimental results on a two-mass system.

A power distribution system that dynamically adjusts the distribution voltage in real time based on fluctuating physical parameters, (e.g., power, motor speed, and voltage) of the load equipment installed within consumer facilities such as factories to enhance energy efficiency is proposed in this study. The proposed system comprises a voltage-adjustment circuit and load equipment. The voltage-adjustment circuit integrates a tap-switching transformer and series-compensating inverter. The voltage-adjustment circuit controller utilizes power from the load equipment while continuously acquiring real-time operational data, including various physical parameters. Voltage-adjustment circuit optimizes energy consumption in the load equipment by sequentially regulating the effective distribution voltage. The energy-saving performance of the proposed system is experimentally validated using a mini-model with a motor load. The results confirm that the proposed flexible distribution voltage system achieves up to an 8.40% reduction in power consumption of compared to the conventional distribution voltage of 200 V/50 Hz.

In this paper, we propose a method to stabilize a teleoperation system with large communication delays using model predictive control (MPC). The proposed scheme includes compensation for the large delay by introducing the reference model driven by an operator. that estimates model as a target value. Communication delay is compensated by using the reference-model output as a target value, and the reference trajectory is generated from early-arriving data. For teleoperation systems with large communication delays, MPC can enhance the tracking performance of the reference model; however, model errors and disturbances can introduce swingback phenomenon and instability. To address this issue, we propose a swingback suppression method. This involves adding the rate of change of the reference model to the reference trajectory and including the rate of change of the augmented plant state. In the proposed method, a terminal cost is also introduced to stabilize the augmented system that includes communication delay. Simulation and experimental results demonstrate the effectiveness of the proposed method.

Magnetically levitated synchronous planar motors have gained widespread use in production environments owing to their fully non-contact multi-degree-of-freedom operation. However, achieving precise and stable motion remains challenging because of intrinsic instability, tracking delay, and cross-axis coupling. This study presents an integrated control system for a magnetically repulsive planar motor that combines feedback, feedforward, and decoupling controllers. The control design is based on system identification and modeling of the dynamics. In particular, the proposed feedforward and decoupling controllers are designed based on the identified motor dynamics, and explicitly account for the misalignment among the actuation point, the center of gravity, and the measurement point. Experiments using a one-dimensional planar motor validate the proposed integrated controllers.

In this paper, the design and analysis of the Line Start Synchronous Reluctance Motor (LS-SynRM) are presented. The proposed LS-SynRM is equipped with multilayer Nonoverlapping Winding (NW). The proposed design consists of 2x18 slots and 6 magnetic poles. A comprehensive analysis of both parallel and series winding configurations, alongside their impact on electromagnetic performance and fault-tolerant capabilities, is also presented in this paper. Furthermore, the impact of rotor cage bar design on the performance of the proposed machine is studied. Electromagnetic performance for different rotor cage designs, namely rectangular cage bars, filleted cage bars, and round cage bars, is analyzed and compared. To verify the electromagnetic performance of the designs, Finite Element Analysis (FEA) is carried out. NWLS-SynRM with round cage bars provides lower iron and rotor cage bar losses; however, the average torque is reduced. Results indicate that NWLS-SynRM with a round cage provides better overall performance in terms of loss minimization, while the integration of multilayer NW topology in a 2x18 slot/6-pole configuration enhances fault tolerance in parallel windings and reduces MMF harmonics compared to those in single-layer designs.

Pneumatic servo valves are essential for regulating flow rates in a wide range of industrial applications. This paper presents a parameterized feedforward control strategy designed to actively suppress pipe resonance, enabling high-speed and high-precision gas flow control. In the developed approach, feedforward parameters representing valve body dynamics and pipe resonance are identified using basis function iterative learning control (BF-ILC) and frequency-domain ILC (FD-ILC), respectively. This physics-based, stepwise learning scheme allows independent identification of the two components, minimizing parameter interference during BF-ILC. Experimental results demonstrate that the method reduces the settling time to 7 ms, over 80% faster than conventional feedforward control based on the inverse model. Furthermore, unlike FD-ILC, the proposed method maintains superior control performance across different flow rate trajectories while offering high interpretability. These results confirm that the method enables faster, more accurate, and task-flexible flow rate control in pneumatic valves.

With the growing demand for higher data storage capacities in hard disk drives (HDDs), achieving precise and reliable positioning of the magnetic head has become increasingly critical. To address this challenge, this paper proposes a control system design methodology tailored to the quadruple-stage actuator (QSA) system, a promising candidate for next-generation HDD architectures. The proposed framework leverages Bode plot analysis to incorporate stroke constraints of thermal actuators—the smallest elements in the QSA system—directly into the controller design. These constraints are visualized as forbidden regions on the Bode diagram, enabling intuitive evaluation of controller feasibility. Specifically, inequality conditions representing the physical stroke limitations are formulated and embedded into the design process. Validation was performed through hybrid simulations that integrated empirical measurement data into mathematical modeling. The results confirm that the enhanced controllers satisfy the stroke constraints while preserving high positioning accuracy and overall system responsiveness.

Flying start refers to the resumption of motor current control by reapplying power to an inverter when the motor continues to rotate at a nonzero speed after a power interruption. This issue is particularly critical for permanent magnet synchronous motors operating under position-sensorless control, where large initial position estimiation errors can induce electromotive-force-driven inrush currents and trigger overcurrent protection. Previous studies have shown that existing flying-start algorithms often require extended settling times and mode switching to address this problem. In this paper, we propose a seamless flying start algorithm that operates entirely within position-sensorless field-oriented control, eliminating the need for mode switching and significantly reducing the settling time. The proposed method exploits the unique initial position estimate provided by a model-based position estimator formulated in the stationary reference frame under specific inverter voltage conditions. Experimental results validate the effectiveness of the proposed algorithm.

Solar photovoltaic (PV) technology is a promising renewable energy source; however, its efficiency considerably reduces dust accumulation, particularly in arid or polluted environments. Traditional water-based or manual cleaning methods are inefficient, costly, and unsustainable. This study proposes an automated dry-cleaning system that integrates machine learning and image processing for real-time dust detection and targeted cleaning. The system employs Canny edge detection and YOLOv5 to isolate PV panel regions and classify dust severity, labeling panels with ≥30% surface coverage as “dusty.” Arduino Mega and Jetson Nano jointly control a brush-based mechanism driven by dual DC gear motors, optimized for vertical (400 rpm) and horizontal (200 rpm) cleaning. Experimental evaluation on 15-W PV modules demonstrated that the system improved module efficiency from 77.1% under dusty conditions to 96.0% after cleaning, corresponding to an average 39% increase in output power. The dust detection algorithm achieved 82% classification accuracy, and the optimal cleaning performance was obtained with two sweeps (≈1 min 59 s), beyond which additional passes yielded negligible gains. By integrating accurate detection, optimized automation, and sustainable dry-cleaning, the proposed framework provides a scalable theresource-efficient solution for maintaining long-term PV performance.

This paper presents a comprehensive survey of driving force control (DFC), recognized as one of the most effective traction control strategies for electric vehicles (EVs). The fundamental philosophy underlying the genesis of DFC is introduced, highlighting its advantages within the broader family of traction control approaches. Owing to its merits, DFC has evolved along various research directions, including integration with road condition estimation, coordination with upper-layer motion controllers, system stability analysis, and adaptation to different types of EVs. In light of the imminent advancement toward an e-mobility society, the emerging trends in DFC research, leading to new motion control challenges, are also discussed. The survey is supported by experimental results obtained from real vehicle tests conducted by the authors' research group.

This paper presents a structured review of interaction force control in robotics, approached from the perspective of mechanical impedance and actuator characteristics. We classify actuators into three types, i.e., Type I: rigid actuators with direct force sensing, Type II: compliant actuators with elastic elements, and Type III: low-impedance actuators with model-based estimation. We further analyze how their inherent mechanical impedance constrains achievable performance. For each type, advanced observer-based control methodologies are introduced to mitigate limitations such as sensor drift, backlash, and model dependence. These include the Reduced-Order Multisensor-Based Force Observer (RMFOB) for drift compensation, the Transmission Force Observer (TFOB) for backlash-robust SEA torque estimation, and Frequency-Shaped Impedance Control (FSIC) for friction-robust model-based actuation. Experimental results across different actuator types verify improvements in transparency, stability, and impedance fidelity. In this review, we systematically link actuator properties with control strategies to provide a unified framework that bridges hardware constraints and control design. In the end, this offers practical guidance for next-generation applications in physical Human-Robot Interaction (pHRI), rehabilitation, and dynamic locomotion.

A tap-switching transformer can serve as an effective voltage regulation circuit when used as a voltage compensator. However, it generates inrush currents because of the step changes in load voltage during tap switching. In this study, we propose an AC voltage regulation circuit that employs a series-compensating inverter to solve the tap-switching transformer problem. We connect a series-compensating inverter to supply high-quality power to the load. However, the load voltage changes in a step-wise manner during tap switching, causing an inrush current. Therefore, we propose an inrush current suppression method for the proposed circuit. The experimental results demonstrate that the tap-switching coordination control reduces the inrush current by 53.4% when compared with the case of the tap-switching transformer alone.

Kinematic redundancy and actuation redundancy contribute to the increased functionality and reliability of multi-degree-of-freedom (DOF) robot manipulators. The former refers to having more kinematic DOF than required for a given task, whereas the latter indicates having more actuators than required by the kinematic DOF of the mechanism. Combining these types of redundancy introduces rich functionality into mechanical systems; however, integrated control methods have not been sufficiently studied. This paper addresses solving these redundancies and proposes a control method for mechanical systems with both types of redundancy. Modal system design based on the statics of robot manipulators was adopted to solve actuation redundancy, and kinematic redundancy was treated separately. The proposed methodology was validated through experiments using a planar 3-DOF parallel robot with four actuators under hybrid position/force control.

This paper presents a novel control strategy for matrix converters in wireless power transfer (WPT) systems. Matrix converters can generate high-frequency AC from commercial AC in a single stage and are suitable for the primary power converter in WPT systems. The matrix converter-based WPT circuit topology offers a compact size and long lifetime, generally based on a pulse width modulation (PWM) strategy. The PWM strategy achieves power transmission by adjusting the pulse width of high-frequency voltage; it is easy to implement and enables high-quality grid currents. In this paper, a control strategy that integrates PWM and pulse density modulation (PDM) is proposed for more efficient power transmission. The proposed strategy, which achieves power transmission by adjusting the pulse width and pulse density, is expected to suppress switching losses by reducing the switching frequency. The pulse pattern is determined based on Δ-Σ modulation, which enables continuous power control and suppression of power fluctuations. This strategy can achieve high system efficiency and high-quality grid currents. Experiments conducted using a 2.5 kW prototype system demonstrated the effectiveness of the proposed strategy.

In this paper, we propose a novel controller design approach for a planar robot arm. The objective is to develop an adaptive robust control (ADRC) algorithm that enhances the trajectory-tracking performance of an Euler–Lagrange dynamic system operating under uncertain conditions. In the ADRC design, the dynamic system is partitioned into two components: one with fixed but unknown parameters and another with parameters that are bounded within known limits. To mitigate performance degradation caused by time-varying parameters, a sliding-mode control element is incorporated into the adaptive control framework. This hybrid design, referred to as the ADRC, leverages the robustness of sliding-mode control and the adaptability of conventional adaptive control. The stability of the closed-loop system is established using Lyapunov's direct method. The proposed ADRC is validated through computational studies of trajectory tracking for a planar robot arm. Comparative analyses with traditional adaptive control and conventional sliding-mode control demonstrate that the ADRC achieves superior tracking accuracy and robustness while maintaining comparable control input effort. These results confirm the effectiveness of the ADRC in addressing uncertainties and parameter variations, thereby offering practical insights for advanced motion control in robotic systems.

Next-generation high-precision mechatronic systems require safe and precise control of unmeasurable states. Statetracking iterative learning control (ILC) can achieve extremely high state-tracking performance up to the performance of state estimation, with convergence guaranteed apriori through the frequency-domain characteristics of the state estimator. The aim of this study is to develop a noncausal state estimation framework with verifable frequency-domain characteristics. In batch-operated systems such as ILC, the use of noncausal design leads to substantial performance improvements that surpass the fundamental limits of causal approaches. Furthermore, by analytically verifying the frequency-domain characteristics of the noncausal state estimator, the developed framework retains the beneft of guaranteeing convergence in ILC. The developed framework is validated both by simulation and experiment, confrming improved state-tracking with monotonic convergence of ILC, achieved by exploiting noncausality in state estimation.

The automation of construction machinery, such as bulldozers, has gained significant attention in recent years. Effective automation requires accurate modeling of soil behavior and plan path planning. Conventional approaches rely on physics-based simulators, which demand significant expertise in soil mechanics and substantial computational resources for their design and operation. To overcome these limitations, we propose a data-driven framework that models soil dynamics directly from real-world observations, eliminating the need for complex simulator design. We developed a robotic testbed to replicate soil spreading work, collected soil behavior data, and introduced an efficient learning method based on localized prediction and data augmentation. Using this approach, we trained a neural network capable of accurately predicting post-push soil profiles. Our data-driven dynamics model is approximately 15.5 times faster than conventional simulators. Additionally, we trained a path planning network within the learned environment and validated its performance on the physical testbed, achieving a high fill rate of 90.8%. This work demonstrates a practical and efficient alternative to traditional simulation, highlighting the potential of real-world modeling for construction automation. A preliminary version of this paper was presented at SAMCON 2025⁽¹⁾, where it received the Outstanding Paper Award.

Condition-based maintenance (CBM) is an effective approach for ensuring the safe operation of power electronic converters used in industrial applications, grid-tied ones, and transportation systems. This paper proposes a condition-monitoring method of a DC-link capacitor used in a propulsion inverter for a rolling stock, where capacitance is used as the indicator of its degradation. This method utilizes the instantaneous output power of the inverter to reproduce the ripple current of the dc-link capacitor, which allows current-sensor-less condition monitoring of the dc-link capacitor. In addition, the capacitor voltage is obtained from a sampled one synchronized to a multiple of the carrier frequency available in a practical propulsion inverter. A 200-V 1.5-kWlaboratory system with an induction motor is designed and constructed for emulating the propulsion inverter with synchronous pulse-width modulation in a region with constant output power. Experimental results confirmed that the capacitance of the dc-link capacitor was monitored from the sampled voltage and reproduced ripple current with the proposed method.

Downsizing and weight reduction of motors are critical in aircraft applications. This paper investigates the effect of the lengthto-diameter ratio (L/D) on the torque density of an axial wound field flux switching motor (AWFFSM) with segmental rotors. The torque density is analyzed for L/D values ranging from 0.106 to 0.312, while maintaining a constant motor volume. The results show that the motor achieves the highest torque-to-volume density at L/D = 0.156 and the highest torque-to-weight density at L/D= 0.127. Furthermore, the motor weight decreases as the design becomes flatter. These findings provide useful insights for guiding the downsizing and weight optimization of AWFFSMs.

Advances in semiconductor technology have dramatically improved the computing performance of controllers in embedded control system, making it possible to realize fast and precise control systems. In this study, 10 MHz multi-sampling deadbeat (MSDB) control was applied to a permanent magnet synchronous linear motor (PMLSM) drive system with a 20 kHz carrier frequency inverter using an FPGA controller. Simultaneously, a high-speed encoder detection system with T-methods based on 100 MHz DIO was implemented. The control characteristics of the proposed method and the conventional motion control method were evaluated and compared through simulations and experiments. As a result, it was confirmed that the proposed method achieves fast control response and robust characteristics compared to a conventional PI control and single-rate deadbeat (SRDB) control. Threfore, in this study we demonstrate the effectiveness of the proposed next-generation motion control method, which can achieve both high control response and robustness.

Measurement of bi-articular muscle strength related to human limb motor control is required for clinical practice and sports training evaluation. The muscle strength measurement method that utilizes functionally different effective muscle theory employs a two-dimensional musculoskeletal model of six muscles for two joints, wherein limb muscles are classified according to their functions and used to measure the muscle strength for each muscle group. In this study, we evaluate the muscle strength measurement system (MSMS) of each muscle group in a five-muscle model including a bi-articular muscle of the knee-ankle joints in ten young adult males. Instructions are provided to participants, and measurements are conducted while checking the results displayed on the monitor. The knee extension torque of MSMS was comparable to that of the conventional joint torque measurement systems. In addition, we propose two new methods for drawing output force distributions (OFD). Five muscle group torques obtained by the two methods are evaluated by the physiological cross-sectional area of the ankle plantar flexors. Reconsidering the measurement instructions given to participants for the two sides of the OFD related to the bi-articular muscle and the OFD drawing methods is necessary.