. Disturbance observer-based control has been recognized as one of the most promising approaches for disturbance attenuation, and many papers on design methods of disturbance observers have been published. Recently, the parameterization of all disturbance observers for plants with any disturbances has been clarified. However, these methods generally require the availability of the control input. Since there are systems which the control input of system is not unavailable, it is essential to design unknown input disturbance observers. In this paper, we clarify the parameterization of all unknown input disturbance observers for plants with general exogenous output disturbances. Furthermore, we present a design method applicable to such observers.

The retirement of lithium-ion batteries from electric vehicles (EVs) has created a significant supply of second-life batteries (SLBs) suitable for stationary energy storage. However, the effective utilization of SLBs is constrained by their heterogeneous degradation states, specifically nonlinear capacity fade and significant increase in internal resistance (R0). Traditional State of Charge (SOC) estimation methods often fail when parameters drift significantly from nominal values. This paper proposes a robust Adaptive Sliding Mode Observer (ASMO) designed explicitly for SLBs. The system integrates a dual-loop structure comprising a Sliding Mode Observer to robustly estimate SOC against modeling uncertainties and a Low Pass Filter (LPF) based adaptive mechanism to identify time-varying internal resistance in real-time. Stability analysis based on Lyapunov theory proves the convergence of the proposed observer. Simulation results demonstrate that the ASMO achieves a Root Mean Square Error (RMSE) of 1.523% under dynamic loading, even with an initial SOC error of 30% and severe resistance degradation.

Traffic at urban intersections frequently encounters unexpected obstructions, resulting in congestion due to uncooperative and priority-based driving behavior. This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles (CAVs) at single-lane intersections, particularly in the context of left-hand side driving on roads. The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks. We consider that all approaching vehicles share relevant information through vehicular communications. The Intersection Coordination Unit (ICU) processes this information and communicates the optimal crossing or turning times to the vehicles. The primary objective of this coordination is to minimize overall traffic delays, which also helps improve the fuel consumption of vehicles. By considering information from upcoming vehicles at the intersection, the coordination system solves an optimization problem to determine the best timing for executing right turns, ultimately minimizing the total delay for all vehicles. The proposed coordination system is evaluated at a typical urban intersection, and its performance is compared to traditional traffic systems. Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.

Accurate recognition of human emotions is essential in many fields such as education, healthcare, and entertainment, and is particularly valuable for improving the adaptability of brain-computer interface (BCI) systems in human-computer interactions. Therefore, in this study, we propose a hybrid deep learning approach that combines a one-dimensional convolutional neural network (1D-CNN) with a Transformer encoder, referred to as the 1D-CNN-Transformer, for emotion classification based on electroencephalogram (EEG) signals using two subsets of selected channels. RF-RFE is employed to select the most informative EEG channels, followed by the extraction of time-domain and frequency-domain features across four frequency bands decomposed by the discrete wavelet transform (DWT). These features are evaluated using three models including 1D-CNN, Multilayer perceptron, and the proposed 1D-CNN-Transformer, with subject-wise 5-fold cross-validation on the validation dataset. Among the models, the hybrid 1D-CNN-transformer model achieves the best results, with $\mathbf{7 0. 0 {\%}}$ accuracy, $\mathbf{7 2. 9 {\%}}$ precision, 82.7% recall, and a 76.7% F1-score for valence, and 67.5% accuracy, 68.89% precision, 82.71% recall, and a 73.9% F1-score for arousal.

This study presents a complete modeling and control framework for a half-quadrotor experimental platform, specifically designed for yaw-axis research, and uses the Quanser Aero system. This hardware setup isolates yaw rotation by mechanically locking the pitch axis and aligning both rotors horizontally. Using a free-body diagram, a linear model can be created, which then provides both transfer function and statespace representations. Building upon classical Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) formulations, the work develops and integrates two advanced nonlinear control strategies: Nonlinear Dynamic Inversion(NLDI) and a Disturbance Observer (DoB) for active disturbance estimation. This integration results in a robust, high-bandwidth controller capable of significant disturbance rejection, appropriate for evaluating quadrotor yaw dynamics under uncertainties. This test-bed platform provides a stable and controlled environment for testing advanced control algorithms as a starting point for deploying them on full-scale quadrotor vehicles.

. This paper presents an advanced biomechanical model with eight degrees of freedom (8-DOF) to rigorously characterize the dynamic interactions between wheelchair occupants and seating systems. In contrast to traditional single- or two degree of freedom (SDOF/TDOF) approaches that oversimplify occupant-seat coupling, the proposed 8DOF framework explicitly resolves the complex mechanical pathways governing vibration transmission through anatomical and structural subsystems. By incorporating precisely calibrated stiffness and damping parameters derived from seven commercial wheelchair cushions, the model achieves high fidelity in predicting seat-to-occupant transmissibility across the critical 0-20 [Hz] frequency range. Experimental validation using real-world chanical responses under realistic excitation. Notably, cushions with low stiffness and low damping produced amplified transmissibility peaks near 3-4 [Hz] - a range corresponding to human vertical resonance - while designs with optimized damping-stiffness combinations effectively mitigated these vibrations. These findings emphasize the pivotal role of energy dissipation in preventing resonance amplification and promoting long-term user comfort. The validated 8-DOF framework thus offers mechanical, computational, and translational advantages, serving as a robust tool for designing next-generation seating systems that balance biomechanical protection with engineering performance.

ABSTRACT Unregulated parking search is a major source of urban inefficiency, leading to unnecessary cruising, wasted time, driver frustration and increased emissions. Moreover, when drivers select spots solely based on immediate self‐interest, ignoring others' dynamic needs and preferences, this behaviour exacerbates congestion, conflict and overall discomfort. To address these challenges and advance towards a smart, sustainable and user‐centric mobility system, this paper proposes an Optimal Parking Allocation Scheme (OPAS). By intelligently integrating user behaviour and real‐time demand, OPAS dynamically assigns parking spaces that enhance overall system efficiency while flexibly providing each user with an optimal spot, balancing collective sustainability with individual comfort. The proposed OPAS wirelessly receives the user information upon arrival at the entrance and determines the best‐suited spot by solving an adaptive optimisation problem considering the travel and parking behaviour in terms of the number of passengers in the car, their walking and driving distances, the priority level of users, in addition to trends of the occupation ratio. The proposed OPAS dynamically tunes its objective parameters to align with overarching system goals, yielding successive, situation‐adaptive decisions. The performance of the proposed scheme is evaluated using data from an indoor parking model of a shopping centre in Gunma City, Japan. The results demonstrate that OPAS achieves a significant reduction in both individual walking distance and vehicular travel distance within the parking facility. Consequently, this efficiency directly translates to a decrease in total fuel consumption and associated emissions. Specifically, the proposed strategy achieves a daily reduction of 23 kg in emissions and 10 L in fuel consumption, highlighting the practical significance of the proposed approach.

Terraced rapeseed fields present persistent challenges for pest control owing to complex terrain, low mechanization, and the limited computing capability of mobile spraying robots. To address these constraints, this study proposes RDD-SPA, a lightweight perception–actuation framework that integrates a symptom-oriented object detector (RDD-YOLO) with a Spraying Point Adaptor (SPA) for real-time precision spraying in terraced environments. A field dataset comprising 3309 images collected at the seedling and bud stages was constructed, containing 13,530 annotated instances of rapeseed plants, leaf holes, and leaf discoloration, and reflecting dense canopies, irregular leaf morphology, and frequent occlusions. RDD-YOLO enhances YOLO11 through the introduction of Lite-PSConv, C3k2-IDC modules, and the SIoU loss, achieving an mAP50:95 of 64.0% while reducing parameters and computational cost by 12.02% and 3.02%, respectively. After pruning and knowledge distillation, the model is compressed to 1.12 million parameters and maintains stable real-time inference at 60 FPS on the AX650N embedded platform under a practical robot speed of 0.18 m/s. Based on binocular depth estimation and geometric calibration, SPA translates detection results into adaptive spraying commands, dynamically adjusting nozzle position, angle, and spray volume. Field experiments on 40 rapeseed seedlings demonstrate that the proposed system increases leaf liquid retention by 33.3% and reduces chemical drift by 27.1% compared with manual spraying. These results indicate that RDD-SPA provides an effective and deployable solution for precision pesticide application in resource-constrained terraced agriculture.

. This paper considers a control system design for minimum-phase systems that ensures the output follows the non-periodic reference input and periodic disturbances are attenuated without repetitive control. In practical applications, a control system often has to attenuate periodic disturbances and make the output follow a non-periodic reference input. To achieve these requirements, repetitive control has been proposed. Repetitive control can attenuate periodic disturbances. However, repetitive control typically results in high-order controllers. To design a low-order controller that attenuates periodic disturbances, the control system has to be designed without using repetitive control. In this paper, the control system using a multi-period disturbance observer to attenuate periodic disturbances is proposed. The multi-period disturbance observer uses a low-order filter, and attenuate periodic disturbances using the period of disturbances. Since the multi-period disturbance observer uses a low-order filter, it becomes possible to design the low-order controller. However, there are no studies about a design method of the control system using the multi-period disturbance observer. To design a low-order controller attenuating periodic disturbances, the transfer function from the disturbance to the output must have a finite number of poles. A condition that the transfer function from the disturbance to the output has a finite number of poles is clarified. In addition, the internal stability condition of the control system using the multi-period disturbance observer that makes the number of poles finite is clarified. Based on these above conditions, a design method of the control system using the multi-period disturbance observer is also proposed.

In the emerging aging society, the expectation for sufficiently safe and reliable mobility tools, like automated wheelchairs, is growing to keep the lives of physically needed persons active and comfortable. An essential intelligence required for an automated wheelchair is interacting with pedestrians in a crowded pathway. This paper addresses this issue and proposes an optimal control approach for interactive trajectory generation based on a pedestrian behavior model in typical pedestrian interaction scenarios. Specifically, we propose a novel way to incorporate pedestrian behavior near the wheelchair using the social force model (SFM) under the predictive optimal control framework without requiring extra computational burden. Such incorporation of SFM enables the wheelchair to intelligently predict the multiple pedestrian interactions when the wheelchair emerges in their range of vision in complex environments. Finally, control decisions in the linear model predictive control framework, which considers conditional safety constraints, ensure optimal performance even in adaptive or changing pedestrian environments.

. To make control systems stable using a stable controller is called the strongly stabilization. The strongly stabilizing controller, which is a stable controller that makes the system internally stable, is often used to make the control system stable even if a sensor or actuator fails for stable plants. However, the strongly stabilizing controller has some problems: not make the output follow the step reference input or sinusoidal reference input, and not make the step disturbance or sinusoidal disturbance be attenuate. This is because the strongly stabilizing controller is not able to have poles at the origin and on the imaginary axis. Since sinusoidal inputs are often used to detect the faulty in some reliable control system design, it is important to overcome problems that the strongly stabilizing controller has. To overcome these problems, the extended strongly stabilizing controller, which has a pole at the origin and two pairs of poles on the imaginary axis, has been proposed. Using the extended strongly stabilizing controller, we can construct a reliable control system that has failure detection and follows the output to the reference input. In this paper, we propose a control system with failure detection without an error between the output and the reference input by using the extended semi-strongly stabilizing controller that has a pole at the origin and two pairs of poles on the imaginary axis.

. This paper proposes a simple design method for fault tolerance control based on fault estimator. The tolerance is formulated as a fault in one of the correlated two plants, or, in other words, the system is under an uncertain state. To estimate the system under an uncertain state, the fault estimator that detects the difference in output in both normal and post-faults is proposed. To design the fault estimator, we also clarify the parameterization of all fault estimators. Using a fault estimator, the system will be effectively recovered by compensating for lost outputs via a newly designated feedback parameter, in the case where system fault is detected. The proposed technique offers simplicity in the self-recovery of fault tolerance controls in an unstable system.

In real-world engineering environments, faults rotating machines typically occur for concise periods, which leads to poor stability and low accuracy in fault diagnosis. The traditional fault diagnosis of rotating machinery relies on analyzing time-series data to detect system degradation and faulty components. However, the complexity of rotating machinery and the presence of multiple fault types across different operating conditions challenges for conventional classification techniques. This paper proposes a LASSO regression-based feature extraction method with adaptive window based on Dynamic Time Warping (DTW) for fault diagnosis in rotating machinery. The approach effectively extract features by modeling the relationship between shaft rotational speeds (25, 50, and 75 rpm) and vibration signals from piezoelectric accelerometers. This research focus on single and combination faults analysis to include 11 faults, enhancing its applicability to real-world fault conditions. To assess its effectiveness, the proposed method is evaluated against Principal Component Analysis (PCA) and Independent Component Analysis (ICA) using the K-Nearest Neighbors (KNN) classifier. The experimental results demonstrate that the LASSO-based approach consistently achieves high classification accuracy across different speeds, outperforming PCA and ICA in both single and double fault scenarios. These findings highlight LASSO regression as a robust feature extraction technique for improving fault detection and predictive maintenance in rotating machinery.

To design the control system with low sensitivity characteristics and robust stability, a double feedback control system is proposed. The double feedback control system is a two-degree-of-freedom control system that is included in another two-degree-of-freedom control system. According to some studies, the double feedback control systems can simultaneously have low sensitivity characteristics and robust stability for a class of uncertainties. The double feedback control system also has the potential to maintain stability even if a component in the control system fails because the control system has multiple controllers. However, if a component in the double feedback control system fails, the control structure of its control system will be changed. This implies that for the failure of a component within the double feedback control system, the control system is not always stable, even if the stability condition of its double feedback control system without the failure is satisfied. Thus, it is important to obtain the stability condition of the double feedback control system in the case that a component within the double feedback control system fails. In the case that a component fails, several cases are considered. However, the stability condition of the double feedback control system in a case where a component fails is not always equal to that in another case. This paper considers a robust fault-tolerant control using a double feedback control system for Single-Output/Single-Input time-invariant minimum phase systems in the case that the output signal from a controller fails. In this paper, we clarify a robust stability condition of the double feedback control system in a case where a component failure is not always equal to that in another case. Based on the clarified robust stability condition, a design method for the double feedback control system that maintains stability in the case that the output signal from a controller fails.

. Several methods have been proposed for using color to express emotion. The most commonly used model of emotion is Plutchik's model of emotion. However, this model may not suit Japanese people. In addition, emotions other than the basic emotion have not been well studied, especially the method to express the secondary dyad. In this paper, we propose a new method that places surprise between anger and expectation, and expresses primary dyads using two basic emotion patterns. Experimental results show that the primary dyad can be expressed using two different gradation methods. Furthermore, we find that the reordering method gives better results for Japanese people. It may be possible to apply these results to finding an even better sorting order for people in other countries, or to create a model that expresses all emotions in the future.

High-precision path tracking for a Four-Mecanum-Wheel Mobile Robot (FMWMR) is challenged by real-world factors such as payload-induced shifts in the center-of-gravity (CoG) and operation on inclined surfaces. These uncertainties introduce complex, coupled dynamic forces that degrade the performance of conventional controllers. This study addresses this problem with a Model Reference Adaptive Controller (MRAC), which learns and compensates for these unpredictable dynamic effects in real time. To ensure effective operation on physical hardware, the controller incorporates practical solutions for motor friction and control signal stability. The proposed approach is validated through implementation of the MRAC on a Rosmaster X3 robot. A performance comparison is made against a well-tuned Proportional-Integral-Derivative (PID) controller is conducted across twelve distinct scenarios. The results show that the adaptive controller reduced the position Root Mean Square Error (RMSE) by an average of 52.7% and the Integral of Time-weighted Absolute Error (ITAE) by 61.5%. This work validates the MRAC as a powerful and robust solution for robots operating in unpredictable environments.