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Real-time detection of rockfall on slopes is an essential part of a smart worksite. As a result, target detection techniques for rockfall detection have been rapidly developed. However, the complex geologic environment of slopes, special climatic conditions, and human factors pose significant challenges to this research. In this paper, we propose an enhanced high-speed slope rockfall detection method based on YOLOv8n. First, the LSKAttention mechanism is added to the backbone part to improve the model's ability to balance the processing of global and local information, which enhances the model's accuracy and generalization ability. Second, in order to ensuredetection accuracy for smaller targets, an enhanced detection head is added, and other detection heads of different sizes are combined to form a multi-scale feature fusion to improve the overall detection performance. Finally, a bidirectional feature pyramid network (BiFPN) is introduced in the neck to effectively reduce the parameters and computational complexity and improve the overall performance of rockfall detection. In addition we compare the LSKAttention mechanism with other attention mechanisms to verify the effectiveness of the improvements. Compared with the baseline model, our method improves the average accuracy mAP@0.5 by 4.8%. Moreover, the amount of parameters is reduced by 20.2%. Among the different evaluation criteria, the LHB-YOLOv8 method shows obvious advantages, making it suitable for engineering applications and the practical deployment of slope rockfall detection systems. [ABSTRACT FROM AUTHOR]

Pre-class learning plays a crucial role in the teaching and learning of the flipped classroom. While it is a challenging task to stimulate students to engage in pre-class autonomous learning, especially for the learning of a foreign language that requires a long-term commitment. In this study, we dealt with the problem of how to improve and maintain students' motivation in pre-class learning of a foreign language. First, we devised a motivational model with factors that encourage student engagement. Then, we developed motivational strategies based on the model to motivate students to be highly engaged before class in this pilot study for a Chinese course for beginners at Tokyo University of Technology. Using a Moodle learning management system, we established a framework for pre-class learning materials and collected data by keeping track of pre-class learning activities and extracting student perceptions and evaluations of their pre-class learning experience. Statistical analysis of the data for three academic years shows that the motivational design used for the flipped classroom effectively enhanced students' behavioral engagement and accordingly increased academic outcomes. The practice of the flipped classroom with motivational design is of particular significance for foreign language teaching and learning. The motivational model and the instructional design for pre-class learning have the potential to be used for other foreign language courses. [ABSTRACT FROM AUTHOR]

A major concern in data-driven deep learning (DL) is how to maximize the capability of a model for limited datasets. The lack of high-performance datasets limits intelligent agriculture development. Recent studies have shown that image enhancement techniques can alleviate the limitations of datasets on model performance. Existing image enhancement algorithms mainly perform in the same category and generate highly correlated samples. Directly using authentic images to expand the dataset, the environmental noise in the image will seriously affect the model's accuracy. Hence, this paper designs an automatic leaf segmentation algorithm (AISG) based on the EISeg segmentation method, separating the leaf information with disease spot characteristics from the background noise in the picture. This algorithm enhances the network model's ability to extract disease features. In addition, the Cycle-GAN network is used for minor sample data enhancement to realize cross-category image transformation. Then, MobileNet was trained by transfer learning on an enhanced dataset. The experimental results reveal that the proposed method achieves a classification accuracy of 98.61% for the ten types of tomato diseases, surpassing the performance of other existing methods. Our method is beneficial in solving the problems of low accuracy and insufficient training data in tomato disease detection. This method can also provide a reference for the detection of other types of plant diseases. [ABSTRACT FROM AUTHOR]

Based on additive state decomposition technique, this article presents an adaptive dynamic‐surface repetitive control method for a class of uncertain nonlinear systems with mismatched disturbances and input delay. First, the original uncertain nonlinear system is decomposed into a linear time invariant (LTI) primary system responsible for the periodic signal tracing and rejection task, and a nonlinear secondary system with input time delay responsible for the robust stabilization task. A modified repetitive controller with a small correction to the delay constant is then independently designed for the LTI primary system, while an adaptive dynamic surface controller is designed for the nonlinear secondary system. Stability analysis is performed for each subsystem and the design procedure of the whole system is presented in detail. Finally, comparative simulations with other repetitive control and dynamic surface control methods show that the presented method not only relaxes the constraints on the reference inputs and exogenous disturbances, but also ensures that the system has better disturbance rejection and periodic‐signal tracking performance in both transient and steady states. As a result, this method broadens the application of both repetitive control and dynamic surface control. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) is a physiological signal directly generated by the central nervous system. Brain rhythm is closely related to a person's emotional state and is widely used for EEG emotion recognition. In previous studies, the rhythm specificity between different brain channels was seldom explored. In this paper, the rhythm specificity of brain channels is studied to improve the accuracy of EEG emotion recognition. Variational mode decomposition is used to decompose rhythm signals and enhance features, and two kinds of information entropy, i.e., differential entropy (DE) and dispersion entropy (DispEn) are extracted. The rhythm being used to get the best result of single channel emotion recognition is selected as the representative rhythm, and the remove one method is employed to obtain rhythm information entropy feature. In the experiment, the DEAP database was used for EEG emotion recognition in valence-arousal space. The results showed that the best result of rhythm DE feature classification in the valence dimension is 77.04%, and the best result of rhythm DispEn feature classification in the arousal dimension is 79.25%. [ABSTRACT FROM AUTHOR]

This paper introduces a new lower-limb rehabilitation machine that meets the rehabilitation needs of hemiplegic patients. First, a left–right independent rotary pedal mechanism was selected to facilitate rehabilitation and adapt to the user's physical condition. Then, a half model of the lower-limb rehabilitation machine is designed and manufactured with ergonomics in mind. As analytical tools, we combine non-negative matrix factorization and non-negative double singular value decomposition to calculate muscle synergy of the walking muscle surface electromyography (sEMG) signal, and use cosine similarity to evaluate the similarity between walking and pedaling activities. By comparing the results of the walking and pedaling experiments, the effectiveness of pedaling in gait rehabilitation is revealed. To further improve the similarity between walking and pedaling, double integration of the sEMG signal is introduced, and the relationship between load input and rotation angle is described for the first time using Fourier series. The results of the experiment confirmed that more than half of the 10 subjects performed pedaling exercises similar to walking using Fourier series loading compared to pedaling exercises with normal constant loading. This loading parameter may have the potential to improve rehabilitation efficiency for many subjects compared to the usual exercise. [ABSTRACT FROM AUTHOR]

Flexible-joint manipulators (FJMs) have been attracting much attention in recent decades. However, position control and vibration suppression of single-input (SI) multi-degrees-of-freedom (multi( $ {\geqslant }3 $ ⩾ 3)-DOF) FJM is still a high mountain to climb due to the characteristic that some states of such FJMs cannot be directly controlled by the SI. This paper aims to carry out this challenging research. We present a double-loop frame-based adaptive neural sliding-mode control method for an uncertain horizontal SI 3-DOF FJM, i.e. a two-link FJM with only one actuator at the first joint. Since only the actuator angle can be directly controlled, in the inner loop, we design a torque controller so that the actuator angle tracks a time-varying reference actuator angle, designed in real-time by the outer loop. In the outer loop, we consider the reference actuator angle as a virtual control signal and design it based on the dynamic coupling relationships, aiming to indirectly control the first link to its target position and suppress the vibration of both links. Furthermore, to enhance the transient system performance, we plan the input signal of the outer loop (i.e. the target angle) as a smooth reference-trajectory signal. The validity and superiority of this method are demonstrated in simulations. [ABSTRACT FROM AUTHOR]

This paper devises a new method for estimating the maximum response and maximum control force for high-rise base-isolated buildings with active structural control (active base isolation) to simplify the conventional complex design procedure. While active base isolation has emerged as a prominent solution for achieving high control performance, its design process is inherently complex, particularly when applied to high-rise buildings where wind loads become prominent. To address this problem, we propose a streamlined method inspired by the gust factor methodology widely used in conventional passive wind-resistant designs. This method estimates the maximum response and maximum control force without the need for numerical simulations. We first construct an equivalent passive model of a multi-degree-of-freedom control system to theoretically compute the dynamics of the system. Based on the constructed equivalent passive model and then propose a method to calculate the mean displacement and mean control force using only the static equilibrium of this model. Furthermore, we extend the conventional gust factor approach to active base isolation to estimate the maximum displacement and maximum control force for active base isolation without the need for numerical simulations. We validate our methods through a series of numerical examples, incorporating key parameters such as feedback gain, aspect ratio of building, return period of wind force, and stiffness of isolation. Numerical verifications show that the mean response and mean control force are estimated by the static equilibrium of the proposed equivalent passive model. Moreover, the maximum response and maximum control force can be estimated by the proposed gust factors. Our methods can be applied for feedback control systems using a given feedback gain. [ABSTRACT FROM AUTHOR]

Rehabilitation evaluation is an important part of rehabilitation training. It is closely related to the robot-assisted training effect. Different rehabilitation robots need different methods to evaluate patients. Rehabilitation training is a long process, and the patient's performance scores will continue to change. A lower-limb rehabilitation robot needs a dynamic performance score to evaluate rehabilitation's effects. This study used an analytic hierarchy process and fuzzy comprehensive evaluation methods to establish a rehabilitation evaluation system for lower-limb rehabilitation robots. A multi-scale personalized rehabilitation plan is conceived, based on the evaluation system and the combination of objective factors. This method dynamically adjusts the plan according to the rehabilitation situation of patients, which is beneficial to the improvement of the efficiency and initiative of training. [ABSTRACT FROM AUTHOR]

Tremors are a symptom of several disorders of the central and peripheral nervous systems, such as Parkinson's disease (PD). Exoskeletons have been investigated as noninvasive tremor suppression alternatives to medication and surgery. The challenge in musculoskeletal tremor suppression is attenuation of tremor motion without impeding the patient's voluntary motion. Linear low-pass filters (LPFs) are commonly used for tremor removal. This study presents an alternative method based on an adaptive filter. We compare the effectiveness of the LPFs and adaptive filter on the recorded acceleration signals from an online database. [ABSTRACT FROM AUTHOR]

Liquid crystal elastomer-based actuators (LCEAs) exhibit large, reversible deformation, which demonstrates superior advantages in developing remote controlled, light-weighted soft robots. However, deformation of LCEAs undergoes complicated physical dynamics and exhibits highly nonlinear hysteretic characteristics, posing challenges in the realization of related applications. It is necessary to develop a dynamic model that captures the characteristics of LCEAs. With this purpose, this paper proposes a dynamic modelling approach for LCEAs to provide a hybrid model for LCEAs with reduced computational complexity and high precision. In the research, a modelling scheme based on a physics-phenomenon-combined approach for LCEAs is proposed, which considers the elastic free energy, the nematic free energy and dissipation of the LCEA. Physics-based models are then developed to describe these energies. Meanwhile, a phenomenon-based model is proposed to characterise the hysteresis as a component of the nematic free energy. The above models are combined together to provide the final hybrid model for the LCEA. To verify the proposed model, a photo-responsive LCEA experimental platform is established and experiments are conducted. Model parameters are identified based on the nonlinear least-squares method. The experimental results demonstrate the proposed model as an excellent representative to characterise LCEAs' dynamic behaviours. [ABSTRACT FROM AUTHOR]

Liquid crystal elastomer is a type of soft material with unique physical and chemical properties that offer a variety of possibilities in the growing field of soft robot actuators. This type of material is able to exhibit large, revertible deformation under various external stimuli, including heat, electric or magnetic fields, light, etc., which may lead to a wide range of different applications such as bio-sensors, artificial muscles, optical devices, solar cell plants, etc. With these possibilities, it is important to establish modeling and control strategies for liquid crystal elastomer-based actuators, to obtain the accurate prediction and description of its physical dynamics. However, so far, existing studies on this type of the actuators mainly focus on material properties and fabrication, the state of art on the modeling and control of such actuators is still preliminary. To gain a better understanding on current studies of the topic from the control perspective, this review provides a brief collection on recent studies on the modeling and control of the liquid crystal elastomer-based soft robot actuator. The review will introduce the deformation mechanism of the actuator, as well as basic concepts. Existing studies on the modeling and control for the liquid crystal elastomer-based actuator will be organized and introduced to provide an overview in this field as well as future insights. [ABSTRACT FROM AUTHOR]

This article presents an improved equivalent-input-disturbance (EID) approach to actively rejecting exogenous disturbances for a plant. A control system based on the approach includes a new EID estimator constructed by embedding integrals to the conventional EID estimator. These integrals enable the estimator to improve the disturbance-estimation precision without amplifying the effect of measurement noise. Stability conditions of the control system are obtained using separation theorem. The system design ensures that the control system satisfies the stability conditions. Experiments of a rotational control system demonstrate the validity of the approach. [ABSTRACT FROM AUTHOR]

The disturbance rejection problem of T–S fuzzy systems is concerned. Since the T–S fuzzy system is characterised by its membership function, less conservative stabilisation conditions can be derived from membership function-dependent Lyapunov function which contributes to the improvement of disturbance rejection performance. Specifically, we utilise a configuration composed of a membership function-dependent state observer for the state estimation, a membership function-dependent equivalent-input-disturbance estimator for the estimation and compensation of disturbance and an internal model for the reference tracking. It is revealed that this membership function-dependent Lyapunov function naturally leads to control gains switching in accordance with the derivative signs of the normalised premise variables. The switching rules and the design conditions for all control gains are obtained explicitly. In particular, the free-weighting-matrix approach is used to lessen the conservatism in the stability condition. Moreover, a concrete procedure for the controller design including the switching rule is given. Finally, the developed method is tested via simulations. The advantage of the membership function-dependent equivalent-input-disturbance method is validated by comparing with conventional methods. [ABSTRACT FROM AUTHOR]

This article develops an improved equivalent-input-disturbance (EID) approach that rejects disturbances for a plant. A control system based on the approach includes an improved EID estimator, which is newly devised using a $\lbrace 1\rbrace$ inverse of the plant input matrix. Compared with a conventional EID estimator, the improved EID estimator has an additional free matrix. This free matrix enables the control system to improve the disturbance rejection performance but not increase the configuration complexity. An analysis of the control system explains how the free matrix improves the disturbance rejection performance. The experiments of a posture control system for a Stewart platform demonstrate the validity of the approach. [ABSTRACT FROM AUTHOR]

This paper presents a new non-contact alternating current impedance instrument (NCACII) with multiple frequencies to measure the electrical impedance during the hydration of cement-based materials. It implements two measurement techniques: the first technique is a frequency conversion measurement with a frequency variation range of 1–100 kHz. The second is to measure the variation in impedance with time. It provides a new way to analyze the pore structure of cement-based materials. In this instrument, we design a current sensor made from nanocrystalline T37. The experimental results demonstrate the detection accuracy of NCACII has been improved and indicates the superiority of nanocrystalline T37. [ABSTRACT FROM AUTHOR]

This article presents a new method that employs the active disturbance rejection control (ADRC) paradigm to handle the control problem for a class of nonlinear systems with unknown disturbances and measurement noise. An improved extended state observer (ESO) is devised to separate disturbance estimation from state reconstruction and a new parameter is used to adjust disturbance-rejection performance independently. This increases the flexibility of system design and makes the gain of the improved ESO (IESO) small compared to that of a conventional ESO in the ADRC. A low-pass filter is added to the IESO. This changes the slope of the magnitude curve of the Bode diagram from $-20$ dB/dec to $-40$ dB/dec at high frequencies for measurement noise, which improves the noise-suppression performance. Comparison with a conventional ESO reveals the superiority of the IESO in disturbance rejection and noise suppression. The presented method is validated by simulation and experimental results. [ABSTRACT FROM AUTHOR]

This paper presents a generalized-extended-state-observer (GESO)-based sliding-mode control (SMC) method to deal with mismatched parameter uncertainty and reference-input mutation for a class of DC-DC buck converter systems (BCS). First, a GESO is designed to estimate the total disturbance together with the system state. Then, by choosing an appropriate disturbance-compensation gain, a composite SMC law is designed to attenuate the influence of the parameter uncertainty and reference input mutation on the system output. Both the stability criterion and deign procedure of the system are given. Finally, simulation results show that the designed GESO-based SMC system for the DC-DC BCS is robustly stable and achieves both satisfactory transient and steady-state performance. Comparisons demonstrate that the proposed method provides better transient and steady-state performance for both disturbance rejection and tracking control than either conventional SMC or ESO-based control approach does. [ABSTRACT FROM AUTHOR]

The use of surface electromyography (sEMG) signals gains importance in rehabilitation and sports science because it provides a noninvasive and convenient way to analyze the activities of muscles. Since sEMG signals are weak, nonstationary electrical signals mixed with baseline noise, motion artifacts, power line interference, and many other types of noise, these signals need to be denoised for the extraction of useful information. This paper presents a method of denoising sEMG signals based on the combination of complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and interval total variation (ITV). First, we devised the CEEMDAN method to decompose an sEMG signal into several intrinsic mode functions (IMFs). Next, we categorized the IMFs into signal-dominant and noise-dominant IMFs according to energy entropy and frequency-domain transform. Then, we separately denoised these signal-dominant IMFs using the ITV method. Finally, we reconstructed the denoised signal-dominant IMFs to obtain a denoised sEMG signal. Extensive comparisons conducted on both synthetic noisy and real sEMG signals demonstrate the effectiveness of the CEEMDAN-ITV method. [ABSTRACT FROM AUTHOR]

Surface electromyography (sEMG) signals play an essential role in disease diagnosis and rehabilitation. This study applied a powerful machine learning algorithm called extreme gradient boosting (XGBoost) to classify sEMG signals acquired from muscles around the knee for distinguishing patients with knee osteoarthritis (KOA) from healthy subjects. First, to improve data quality, we preprocessed the data via interpolation and normalization. Next, to ensure the description integrity of model input, we extracted nine time-domain features based on the statistical characteristics of sEMG signals over time. Finally, we classified the samples using XGBoost and cross-validation (CV) and compared the results to those produced by the support vector machine (SVM) and the deep neural network (DNN). Experimental results illustrate that the presented method effectively improves classification performance. Moreover, compared with the SVM and the DNN, XGBoost has higher accuracy and better classification performance, which indicates its advantages in the classification of patients with KOA based on sEMG signals. [ABSTRACT FROM AUTHOR]

This article presents an improved equivalent-input-disturbance (EID) approach to handle unknown disturbances and plant uncertainties. The disturbances and uncertainties are treated as a lumped disturbance in an EID-based control system. The effect of the lumped disturbance is compensated by an EID estimator. A constraint between design parameters and uncertainties is imposed on the design of the estimator. In addition, there are insufficient analyses of the influence of uncertainties on the control performance and the stability of the system. A new filter is devised for an improved EID estimator in this article to remove the constraint. This ensures that the sensitivity of the system to disturbances at low frequencies can be freely decreased. An analysis of the system reveals that uncertainties not only influence disturbance-rejection and reference-tracking performance but also affect system stability. A sufficient stability criterion is derived with consideration of uncertainties. The validity of the presented method is demonstrated by simulation and experimental results. [ABSTRACT FROM AUTHOR]

This paper presents an improved equivalent-input-disturbance (EID) approach to deal with periodic disturbances. The approach has two degrees of freedom. One is an improved EID compensator, in which a repetitive controller is inserted in this study. The other is a conventional servo system for a reference input. The improved EID compensator estimates and compensates for periodic disturbances without steady-state error, and the servo system ensures a satisfactory tracking performance. The improved EID compensator is designed using the linear-matrix-inequality (LMI) method. Three parameters in an LMI are selected using the particle-swarm-optimization (PSO) algorithm. The state-feedback gain of the conventional servo system is designed using the linear-quadratic-regulator (LQR) method. Simulation results of a rotational control system demonstrate the validity of the approach and its advantage over others. [ABSTRACT FROM AUTHOR]

Disturbance rejection and time-delay compensation are important for control systems in practice. This article presents an observer-and-predictor-based method to reject an unknown exogenous disturbance for an input-delay system in which only delayed output is available. First, the equivalent-input-disturbance method is employed to reconstruct the system state and to estimate the disturbance. A prediction of the disturbance is calculated to reduce the effect of a phase lag caused by the delays. A new predictive scheme is designed for feedback control. Next, the system is divided into two subsystems for analysis and design: one for reference tracking and the other for disturbance rejection. Sufficient conditions are derived to guarantee the system’s stability. An analysis of the method reveals that the disturbance rejection can be viewed as a patch to the predictor-based feedback control. Finally, two case studies are used to show the validity and superiority of the method. [ABSTRACT FROM AUTHOR]

This paper presents a method that uses a web questionnaire to create a corpus containing spontaneous utterances of natural ideas, which may contain grammatical mistakes. In an experimental implementation of the method, the subjects were informed that they were receiving nursing care from a person, and they were required to answer a web-based questionnaire in which their responses were recorded as speech utterances. Compared to the Wizard of Oz approach and interview-based corpus-creation methods, the presented method simplifies the collection of utterances. Furthermore, we conducted a two-fold assessment to verify the effectiveness of the presented method. First, the approach exhibited a significant reduction in workload compared to interview-style utterance collection. Second, we compared the variety of expressions collected when subjects were informed that they were talking to a person with those collected when they were informed that they were communicating with a nursing robot. The results indicate that, although the number of utterances was larger for a robot than for a person, in terms of other metrics such as time efficiency index, the total number of morphemes, the average number of morphemes per utterance, the number of unique morphemes, and coefficient of variation, the utterances were larger for a human speech target than for a robot. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive compensation control strategy for packet losses, time delays, and exogenous disturbances in a networked control system. The structure consists of five parts: a plant, a Luenberger observer, an equivalent-input-disturbance (EID) estimator, an adaptive model predictive controller (AMPC), and a network. The AMPC in the local main control room produces an adaptive tracking gain, which can ensure the effective tracking of the reference signal in the presence of uncertainty and time delays in the plant. The EID estimator at the local site compensates for packet losses and exogenous disturbances through an independently designed state observer and a low-pass filter. A practical application case results show the effectiveness of the presented method compared with the conventional EID approach. [ABSTRACT FROM AUTHOR]

The active structural control (ASC) has been applied to base-isolated buildings to achieve a high-damping system. The critical step for designing an ASC system is selecting control parameters and isolation parameters that satisfy the design restrictions. However, the conventional methods are limited in theoretically estimating the maximum control force, which requires great demand for trial-and-error approaches and numerical simulations. This paper constructed the equivalent model of the feedback control system that theoretically expresses the dependence of vibration characteristics (natural period and damping ratio) of the control system on the feedback gain. Then, the control-force spectrum is proposed that estimates the maximum control force for a feedback control system, adjusting both the natural period and damping ratio of the control system. The maximum responses and control force are estimated without additional numerical simulations and trial-and-error approaches using the equivalent model and control-force spectrum. Moreover, a design method was devised for determining the allowance range of the vibration characteristics of structures (damping ratio and natural period) and controllers that satisfy the design limitations (maximum responses and maximum control force). The design method does not require trial-and-error and numerical simulations, thus simplifying the design procedure. Finally, this paper uses numerical examples and a design example to verify the validity of the control-force spectrum and design method. [ABSTRACT FROM AUTHOR]

A demagnetization fault not only degrades the performance of a permanent magnet synchronous motor (PMSM) drive system, but also may even cause the irreversible demagnetization. This article presents an improved-sliding-mode-observer (ISMO) based equivalent-input-disturbance (EID) method to reconstruct a demagnetization fault and to perform fault-tolerant control for the demagnetization fault in a PMSM drive system. The models of healthy and demagnetized PMSM are first established. Next, an ISMO is constructed by inserting a decoupling term. It completely removes the influence of the motor speed on the reconstruction of a demagnetization fault. Then, the ISMO-based EID approach is developed to reconstruct the fault with the high precision and maintain system control performance even for a demagnetization fault. The stability of the whole system is proved by using the concept of globally uniformly ultimately bounded. Finally, the validity of the method is demonstrated using hardware-in-the-loop simulation. [ABSTRACT FROM AUTHOR]

This article is concerned with the trajectory optimization problem of a drilling process, which is of significance to drilling efficiency and safety. Due to the difference between an actual trajectory and a planned trajectory, this problem is a multiobjective optimization problem (MOP) with parameter uncertainties. To solve the problem, this study devises a new approach named nondominated sorting genetic Algorithm II using outlier removal (OR-NSGA-II). First, the optimization problem with parameter uncertainties is formulated, including two objective functions: 1) a trajectory length and 2) an expected value of drill-string torque. Then, an outlier-removal mechanism is devised in the sorting process of NSGA-II to reduce the negative effects of parameter uncertainties. Next, the crowding distance calculation in NSGA-II is improved to ensure population diversity. Comparison of simulation results show that our method is effective on the MOP with parameter uncertainties. [ABSTRACT FROM AUTHOR]

The moment of inertia (MOI) is one of the most important parameters of a permanent magnet synchronous motor. High-precision identification of the MOI is essential to ensure system performance. This paper explains an MOI identification method based on the frame of an improved model-reference adaptive system (IMRAS). It improved a model-reference adaptive system by incorporating a curvature model and a dynamic gain in the system. First, a curvature model is used to estimate a load torque to construct an accurate reference model. This strategy reduces the identification error caused by ignoring the load torque. Note that identification accuracy and convergence speed are closely related to a gain factor in the system. Then, the relationship between the gain factor and the convergence time of the identification error is modeled as a power function. Finally, the IMRAS uses the absolute value of the relative MOI error and the convergence time for a given gain factor as switching conditions to balance the convergence speed and identification accuracy. A comparison with a conventional fixed-gain MRAS shows the effectiveness and superiority of the developed method. [ABSTRACT FROM AUTHOR]

This article presents a modified equivalent-input-disturbance (MEID) approach that actively rejects exogenous disturbances. A dynamic compensator is newly introduced in a conventional equivalent-input-disturbance (EID) estimator to further suppress the disturbances. The mechanism of the MEID approach to improving disturbance rejection is analyzed based on the transfer characteristic from the EID to the output of the system, and a stability condition of the MEID control system is presented and is used to design the parameters of the system. A comparison with the conventional EID approach and the disturbance-observer method through experiments demonstrates the validity of the presented method. [ABSTRACT FROM AUTHOR]

This article presents an additive-state-decomposition-based repetitive control (RC) method for a class of nonlinear systems with nonintegral-chain form and multiple mismatched disturbances. The key idea of this method is to decompose the original tracking problem for an uncertain nonlinear system into two tractable subproblems, namely RC problem for a primary linear time-invariant (LTI) periodic system and robust stabilization problem for a nonlinear secondary system. By taking the equivalent total effect of the multiple disturbances as one matched total disturbance, an improved linear extended state observer (ILESO) is constructed to estimate and compensate for the adverse effect of the multiple disturbances. The method has three significant features: First, it builds a bridge between the existing RC methods for LTI systems and nonlinear systems and, thus, broadens the application scope of RC; second, besides the known information of the model being used to the design of ILESO, the periodic nonlinearity in the plant is extracted to be treated by RC, which enhances both the transient and steady-state performances; and third, the ILESO-based control scheme and the RC law are designed separately, which is flexible for system design. The stability criterion and design procedure are presented in detail. Finally, numerical and experimental results illustrate the validity and superiorities of the method. [ABSTRACT FROM AUTHOR]

In crowdsensing, the diversity of the sensing tasks and an enhancement of the smart devices enable mobile users to accept multiple types of tasks simultaneously. In this study, we propose a new practical framework for dealing with the challenges of task assignment and user incentives posed by complex heterogeneous task scenarios in a crowdsensing market full of competition. First, based on the non-cooperative game property of mobile users, the problem is formulated into a Nash equilibrium problem. Then, to provide an efficient solution, a judgment method based on constraints (sensing time and sensing task dimension) is designed to decompose the problems into different situations according to the complexity. We propose a genetic-algorithm-based approach to find the combination of tasks that maximizes the utility of users and adopts a co-evolutionary model to formulate a stable sensing strategy that maintains the maximum utility of all users. Furthermore, we reveal the impact of competition between users and tasks on user strategies and use a cooperative weight to reflect it mathematically. Based on this, an infeasible solution repair method is designed in the genetic algorithm to reduce the search space, thus effectively accelerating the convergence speed. Extensive simulations demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents an output‐feedback repetitive control method for a class of systems with nonlinearities, unknown state‐dependent uncertainties, and external disturbances. An additive‐state‐decomposition approach is used to decompose the system under study into a primary linear periodic system in charge of repetitive‐control tracking task and a secondary linearized system in charge of robustly stabilization task. Such a treatment not only significantly simplifies system analysis and controller design, but also provides an effective method to cope with the intrinsic constraints between tracking and disturbance rejection. Another attractive point lies in two aspects. On one hand, a periodic term from the nonlinearity in the plant is extracted to be treated in the primary system and the known plant information is used to design controller parameters, which greatly improves the disturbance rejection ability of the system. On the other hand, repetitive control gain, feedback control gain, and the observer gain are optimized through an optimization algorithm by introducing an index to evaluate the overall system performance. System stability criteria and design procedure are developed. Finally, a case study on the tracking control of a chuck‐piece system demonstrates the validity of the proposed method. Comparisons with some existing methods show that the proposed method achieves better performances in both signal tracking and disturbance rejection. [ABSTRACT FROM AUTHOR]

Wind is the dominant factor for wind power generation. However, wind, which is intermittent and fluctuating all the time, is hard to be accurately forecasted, especially only by single-source numerical weather prediction. This article presents a short-term wind power prediction method based on multisource wind speed fusion. First, the relationships among the weather factors and wind power, and the characteristics of three independent forecasted wind speed (FWS) provided by three organizations are analyzed. Next, a weighted naive Bayes method is described to fuse the three FWSs in order to estimate an accurate wind speed according to their characteristics. Then, a backpropagation neural network is designed to predict the wind power based on the fused wind speed. Finally, application results show that the method accurately predicts wind power generation, and the accuracy is much higher than that predicted by conventional methods. [ABSTRACT FROM AUTHOR]

Brain hypothermia is widely operated to prevent peripheral healthy brain tissue from secondary damage, caused by increased brain temperature due to hypermetabolism around the damaged areas. Selective brain hypothermia is a new clinical technique of brain hypothermia for a brain disorder. Appropriate solution in a controlled temperature is directly infused into the artery to cool down a brain tissue through some catheters. While the brain temperature is controlled quickly, the body temperature has to be kept within a prescribed range by re‐warmed cerebrovascular blood return to the systemic circulation. As metabolic changes in the brain is variable due to the complex behavior of the disease state with the treatment effect.. In the present study two adaptive control methods as adaptive‐gain control and Fuzzy‐adaptive control are provided to carry out for precise temperature control for selective brain hypothermia. Several kinds of mathematical brain models with cerebrovascular damage are specially used to evaluate our temperature control methods. The effectiveness of the management of the brain temperature is demonstrated for four kinds control laws and five mathematical clinical brain models. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This article deals with contour tracking control of a networked multiaxis motion control system with repetitive tasks. To improve the control performance of the system under the overall effects of aperiodic time-varying delays and exogenous disturbances, a network-induced delay is first modeled to be a disturbance. Next, an improved equivalent-input-disturbance (IEID) approach is devised to suppress the overall effect. Then, an iterative learning controller is designed in combination with the IEID estimator to perform individual-axis tracking control. The stability of the system is analyzed using the separation theorem. After that, a cross-coupled controller is used to further improve the contour-tracking performance. Finally, simulations and experiments are carried out to verify the feasibility and effectiveness of the new approach. [ABSTRACT FROM AUTHOR]

The vision of the upcoming 6G technologies that have fast data rate, low latency, and ultra-dense network, draws great attentions to the Internet of Vehicles (IoV) and Vehicle-to-Everything (V2X) communication for intelligent transportation systems. There is an urgent need for distributed machine learning techniques that can take advantages of massive interconnected networks with explosive amount of heterogeneous data generated at the network edge. In this study, a two-layer federated learning model is proposed to take advantages of the distributed end-edge-cloud architecture typical in 6G environment, and to achieve a more efficient and more accurate learning while ensuring data privacy protection and reducing communication overheads. A novel multi-layer heterogeneous model selection and aggregation scheme is designed as a part of the federated learning process to better utilize the local and global contexts of individual vehicles and road side units (RSUs) in 6G supported vehicular networks. This context-aware distributed learning mechanism is then developed and applied to address intelligent object detection, which is one of the most critical challenges in modern intelligent transportation systems with autonomous vehicles. Evaluation results showed that the proposed method, which demonstrates a higher learning accuracy with better precision, recall and F1 score, outperforms other state-of-the-art methods under 6G network configuration by achieving faster convergence, and scales better with larger numbers of RSUs involved in the learning process. [ABSTRACT FROM AUTHOR]

The control method based on the equivalent‐input‐disturbance (EID) estimator and the Luenberger state observer has received much attention in recent years. However, the property of EID‐based control systems is still not well investigated. A number of design procedures were proposed but lacked sufficient theoretic justification. In this article, the two‐degree‐of‐freedom nature of an EID‐based control system is revealed. Specifically, the reference‐tracking is determined by an outer loop, while the disturbance rejection and robustness is determined by an inner loop consisting of the EID estimator and state observer. The bandwidth constraints on the inner loop are analyzed for plants having zeros or poles in the open right‐half plane by using the Bode and the Poisson integral formulas. These analyses provide a theoretic justification for conventional design procedures. Further, a coordinated design algorithm is provided for the EID‐based uncertain control systems. In addition, a comprehensive comparison of the EID‐based and the disturbance observer (DOB) based control systems is conducted in the system design aspect. Lastly, comparative studies of the EID‐based and DOB‐based control methods are given for various types of plants to validate the developed design algorithm. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), the barrier coverage’s sensing belt can be open or closed. A closed belt is more difficult to construct than an open belt, as it has higher requirements, especially for the directional sensor networks (DSNs) with rotatable sensors. A target-barrier is a closed annular belt around a target with anti-intrusion and surveillance protection functions for both the inside and outside of the belt. Thus, a target-barrier is well suited to many applications including ecological reserve monitoring and contamination monitoring. In this paper, we employ the Virtual Target-barrier Construction (VTBC) method to facilitate its construction. First, we define and calculate a virtual barrier curve in order to convert the complicated target-barrier construction problem in a rotatable DSN into a much simpler optimization problem. Then, a distributed particle swarm optimization (D-PSO) algorithm is designed to achieve a near optimal coverage of the closed virtual barrier curve. Finally, simulations are presented to prove that VTBC has a better performance than other approaches. [ABSTRACT FROM AUTHOR]

In recent years, there have been several breakthroughs in the theoretical research of servo control algorithms. However most of these control algorithms remain in the simulation stage. They are difficult to be applied directly to practical platforms or complex industrial sites because of the lack of an experimental system suitable for the verification of their effectiveness. To address this problem, we designed a multi-function servo control algorithm verification experiment system (MVES) within the MATLAB/Simulink theoretical simulation model directly to communicate with the TwinCAT 3 PLC master program to perform different servo control experiments. The MVES supports various Simulink models. However, its and the operation is simple and convenient, which greatly reduces the workload of the algorithm test and has important practical value. Two sets of comparative experiments were used to verify the versatility and superiority of MVES. [ABSTRACT FROM AUTHOR]

The industrial electronics (IE) discipline includes a wide variety of technical areas devoted to the application of electronics and electrical sciences for the enhancement of industrial and manufacturing processes. It inherently acts as a key enabling technology for a diverse number of applications and includes the latest developments in intelligent and computer control systems, robotics, factory communications and automation, flexible manufacturing, data acquisition and signal processing, vision systems, and power electronics, among others, as well as the educational and human factors involved. This makes IE inherently multidisciplinary, and, with many interconnected synergies, it plays a key role as an enabling technology in multiple domestic, biomedical, transportation, and industrial applications. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is presented to improve the speed-sensorless control performance of an interior permanent magnet synchronous motor using a nonsingular fast terminal sliding-mode observer and fractional-order software phase-locked loop. The interior permanent magnet synchronous motor system is first described. Next, a nonsingular fast terminal sliding mode observer is constructed to estimate the d-q-axis back electromotive force. The speed and position of the rotor are then accurately tracked using a fractional-order software phase-locked loop. The effectiveness and feasibility are verified through a simulation in MATLAB/Simulink. The results show an excellent performance despite a fluctuation in speed and torque ripple. [ABSTRACT FROM AUTHOR]

Vibration suppression in servo systems is significant in high-precision motion control. This paper describes a vibration-suppression method based on input shaping and adaptive model-following control. First, a zero vibration input shaper is used to suppress the vibration caused by an elastic load to obtain an ideal position output. Then, a configuration that combines input shaping with model-following control is developed to suppress the vibration caused by changes of system parameters. Finally, analyzing the percentage residual vibration reveals that it is effective to employ the sum of squared position error as a criterion. Additionally, a golden-section search is used to adjust the parameters of a compensator in an online fashion to adapt to the changes in the vibration frequency. A comparison with other input shaper methods shows the effectiveness and superiority of the developed method. [ABSTRACT FROM AUTHOR]

The weight of alloying elements is a decisive factor to determine the grade and quality of alloy steel. It is difficult to control the weight of alloying elements accurately running due to the fluctuations in the feed rate. This article presents a new control system including a controller and a two-level adjustment strategy. First, a mathematical model is built to describe the weighing system based on the analysis of the weighing characteristics. Next, a control system is developed based on the model. Then, a two-level data-based adjustment strategy for the parameters of the controller is devised based on both the real-time data of the current batch and historical data of former batches of weighing. It ensures that the controller adapts to the fluctuations in the feed rate. Finally, the results of real runs show that the method has high accuracy and strong robustness. [ABSTRACT FROM AUTHOR]

The preprocessing of surface electromyography (sEMG) signals with complementary ensemble empirical mode decomposition (CEEMD) improves frequency identification precision and temporal resolution, and lays a good foundation for feature extraction. However, a mode-mixing problem often occurs when the CEEMD decomposes an sEMG signal that exhibits intermittency and contains components with a near-by spectrum into intrinsic mode functions (IMFs). This paper presents a method called optimized CEEMD (OCEEMD) to solve this problem. The method integrates the least-squares mutual information (LSMI) and the chaotic quantum particle swarm optimization (CQPSO) algorithm in signal decomposition. It uses the LSMI to calculate the correlation between IMFs so as to reduce mode mixing and uses the CQPSO to optimize the standard deviation of Gaussian white noise so as to improve iteration efficiency. Then, useful IMFs are selected and added to reconstruct a de-noised signal. Finally, considering that the IMFs contain abundant frequency and envelope information, this paper extracts the multi-scale envelope spectral entropy (MSESEn) from the reconstructed sEMG signal. Some original sEMG signals, which were collected from experiments, were used to validate the methods. Compared with the CEEMD and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the OCEEMD effectively suppresses mode mixing between IMFs with rapid iteration. Compared with approximate entropy (ApEn) and sample entropy (SampEn), the MSESEn clearly shows a declining tendency with time and is sensitive to muscle fatigue. This suggests a potential use of this approach for sEMG signal preprocessing and the analysis of muscle fatigue. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of designing a two-dimensional (2D) modified repetitive-control system based on a Takagi–Sugeno (T-S) fuzzy model to achieve high tracking performance for a nonlinear plant. First, a nonlinear plant is represented by a T-S fuzzy model, and a modified repetitive controller with two repetitive loops is used to increases design flexibility. Next, a continuous-discrete 2D model is established to make use of the 2D characteristics in the modified repetitive-control system. Then, a sufficient stability condition is derived in terms of linear matrix inequalities. Three parameters are used to balance continuous control and discrete learning actions: one in a repetitive loop and two in a Lyapunov–Krasovskii functional. A particle swarm optimisation algorithm yields optimal parameters and the gains of the modified repetitive and state-feedback controllers. Finally, simulation and comparison results demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Reconstructing a pitchfork bifurcation that is destroyed by an exogenous disturbance is important to control it. This paper extends the equivalent-input-disturbance (EID) approach to solve the problem of reconstructing a pitchfork bifurcation when there is an exogenous disturbance for the first time. A nonlinear term and a bifurcation parameter are designed in the system to reconstruct the pitchfork bifurcation. The bifurcation characteristics of the equilibriums of the system are analyzed by exploring the features of the EID-based system. This provides us analytic solutions for a system with a degree higher than four for the first time. Unlike other pitchfork-bifurcation control systems, the EID-based control system only rejects the disturbance and retains the nonlinearity. Numerical examples demonstrate the effectiveness of the presented method and show its superiority over others. The robustness of the reconstruction with respect to measurement noise and control of a pitchfork bifurcation are also shown by the examples. [ABSTRACT FROM AUTHOR]

A generalized disturbance observer‐based (GDOB) control system and its robust stability are studied in this paper. One feature of this new GDOB scheme is that an inverse of the nominal plant in the standard DOB is no longer required. Thus, it is applicable to both minimum‐phase (MP) and non‐minimum‐phase (NMP) plants. New conditions for robust stability are derived for two cases: plants with no right half plane (RHP) zero and plants with no RHP pole, which are in a perfect dual form. The duality reveals that the robust stability for a plant with RHP zeros is met when the Q‐filter has a sufficiently large constant. This is fundamentally different from well‐known results for a MP plant. The stability conditions indicate that the robustness against large uncertainties not only can be achieved by a well‐selected Q‐filter, but also by the reduction of mismatch between a compensated plant and a desired model. The obtained stability results provide guidelines for the control system design. This is illustrated by three examples. [ABSTRACT FROM AUTHOR]

This paper considers the problems of designing multivariable proportional-integral-derivative (PID) controllers for discrete-time systems with D-stability and/or H ∞ performance constraints. First, the PID control problem is cast into a static output feedback (SOF) control problem, and linear matrix inequality (LMI) conditions are presented. Then, a cone complementarity linearisation (CCL) method is used to solve the SOF-stabilisation problem, and a coordinate transformation matrix (CTM) optimisation approach is extended to solve the optimal H ∞ SOF control problem. Finally, a numerical example is provided to show the effectiveness and advantage of the CTM optimisation approach, and application to the anti-sway and positioning control of an overhead crane demonstrates the validity of the approach. [ABSTRACT FROM AUTHOR]

This article presents an improved disturbance estimation and rejection method based on equivalent input disturbance (EID) with H∞ robust performance for a time-varying uncertain system. The analysis and synthesis are conducted in the frequency domain. Different from conventional two-degree-of-freedom (2-DOF) H∞ control, some related weighting functions are introduced into a new devised structure to separate the rejection of the parameter uncertainty and disturbance from reference tracking. Compared with the existent EID-based approaches designed in the time domain of which the robust performance is not addressed, H∞ robust performance of the closed-loop system is guaranteed in this design. Treating the difference between the real plant and an ideal plant, namely, the overall effect of the parameter uncertainty and disturbance, as an EID, an EID estimation and compensation strategy is then established to cancel out the effect of the EID in system output. Furthermore, a constant scaling matrix is introduced into system design to reduce the conservativeness of a quadratic stabilization condition. Finally, a comparison with conventional 2-DOF robust H∞control illustrates the advantages of the developed method. [ABSTRACT FROM AUTHOR]