The purpose of this study was to ensure the compliance and safety of a robot’s movements during interactions with the external environment. This paper proposes a control strategy for learning variable impedance characteristics from multiple sets of demonstration trajectories. This strategy can adapt to the control of different joints by adjusting the parameters of the variable impedance control policy. Firstly, multiple sets of demonstration trajectories are aligned on the time axis using Dynamic Time Warping. Subsequently, the variance obtained through Gaussian Mixture Regression and a variable impedance strategy based on an improved Softplus function are employed to represent the variance as the variable impedance characteristic of the robotic arm, thereby enabling variable impedance control for the robotic arm. The experiments conducted on a self-designed robotic arm demonstrate that, compared to other variable impedance methods, the motion accuracy of the trajectories of joints 1 to 4 improved by 57.23%, 3.66%, 5.36%, and 20.16%, respectively. Additionally, a stiffness-variable segmented generalization method based on Dynamic Movement Primitive is proposed to achieve variable impedance control in various task environments. This strategy fulfills the requirements for compliance and safety during robot interactions.

This paper presents a finite-time switching resilient controller for the networked teleoperation system control under time-varying delays and denial-of-service (DoS) attacks. The proposed controller comprises a proportional-differential plus damping (PD+d-like) controller and a switching resilient compensator. The first component, a PD+d-like controller, uses a continuous non-smooth function on the state errors and velocity signals to guarantee the global finite-time convergence. The latter part of the proposed controller, a switching resilient compensator, combines the zero-order holder (ZOH) with the continuous-time proportional-derivative (PD) regulator. This proposed controller could maintain global finite-time stability (GFTS) when time-varying delays and random DoS attacks simultaneously occur. Furthermore, we obtain the system stability criterion and establish relationships between controller parameters and maximum stability delay using Linear Matrix Inequality (LMI) technology for parameter tuning guidance. Both simulation and experimental results validate the resiliency of the proposed controller to time-varying delays and random DoS attacks.

Purpose Robotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm. Design/methodology/approach This study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification. Findings Experiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved. Originality/value A new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.

Solid oxide fuel cell system is widely acknowledged as the leading alternative energy generation system in the field. Due to their high efficiency, low emissions, low noise, and various other advantages, solid oxide fuel cell systems are being considered for use in automobiles as a replacement for traditional internal combustion engines. However, prolonged operation and abnormal shutdowns can lead to performance degradation, which affects the efficiency, stability, and lifespan of stack. In the context of prolonged operation, considering the fluctuations in stack performance parameters and balance of plant, several regression models based on voltage parameters are established to accurately predict changes in stack performance. The results reveal that the genetic algorithm optimized backpropagation neural network model is highly sensitive in predicting the system performance degradation. Further analysis reveals that abnormal shutdowns can cause system performance fluctuations. As a result, the number and duration of shutdowns are incorporated into genetic algorithm optimized

Based on the basic nonlinear parameter system of the solid oxide electrolysis cell, the data-driven method was used for system identification. The basic model of the solid oxide electrolysis cell was accomplished in Simulink and experiments were performed under a diversified input/output operating environment. The experimental results of the solid oxide electrolysis cell basic parameter system generated 15 datasets. The system identification process involved the utilization of these datasets with the application of nonlinear autoregressive-exogenous models. Initially, data identification came from the Matlab mechanism model. Then, the nonlinear autoregressive-exogenous structures were estimated and selected exploratively through an individual operating condition. In terms of fitness, we conclude that the solid oxide electrolysis cell parameter system cannot be satisfied by a solitary autoregressive-exogenous model for all datasets. Nevertheless, the nonlinear autoregressive-exogenous model utilized S-type nonlinearities to fit a total of 2 validation datasets and 15 estimated datasets. The obtained results were compared with the basic parameter system of a solid oxide electrolysis cell, and the nonlinear autoregressive-exogenous projected output demonstrated an accuracy of over 93% across diverse operational circumstances—regardless of whether there was noise interference. This result has positive significance for the future use of the solid oxide electrolysis cell to achieve the dual carbon goal in China.

The application of new energy systems for industrial production to advance air pollution prevention and control has become an irreversible trend. This development includes hybrid systems consisting of reversible solid oxide cells (RSOC) and a Li-ion battery; however, at present the energy dispatching of such systems has an unstable factor in the form of poor heat/electricity/gas controllability. Therefore, the system studied in this paper uses the Li-ion battery as the energy supply/storage case, and uses the RSOC to supply power for the Li-ion battery charge or the Li-ion battery supply power to the RSOC for hydrogen production by water electrolysis. In this hybrid system, Li-ion battery thermoelectric safety and RSOC hydrogen production stability are extremely important. However, system operation involves the switching of multiple operating conditions, and the internal thermoelectric fluctuation mechanism is not yet clear. Therefore, in this paper we propose a separate control with a dual mode for hybrid systems. Active disturbance rejection control (ADRC) with a simple structure is used to achieve Li-ion battery module thermoelectric safety and control the hydrogen production/consumption of the RSOC module in the hybrid system. The results show that the required Li-ion battery thermoelectric safety and RSOC hydrogen consumption/production requirements can be met using the proposed controller, leading to a hybrid system with high stability control.

Fuel cell technology is the fourth generation technology after hydropower, thermal power and nuclear power. Once the solid oxide fuel cell system fails, if it can't be found in time, the initial glitch may slowly evolve and spread to the subsequent components. Therefore, fault diagnosis is a prerequisite to ensure its stability. In order to diagnose fuel leakage fault of solid oxide fuel cell system, a decision tree is proposed to diagnose the fuel leakage fault of solid oxide fuel cell. Compared with other machine learning methods, it can be clearly observed that the decision tree method can effectively identify the severity of faults. This method can be extended to the fault diagnosis of air leakage.

It is difficult to simulate haptic interactions in a virtual surgical system due to the complexity of biomechanical features. Most prevalent methods simulate the viscoelasticity of soft tissues by establishing spring and damping models or neural networks, but the former often have poor accuracy while the latter cannot satisfy the real-time requirements of virtual surgical systems. This paper proposes an interaction force model based on K-nearest neighbor algorithm (KNN algorithm) and applies it to a virtual surgical system. The experimental result shows that the model to predict the force feedback of the soft tissue based on KNN algorithm can calculate the interactive force accurately during the puncture process. It meets the requirements of real-time and yields a range of error narrower than that incurred in case of manual perception.

Motor imagery brain-computer interface (MI-BCI) can parse user motor imagery to achieve wheelchair control or motion control for smart prostheses. However, problems of poor feature extraction and low cross-subject performance exist in the model for motor imagery classification tasks. To address these problems, we propose a multi-scale adaptive transformer network (MSATNet) for motor imagery classification. Therein, we design a multi-scale feature extraction (MSFE) module to extract multi-band highly-discriminative features. Through the adaptive temporal transformer (ATT) module, the temporal decoder and multi-head attention unit are used to adaptively extract temporal dependencies. Efficient transfer learning is achieved by fine-tuning target subject data through the subject adapter (SA) module. Within-subject and cross-subject experiments are performed to evaluate the classification performance of the model on the BCI Competition IV 2a and 2b datasets. The MSATNet outperforms benchmark models in classification performance, reaching 81.75 and 89.34% accuracies for the within-subject experiments and 81.33 and 86.23% accuracies for the cross-subject experiments. The experimental results demonstrate that the proposed method can help build a more accurate MI-BCI system.

As communication networks are implemented for information exchange between the master and slave sides of bilateral teleoperation systems, they are exposed to cyber-attack threats. This paper aims to analyse the performance of bilateral teleoperation systems in the presence of random denial-of-service (DoS) attacks and constant transmission delays and propose a mode-dependent switching controller to mitigate the influence of DoS attacks. The characteristics of DoS attacks and networks are thoroughly incorporated in the design; also considered is the case of both communication directions behaving independently. Specifically, the model of a teleoperation system under a DoS attack is integrated as a stochastic jump system. A mode-dependent control approach is proposed for a teleoperation system to mitigate the influence of random DoS attacks. In case studies, vulnerability analysis and time-domain simulation results show that teleoperation system performance can be degraded under continuous random DoS attacks. When the proposed mode-based switching controllers are installed, the trajectory tracking performance and authenticity of interaction force feedback are significantly improved during the attacking period.

Solid Oxide Fuel Cell (SOFC) is usually composed of the SOFC stack and peripheral auxiliary subsystems. The SOFC stack is the main part of the battery. Its temperature control, namely thermal management, is the key to the safe, long-term and efficient operation of the battery system.This paper first introduces the system architecture, then lists the general Model equation, and then defines the Control objective. According to the information, the stack is modeled in MATLAB/Simulink environment, and then the Model Predictive Control (MPC) is used to select multiple variable pairs,in order to achieve closed-loop thermal management of the SOFC stack under the output constraints, to ensure that each link of the stack in the appropriate temperature to achieve long-term, efficient operation.The simulation shows that the changes of various temperatures in the system are within a reasonable range through the demonstration of the output waveform, the average temperature in the stack is stabilized at about 1045K, and the inlet temperature of the stack is stabilized at 978K, and the outlet temperature of the stack is stabilized at 1063K, which perfectly achieves the expected goal.

In this article, an adaptive path following the controller of a multijoint snake robot (MSR) based on the improved Serpenoid curve is proposed. The proposed controller can make the MSR follow the desired path. Compared with the traditional controller, this controller can make the position error possess fast convergence speed and high stability. The swing, the controller can estimate unknown friction coefficients, which improves the adaptive path following the ability of the MSR in an environment with unknown friction coefficients. First, the dynamic model without lateral force is established. Then, the control objectives of the controller are formulated. Third, the Serpenoid gait equation is improved, and the state-dependent time-varying amplitude is obtained. Fourth, the input–output control function of the system and the tracking function of the swing amplitude compensation are designed by the adaptive control method. The stability of the motion attitude angle variable errors and uniformly ultimately bounded stability of the tracking position are verified, respectively. Finally, the effectiveness and superiority of the proposed controller are verified by experiments.

The control of the charging and discharging process of the power lithium battery is the key to its efficient operation. However, there are many complex electrochemical reaction processes, so the power lithium battery is difficult to model. In order to study a more suitable modeling method for the charging and discharging process of lithium batteries, this paper uses the model identification method of ARMAX and Hammerstein-Wiener to carry out a data-driven modeling of the relationship between the input current and the open-circuit voltage of the power supply during the charging and discharging process of lithium batteries. Through parameter identification, this paper compares the ability of two lithium battery system identification models to predict the battery voltage response. Through the identification of parameters, this paper compares the ability of two lithium battery system identification models to predict battery voltage response. The results show that this process has certain nonlinear characteristics, the predictive ability of the linear model is insufficient, and the nonlinear model is more suitable for predicting the dynamic behavior of this process, which provides a reference for future health control.

Proton exchange membrane fuel cell has become the most widely used fuel cell type in fuel cell vehicles. Estimation of fuel cell operating conditions can ensure the safety of the system, determine whether the system is faulty, and provide help for controller design. Aiming at the air supply system of PEMFC, considering the non-linearity and coupling of the system, a condition estimation algorithm based on the TrAdaboost algorithm is proposed to estimate whether the membrane thickness of the proton exchange membrane inside the stack is normal or not. Through the analysis of the model, the key variables affecting the membrane thickness of the stack are written into the system measurement values, and the working conditions of the stack are estimated by using the proposed algorithm. Finally, the proposed hybrid algorithm is verified on the Matlab simulation platform, and the results show that the proposed method can accurately track the real-time working conditions of the fuel cell stack.

A second-order adaptive integral terminal sliding mode controller is proposed for the trajectory tracking control of robotic manipulators with uncertainties. A second-order integral terminal sliding mode surface is designed for which an integral sliding mode (ISM) surface and a fast nonsingular integral terminal sliding mode surface are combined. By using the ISM surface, the reaching phase is removed, which enhances system robustness. The steady-state error is reduced because of the presence of an error integral term. A fast second-order nonsingular integral terminal sliding mode surface is employed to ensure that the ISM surface is able to converge to zero rapidly within a finite period of time without leading to a singularity problem. The control input of the proposed controller is continuous. Thus, the chattering phenomenon is removed. An adaptation technique is employed to estimate the upper bound of unknown lumped disturbance. The second-order derivative of position is calculated using a robust differentiator, making it practical. Simulations and experiments show that the proposed scheme improves the tracking performance and eliminates chattering.

为了改善传统分形图像纹理特征处理效果不理想问题,提出基于牛顿迭代算法的分形图像纹理细节增强方法.利用牛顿迭代算法获取分形图像坐标极值,设置迭代初始点值.将图像绕原点120°的旋转变换,完成零点吸引域映射;采用迭代距离参数、初始参数、色彩初始值、色彩渐变参数以及相关运算符号构建RGB颜色通道表达式,计算覆盖图像目标部分的网格数量,通过计算收缩仿射变换,得到压缩映射与压缩因子关系,通过迭代计算完成纹理细节增强.经实验分析可知,迭代初始参数、色彩初始值参数的取值影响分形图像增强效果,且通过对比不同方法峰值信噪比与均方误差指标测试结果.根据实验结果可得结论,上述方法具有应用有效性且实践性较强.

Background: Brain-Computer Interface (BCI) can bring great convenience to patients in the process of rehabili-tation training and prosthetic control. However, how to extract effective features is a core issue in a BCI system. Methods: We proposes the Multi-Feature Fusion Method based on Wavelength Optimal Spatial Filter and Mul-tiscale Entropy for classifying the electroencephalogram signals (EEG) in four kinds of motor imagery tasks. The method can combine Wavelength features with Multiscale Entropy. Results: Two groups of experiments were conducted. One for simple four types of motor imagery (MI) tasks and another for unilateral limb. Experiments show that our proposed method has better performance compared to other methods. (82.55% versus 73.08% average accuracy respectively). Conclusions: The proposed method can effectively improve the accuracy of EEG classification in multiclass motor imagery and will be useful for neurorehabilitation through motor imagery for hemiplegic patients.

Usually the highlights can be calculated with the specular term of the bidirectional reflectance distribution functions developed for glossy or matte materials. However, as for the translucent materials, complex appearance could be caused by the scattering of light inside the medium. An efficient highlight generation model is presented to simulate the highlight effects on smooth or rough surfaces or around the boundaries of objects made from translucent materials. The presented model is derived from the directional dipole model approximation of the diffusive part of the bidirectional scattering surface reflectance distribution function. Unlike the previous specular reflection models, the presented model builds a relationship between the highlights and the scattered lights inside the medium by considering the refracted ray of the incident point and the ray toward the emergent point, which could represent the variation in fluence due to the internal scattering at the surface. By integrating a rendering process with the directional dipole model, the resulting highlight effects term could be represented in a similar way by the specular term of a bidirectional reflectance distribution function model. The number and the strength of the generated highlight pixels were compared among typical highlight generation models. It is demonstrated that the presented model could generate highlight effects at the appropriate positions and enhance the perceptual translucency of specific edge areas greatly.

目的 以猪肾为例,通过一系列对比和类比实验分析生物组织松弛阶段压应力变化的影响因素,并建立较为准确且具有一定泛化性的生物组织松弛阶段力学模型.方法 利用自搭建力学实验平台,对猪肾实施不同情况下压应力松弛实验.分析实验数据并整理作图,总结影响力变化的各种因素.基于获得结论采用神经网络学习算法,对猪肾松弛阶段力变化过程进行建模.结果 预挤压力、松弛时间为生物组织松弛阶段压应力变化的主要影响因素.测试样本验证实验平均误差为6.4 mN,泛化样本验证实验平均预测误差为34.9 mN,建模效果良好.结论 神经网络建模算法具有泛化能力强、容错性好等优点,有利于为虚拟手术系统提供更为真实的力触觉反馈预测,对非线性生物组织力学建模而言是一种新思路.

As an energy conversion and generation device that converts hydrogen energy into electrical energy, the solid oxide fuel cell is one of the most promising fuel cells. However, thermal stability is the major reason for the shortened solid oxide fuel cell lifetime of hybrid electric vehicles usage. In existing solid oxide fuel cell system, the peak temperature occurs in the afterburner, so the thermal safety assessment is very important for solid oxide fuel cell stack. In this paper, afterburner thermal performance of natural gas solid oxide fuel cell system is studied based on computational fluid dynamics simulation. The model is analysed by changing gas temperature, gas flow velocity, gas composition ratio and steam to carbon ratio. The afterburner temperature distribution and its safety characteristics are obtained through model analysis. The simulation and discussion results can guide the thermoelectric safety assessment and controller design of the SOFC system.