A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.     

Combination of dynamic features with a new mask to optimize neural network speech enhancement

Concerning the problem that the Neural Network speech enhancement algorithm cannot fully represent the nonlinear structure of speech due to feature selection,which leads to speech distortion.This paper proposes the combination of dynamic features with a new mask to optimize neural network speech enhancement.First,three features of noisy speech are extracted and spliced to obtain static features.Then,the first and second difference derivatives are obtained to capture the instantaneous signals of speech and fuse them into dynamic features.The combination of dynamic and static features completes internal complementarity of features and reduced speech distortion.Second,in order to enhance the intelligibility and clarity of speech at the same time,an adaptive mask is proposed,which can adjust the energy ratio of speech and noise as well as the ratio of the traditional mask and the square root mask.The Gammatone channel weight is used to modify the mask value in each channel to simulate the human auditory system and further improve the speech intelligibility.Finally,the simulation of multiple voices under different noise backgrounds shows that compared with different literature algorithms,the algorithm has a higher SNR,subjective speech quality and short-term objective intelligibility,which verifies the effectiveness of the algorithm.     

Passivity‐based adaptive control of a 2‐DOF serial robot manipulator with temperature dependent joint frictions

Mechanical systems are always suffering from the effects of temperature dependent friction forces where the system is operated in a wide range of temperature. Temperature and its variation play an important role in friction force in mechanical systems. If it is not compensated, it will tend to unwanted consequences, including steady‐state errors, limit cycling, and hunting. Therefore, it is necessary to take the temperature effects into account. This has been a strong motivation for the researchers to work on temperature effects on joint friction. In this paper, an adaptive compensation (control) scheme is proposed and applied to a 2‐degree‐of‐freedom serial robot manipulator by taking the temperature effects into account on the joints friction. In the proposed control scheme, the temperature is not required to be sensed. In this paper, joint friction is described by LuGre dynamic model with temperature dependent parameters. These parameters are described by some functions with unknown temperature dependent terms. According to the mathematical and practical concepts, the temperature dependent friction is decomposed into a viscous term and a disturbance term. An adaptive controller is designed to compensate the friction effect and it is shown that the proposed controller relaxes the condition for a priori knowledge about the environment characteristics, including the upper and lower bounds of the environment temperature and the parameters of the functions, describing the temperature dependent joint frictions. The stability and convergence of the joint position and velocity are proved in the sense of Lyapunov and then the proposed method is confirmed by the simulations.     

钢制导热油烘缸的数值模拟与结构优化

针对钢制导热油烘缸在工作时出现工作表面温度分布不均匀、温差大等情况，采用三维建模和模拟仿真方法对钢制导热油烘缸的结构进行优化。本研究主要从3个方面对钢制导热油烘缸进行结构改进，分别是改变循环油路通道数量，改变进油槽与出油槽上孔的排列方式，改变循环油路的结构。研究结果表明，相对其他烘缸结构，具有循环油路通道数量为20个、进油槽与出油槽上孔为单排排列、循环油路两两相通的钢制导热油烘缸的性能更好，可以达到工作表面温度分布均匀、温差控制在±5℃以内的目的。     

Novel model reference adaptive control architecture using semi‐initial excitation‐based switched parameter estimator

This paper is a generalization of the recently developed techniques of initial excitation (IE)–based adaptive control with an introduction to the definition of semi‐initial excitation (semi‐IE), a still more relaxed notion than IE. Classical adaptive controllers typically ensure Lyapunov stability of the extended error dynamics (tracking error + parameter estimation error) and asymptotic tracking, while requiring a stringent condition of persistence of excitation (PE) for parameter convergence. Of late, the authors have proposed a new adaptive control architecture, which guarantees parameter convergence under the online‐verifiable IE condition leading to exponential stability of the extended error dynamics. In earlier works, it has been established that the IE condition is significantly milder than the classical PE condition. The current work further slackens the excitation condition by proposing the concept of semi‐IE. The proposed adaptive controller is proved to ensure convergence of the parameter estimation error to a lower‐dimensional manifold under the weaker semi‐IE condition, while the stronger condition of IE guarantees convergence of the parameter estimation error to zero. The designed algorithm is shown to improve transient response of tracking error sufficiently in contrast to conventional adaptive controllers.     

Adaptive constraint control for flexible manipulator systems modeled by partial differential equations with dead-zone input

In this article, based on partial differential equations (PDEs), the flexible manipulator system with both dead-zone input and state constraints is studied. The dynamic model of the flexible manipulator system is described by PDEs. The parameters of the dead-zone input are unknown, and the state constraint problem is also considered. An adaptive approach is proposed to offset the effects caused by dead-zone input. Thus, to guarantee that all states remain within their respective constraint regions, the boundary control law based on the barrier Lyapunov function is given, and an adaptive controller is designed. According to the Lyapunov analysis method, the control method is given to ensure that all signals of the closed-loop system are bounded and all states satisfy the constraint conditions. Finally, simulation results show the effectiveness of the proposed control method in this article.     

接触器触头系统振动机理分析及算法优化

为了解决合闸过程中动、静触头接触引起的振动弹跳问题.本文建立了接触系统的二自由度运动微分方程,并利用遗传算法对交流接触器吸合过程进行优化,同时通过高速摄影实验对接触弹跳的全过程进行了观察和分析.结果表明:理论与实验结果高度一致,铁心在触头分离前发生碰撞,进一步加剧触头弹跳;铁芯弹跳再次碰撞时,触头的弹跳不受影响;在接触器运行过程中,动铁芯的运动会引起系统轻微振动;采用遗传算法优化的接触器触头弹跳时间和最大振幅均减小.研究结果为进一步控制和减小接触弹跳提供了理论依据.     

Robust adaptive control for unmatched systems with guaranteed parameter estimation convergence

This paper provides a modified model reference adaptive control (MRAC) scheme to achieve better transient control performance for systems with unknown unmatched dynamics, where an adaptive law with guaranteed convergence is introduced. We first revisit the standard MRAC system and analyze the tracking error bound by using L₂‐norm and Cauchy‐Schwartz inequality. Based on this analysis, we suggest a feasible way to compensate the undesired transient dynamics induced by the gradient descent–based adaptive laws subject to sluggish convergence or even parameter drift. Then, a modified adaptive law with an alternative leakage term containing the parameter estimation error is developed. With this adaptive law, the convergence of both the estimation error and tracking error can be proved simultaneously. This enhanced convergence property can contribute to deriving smoother control signal and improved control response. Moreover, this paper provides a simple and numerically feasible approach to online verify the well‐known persistent excitation condition by testing the positive definiteness of an introduced auxiliary matrix. Comparative simulations based on a benchmark 3‐DOF helicopter model are given to validate the effectiveness of the proposed MRAC approach and show the improved performance over several other MRAC schemes.     

Pore-scale study of effects of different Pt loading reduction schemes on reactive transport processes in catalyst layers of proton exchange membrane fuel cells

Reducing the Platinum (Pt) loading while maintaining the performance is highly desired for promoting the commercial use of proton exchange membrane fuel cells (PEMFCs). Different methods have been adopted to fabricate catalyst layers (CLs) with low Pt loading, including utilizing lower Pt/C catalysts (MA), mixing high Pt/C catalysts with bare carbon black particles (MB), and reducing CL thickness while maintaining high Pt/C ratio (MC). In this study, self-developed pore-scale model is adopted to investigate the performance of the three Pt reduction methods. It is found that MA shows the best performance while MB shows the worst. Then, effects of Pt dispersion are further explored. The results show that denser Pt sites will result in higher local oxygen flux and thus higher local transport resistance. Therefore, MA method, which shows the better Pt dispersion, leads to improved performance. Third, CLs with quasi-realistic structures are investigated. Higher tortuosity resulting from the random pores produces higher bulk resistance along the thickness direction, while MA still exhibits the best performance. Finally, improved CL structures are investigated by designing perforated CL structures. It is found that adding perforations can significantly reduce the bulk transport resistance and can improve the CL performance. It is demonstrated that CL structure plays important roles on performance, and there are still huge potentials to further improve CL performance by increasing Pt dispersion and optimizing CL structures.