Carbon fiber reinforced polymer (CFRP) plays an important role in many fields, especially in aviation and civil industries. The electrical conductivity of CFRP is critical for its electrical behavior, such as its lightning strike vulnerability, electromagnetic shielding ability, and potential uses for self-sensing. In addition, the electrical conductivity is related to the mechanical integrity. Therefore, electrical properties can be measured as an indication when detecting delamination and other defects in CFRP. This review provides a comprehensive basis for readers to grasp recent research progresses on electrical behaviors of CFRP.

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To improve the recognition rate of lower limb actions based on surface electromyography (sEMG), an effective weighted feature method is proposed, and an improved genetic algorithm support vector machine (IGA-SVM) is designed in this paper. First, for the problem of high feature redundancy and low discrimination in the surface electromyography feature extraction process, the weighted feature method is proposed based on the correlation between muscles and actions. Second, to solve the problem of the genetic algorithm selection operator easily falling into a local optimum solution, the improved genetic algorithm-support vector machine is designed by championship with sorting method. Finally, the proposed method is used to recognize six types of lower limb actions designed, and the average recognition rate reaches 94.75%. Experimental results indicate that the proposed method has definite potentiality in lower limb action recognition.

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Summary This article investigates the tracking control problem for a kind of chained‐form nonholonomic systems in the presence of unknown time‐varying disturbances and full‐state constraints. First, a finite‐time tracking controller is constructed to drive the action of relay switching, which converts the whole control system into two design stages. In order to handle with the predefined asymmetric state constraints, the original tracking error system is turned into an unconstrained system on the basis of state‐dependent function (SDF) function transformations. In this framework, an adaptive tracking control strategy is proposed by means of backstepping and dynamic surface control. Correspondingly, another tracking control law is also constructed to assure the stability of the tracking error system before the action of switching. The theoretical analysis of stability indicates that the proposed tracking control scheme can guarantee that the output state tracking errors converge to zero and the desired state constraints are not violated. At the same time, all the closed‐loop system signals maintain bounded in entire control process. Finally, the validity of the presented tracking control algorithm is demonstrated by simulation and experiment results.