The raising process has been widely used in manufacturing fabric productions. After raising the surface of the fabric, productions are covered with a fluff layer. The quality of the fabric surface is often valuated by the fluffing type. In order to objectively assess the fluff quality of the fabric surface, an optimal sensing method is proposed in this paper. The fluff contour image was firstly collected by the light-cut imaging device. Then, the fluff region was segmented by the adaptive image segmentation method, the contour coordinates of the fabric were extracted using the freeman chain code and constructed in the form of the binary image. Lastly, a back-propagation neural network (BPNN) was used to learn the relationship between the contour coordinates and the fluff quality. On this basis, a practical fabric fluff detection platform was developed based on the optimal sensing technique. Experimental tests were conducted to evaluate the performance of the proposed method in detecting the fluff quality with four different colours and different fluffing processes. Furthermore, the actual fabric inspection was carried out. The detection correct rate can reach 94.17 %, which can meet the practical production requirement.

Servo control requires fast following performance and high steadystate accuracy in position follow-up mode. Servo of special environment application has more strict requirements for motor performance and reliability. The development of servo system has experienced the initial electro-hydraulic servo, using DC brush motor, whose speed, reliability and service life are relatively limited. Today's AC servo system is mainly AC asynchronous or permanent magnet synchronous motor. The development of servo system is more and more alternating, permanent magnetization, intelligence, integration, miniaturization, networking and modularization. This paper mainly studies the servo control of permanent magnet synchronous AC motor. Permanent magnet synchronous AC motor is divided into permanent magnet synchronous motor and permanent magnet brushless DC motor. It is found that the servo control based on permanent magnet synchronous motor is better than that based on permanent magnet brushless DC motor in following performance and steady-state accuracy.

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An improved SURF (Speeded-Up Robust Feature） algorithm is proposed to deal with the time-consuming and low precision of positioning of industrial robot. Hessian matrix determinant is used to extract feature points from the target image and a multi-scale spatial pyramid is constructed. The location and scale value of feature points are determined by neighbourhood non-maximum suppression method. The direction of feature points is defined as directional feature descriptors by the binary robust independent elementary feature (BRIEF). The progressive sample consensus (PROSAC) is used to carry out second precise matching and remove mismatching points based on the Hamming distance. Then, an affine transformation model is established to describe the relationship between the template and target images. Centroid coordinates of the target can be obtained based on the affine transformation. Comparative tests were carried out to demonstrate that the proposed method can effectively improve the recognition rate and positioning accuracy of the industrial robots. The average time consuming is less than 0.2 s, the matching accuracy is 96 %, and the positioning error of the robot is less than 1.5 mm. Therefore, the proposed method has practical application importance.

Brushless Direct Current (BLDC) motors, also known as permanent magnet motors find wide applications in many industries due to their higher performance, reliability and ease of control. A new generation of microcontrollers and advanced electronics has overcome the challenge of implementing required control factions, making BLDC motor more practical for a wide range of uses. The main objective of this paper is controlling speed of BLDC motor and displays its speed. The speed control of the BLDC motors is very essential. This proposed system provides a very precise and effective speed control system. The user can increase or decrease the speed as per the requirement and the motor will run at that exact speed. In this paper, we focus on the simulink modeling of BLDC using MATLAB/SIMULINK. Keywords-Brushless DC motor speed control in open and closed loop; six step inverter; PI controller; HALL sensor; Simulink model in MATLAB,