This paper presents a control method for the problem of trajectory jitter and poor tracking performance of the end of a three-joint rigid manipulator. The control is based on a high-order particle swarm optimization algorithm with an improved sliding mode control neural network. Although the sliding mode variable structure control has a certain degree of robustness, because of its own switching characteristics, chattering can occur in the later stage of the trajectory tracking of the manipulator end. Hence, on the basis of the high-order sliding mode control, the homogeneous continuous control law and super-twisting adaptive algorithm were added to further improve the robustness of the system. The radial basis function neural network was used to compensate the errors in the modeling process, and an adaptive law was designed to update the weights of the middle layer of the neural network. Furthermore, an improved particle swarm optimization algorithm was established and applied to optimize the parameters of the neural network, which improved the trajectory tracking of the manipulator end. Finally, MATLAB simulation results indicated the validity and superiority of the proposed control method compared with other sliding mode control algorithms.

Aiming at the realization of fast and high-precision detection of the workpiece, an adaptive bidirectional gray-scale center of gravity extraction algorithm for laser stripes is proposed in this paper. The algorithm is processed in the following steps. Firstly, the initial image processing area is set according to the floating field of the camera’s light stripe, followed by setting the adaptive image processing area according to the actual position of the light stripe. Secondly, the center of light stripe is obtained by using the method of combining the upper contour with the barycenter of the bidirectional gray-scale. The obtained center of the light stripe is optimized by reducing the deviation from adjacent center points. Finally, the slope and intercept are used to complete the breakpoint. The experimental results show that the algorithm has the advantages of high speed and precision and has specific adaptability to the laser stripes of the complex environment. Compared with other conventional algorithms, it greatly improves and can be used in industrial detection.