To achieve high-precision trajectory tracking for multi-joint robotic manipulators in the presence of model uncertainties, external disturbances, and strong coupling effects, this paper proposes a nonsingular fast terminal sliding mode control (NFTSMC) scheme incorporating an extended Kalman filter-based disturbance observer. First, the Kalman filter is combined with an extended state observer to perform the real-time observation of both internal and external disturbances in the system, accurately estimating system uncertainty and external disturbances. This approach reduces noise interference while significantly improving the correction accuracy of position and tracking errors. Second, an improved nonsingular fast terminal sliding mode controller with an optimized convergence law is introduced to ensure stability during the tracking process, effectively mitigate oscillation phenomena, and accelerate the system’s convergence speed. Finally, the convergence of the proposed method is analyzed by constructing an appropriate Lyapunov function. Simulation and experimental results strongly validate the superior performance of the proposed control strategy, demonstrating that the system can achieve high-precision trajectory tracking under the complex coupled effects of a six-axis robotic manipulator, and exhibits significant advantages in terms of accuracy and robustness.

Introduction: Plant type is an important part of plant phenotypic research, which is of great significance for practical applications such as plant genomics and cultivation knowledge modeling. The existing plant type judgment mainly relies on subjective experience, and lacks automatic analysis and identification methods, which seriously restricts the progress of efficient crop breeding and precision cultivation.Methods: In this study, the digital structure model of cotton plant was constructed based on multi-dimensional vision, and the rapid analysis and identification method of cotton plant type was established. 50 cotton plants were used as experimental objects in this study. Firstly, multi-view images of cotton plants at boll opening stage were collected, and a three-dimensional point cloud model of cotton plants was constructed based on Structure From Motion and Multi View Stereo (SFM-MVS) algorithm. The original cotton point cloud data was preprocessed by coordinate correction, statistical filtering, conditional filtering and down-sampling to obtain a high-quality three-dimensional model. The three-dimensional model is projected in two dimensions to obtain the two-dimensional projection data of cotton plants from multiple perspectives. Secondly, based on the fast convex hull algorithm, the cotton plant two-dimensional convex hull was constructed from multiple perspectives, and the distribution range and corner change rate of each corners of the convex hull were analyzed, and the identification basis of cotton plant type was established.Results: The R2 of plant height and width extracted from the model were greater than 0.90, and RMES were 0.372 cm and 0.387 cm, respectively. When the maximum number of point clouds is 75335, the point cloud reading time, cotton multi-view projection time, and convex hull automatic construction time are 0.402 S, 2.275 S, and 0.018 S, respectively. Finally, the cotton cylinder type classification interval is 0-0.2, and the tower type classification interval is 0.4-1.5.Discussion: The cotton plant type identification method proposed in this study is fast and efficient. It provides a solid theoretical basis and technical support for cotton plant type identification.

In the production process, the manufacturing behavior and all the essential factors are affected by several disturbance factors, showing a complex dynamic fluctuation law. It makes the stability control process a difficult problem in environmental constraints. In this paper, the workshop production process is considered, and an improved coupled map lattice workshop production network state model is proposed. On this basis, the controller with the function of resource load protection is designed, and the network state model of the workshop based on the pinning control is developed. Three kinds of stability control strategies, SAC (Self-adaption Control) , SC (Self-acting Control) and PC (Pinning Control) , are designed based on disturbance triggering behavior and node state transition rules. In addition, two control effect evaluation indexes, RTS (Recovery Time Steps) and NFT (Node Failure Times) are designed. Considering the actual production data of diesel fuel injection system parts production workshop as example, the model is simulated and verified. The results show that under different disturbance intensities, compared with the SAC strategy, the RTS-Average value of the PC strategy is reduced by 29.83% on average, and the NFT-Average values are reduced by 46.9% on average. This proves that the pinning control strategy has certain advantages in controlling time length and propagation scale of disturbance propagation.