A new adaptive saturation Integral Sliding Mode Controller (ADSA-ISMC) is presented to solve the speed fluctuation problem caused by a small brushless DC motor (S-BLDC) under variable load. First, according to the characteristics of S-BLDC and Kalman filter, a Kalman-based S-BLDC load disturbance observer is established, and an adaptive integrated sliding mode controller (AD-ISMC) is constructed based on the motor load obtained by the observer. Then, we introduce the saturation function into the input error link of the traditional integral sliding mode function, to propose a new ADSA-ISMC to improve the windup phenomenon produced by the integral sliding mode controller and prove the stability and convergence of the controller by the Lyapunov method. Finally, the experimental results show that the Kalman load disturbance observer can observe the motor load disturbance well. In addition, when a load of S-BLDC changes, many speed control performance indexes of ADSA-ISMC are significantly better than SMC and AD-ISMC. Theoretical analysis and experimental results show that the ADSA-ISMC proposed in this paper shows excellent control performance in S-BLDC speed control.

Aiming at the problems of model uncertainties and other external interference in trajectory tracking control of n-degree of freedom manipulators, a non-singular terminal sliding mode controller with nonlinear disturbance observer (NDO–NTSMC) trajectory tracking method is proposed. A nonlinear disturbance observer (NDO) is designed to forecast and compensate the system external interference, and a nonlinear gain is designed to make the observer error achieve the expected exponential convergence rate so that the feedforward compensation control is realized. Then, a non-singular terminal sliding mode controller (NTSMC) built on nonlinear sliding surface is designed to surmount the singularity fault of classic terminal sliding mode controller (TSMC). Therefore, the time required from any initial state to reach the equilibrium point is finite. In addition, the redesign of the sliding surface ensures the tracking accuracy rate of uncertain systems. Then, based on Lyapunov principle, we complete the stability analysis. Finally, the method is applied to a 2-DOF robotic manipulator model compared with other methods. In the simulation, the manipulator needs to track a continuous trajectory under the condition of joint friction disturbance. The simulation result shows that the torque output of the designed method is chattering-free and smooth, and the tracking effect is precise. Simulation results indicate that the proposed controller has the advantages of excellent tracking performance, strong robustness, and a fast response.

At present, human action recognition can be used in all walks of life, because the skeleton can transmit intuitive information without being affected by environmental factors. However, it only focuses on local information. In order to solve these problems, we introduce a neural network model for human body recognition in this paper. We propose a model named NEW-STGCN-CA. The model is based on a spatial–temporal graph convolution network (ST-GCN), which contains a new partition strategy and coordination attention (CA) mechanism. By integrating the CA attention mechanism model, we enable the network to focus on input-related information, ignore unnecessary information, and prevent information loss. Second, a new partitioning strategy is proposed for the sampled regions, which is used to enhance the connection between local information and global information. We proved that the Top-1 accuracy of the NEW-STGCN-CA model in the NTU-RGB+D 60 dataset reached 84.86%, which was 1.7% higher than the original model; the accuracy of Top-1 on the Kinetics-Skeleton dataset reached 32.40%, which was 3.17% higher than the original model. The experimental results show that NEW-STGCN-CA can effectively improve the algorithm’s accuracy while also having high robustness and performance.