Yunpeng Ju

The three-pronged sliding universal coupling is a new kind of three-pronged coupling. To study its kinematic and dynamic characteristics, directional cosine matrices are utilized as tools to analyze the coordinate systems by establishing simplified geometric models and corresponding motions. Through the analysis of the motions of the input and output shafts and employing the method of single-force element, a set of equilibrium equations for the forces acting on the output shaft and the input shaft are formulated for solution analysis. The research indicates that during the rotation process, there exists a small angular difference between the input shaft and the intermediate shaft, demonstrating the quasi-constant angular velocity characteristics of the new tripod sliding universal coupling. The curves of the forces and torques acting on each component of the coupling approximate sinusoidal curves. The optimal operating angle range for the new tripod universal coupling is at angle , during which the system exhibits good transmission and mechanical performance.

Aiming at the problem of strong coupling and time-delay issues in temperature-pressure control of supercritical CO ₂ extraction system, an inverse decoupling fuzzy active disturbance rejection control method based on pole approximation is proposed. Firstly, by approximating the time-delay link of the controlled subsystem using pole approximation, the matching degree of the two-input signals of the linear extended state observer (LESO) is improved. Secondly, an inverse decoupling time-synchronized active disturbance rejection control method is proposed to address the coupling link of temperature-pressure as well as the complex control environments and numerous disturbances. Furthermore, the controller parameters are tuned using bandwidth method and fuzzy control rules, and theoretical analysis of this method is conducted. In addition, comparative simulations are conducted to validate this method against other approaches. The results indicate that this method exhibits better tracking performance, disturbance rejection capability, and robustness in temperature-pressure control of supercritical CO ₂ extraction systems. Finally, the practical feasibility of this method is verified through experimental validation on a test platform.

Aiming to address the limitations of the traditional rapidly-exploring random tree (RRT) algorithm in robotic arm path planning, such as high randomness, slow planning speed, non-smooth paths, and excessive corners, an improved RRT algorithm incorporating a target bias strategy and artificial potential field approach is proposed. Firstly, this algorithm employs a probabilistic sampling strategy to facilitate quicker growth of the tree structure towards the target point, thus accelerating the planning speed of the RRT algorithm. Subsequently, a repetitive greedy strategy is introduced to reduce redundant nodes in the path and enhance search efficiency. Furthermore, an artificial potential field combined with a target bias strategy is integrated to guide the RRT algorithm towards the target direction. Finally, a third-order B-spline curve optimization is introduced to ensure smoother paths. Considering the complexity of robotic arm operations and uncertainties in system parameters that hinder the establishment of accurate mathematical modeling, a sliding mode control based on radial basis function (RBF) neural networks is proposed. To substantiate the enduring stability of the designed controller, we harness the Lyapunov stability principle. The algorithm validation for path planning and trajectory tracking of the robotic arm are conducted in the MATLAB simulation environment. Experimental results illustrate significant improvements in terms of iteration count, planning speed, path length, and smoothness of the RRT algorithm. Moreover, the motion trajectory of the robotic arm is basically consistent with the expected trajectory.

In order to solve the problem of load variation and nonlinear strong coupling of the quadrotor load unmanned aerial vehicle(UAV), this paper proposes a fuzzy adaptive linear active disturbance rejection control algorithm based on the Kalman filter(KFFA-LADRC). Firstly, according to the established dynamics model of the quadrotor load UAV, the linear extended state observer(LESO) and the controller based on the bandwidth method are designed. Secondly, to enhance system adaptability and robustness, a real-time fuzzy adaptive controller is introduced to dynamically adjust the controller parameters. Furthermore, to tackle uncertainty disturbances arising from sensor noise and unknown external disturbances, the Kalman filter is employed to predict the output state, which provides the optimal estimation input for the LESO. The approach not only realizes the stable control of internal interference, but also reduces the dependence of the Kalman filter on the mathematical model. Finally, simulation results substantiate the efficacy of the proposed KFFA-LADRC, highlighting its robustness and adaptability in accommodating variations in load mass, sensor noise, and external interferences within unfamiliar environments.