Design Method of Intelligent Ropeway Type Line Changing Robot Based on Lifting Force Control and Synovial Film Controller

Abstract

Aiming at the problems of low efficiency, reliability, and safety of manual construction for demolition of old lines, a design method of an intelligent ropeway type line changing robot based on lifting force control and synovial film controller is proposed. First, the mechanical model of robot load and line sag is established, and the sag of the overhead line where the robot is located is used to calculate the jacking force that the jacking device needs to provide to the robot. Then, by introducing the radial basis function (RBF) neural network adaptive algorithm into the synovial controller, an adaptive sliding mode position control algorithm based on the RBF neural network is designed to achieve high-precision motion control of the robot in complex operating environments. Finally, based on the compactness, weight, and reliability of the robot, the optimal design is carried out from four aspects of topology, size, shape and morphology, and the design scheme of the robot for wire removal is proposed, and the robot is produced. The developed robot and the other three robots are compared and analyzed under the same conditions through simulation experiments. The results show that the maximum operating time, maximum climbing angle, and maximum traveling speed of the robot developed in this study are all optimal, which are 45 min, 10°, and 1 m/s respectively, and the performance is better than the other three comparison algorithms.

Robust trajectory tracking control for collaborative robots based on learning feedback gain self-adjustment

“…Collaborative robot (CoBot) is a kind of robot with high flexibility, security, and cooperation, which can work in the same workspace as human beings and complete tasks together (Lytridis et al, 2021;Fan et al, 2023;Zhai et al, 2022). Compared with traditional industrial robots, collaborative robots are adaptive and customizable, which can quickly adapt to diverse production and service needs, and well meet the requirements of flexible customization in intelligent manufacturing.…”

“…tracking tasks more effectively. Academic researchers have made considerable efforts to enhance the performance of collaborative robots' trajectory tracking, such as computing torque control (Ghediri et al, 2022;Ramuzat et al, 2022), disturbance observer (Jie et al, 2020;Regmi et al, 2022;Salman et al, 2023), adaptive immunity control (ADRC; Khaled et al, 2020;Ma et al, 2021;Guo et al, 2016;Gaidhane et al, 2023), fuzzy control (Xian et al, 2023;Jiang et al, 2020), and sliding mode control (SMC; Lv, 2020;Duan et al, 2022;Zhao et al, 2022). These control algorithms can greatly improve the trajectory tracking performance of the robot to some extent, but they also have some shortcomings, for example, computational torque control and optimal control cannot handle uncertainties such as parameters, external disturbances, and variable loads well.…”