Neural Network-Based Active Vibration Suppression and Accurate Trajectory Tracking for Flexible Robotic Arms

Due to the expansion of the operating space of the mobile robotic arm and the improvement of the mobile platform’s ability to interact with the environment, it is widely used in the fields of transportation, search and rescue, security and surveillance. The study firstly designs the vibration suppression scheme of the robotic arm, uses the observer for observing the modal variables of the system, and observes the external perturbations as the state variables of the system, and then proposes the residual vibration suppression strategy based on the input shaping and the adaptive robust control method based on neural network, which utilizes the neural network to approximate the unknown nonlinearities in the controller and the observer, and designs the speed based on the adaptive neural network observer and controller, eliminating the influence of external factors on the control system and improving the control accuracy. Finally, a robotic arm experimental platform is built, and the control algorithm is verified through a large number of simulation experiments, and the research results show that the speed has a significant effect on the dynamic characteristics of the flexible arm, and the vibration curve range is smaller in the case of increasing the controller, and compared with the other four control methods, the tracking performance of the control method in this paper is improved by 15.8% to 65.4%, and it is able to better inhibit the elastic vibration of the flexible arm The tracking performance is improved by 15% to 65.4% compared with the other four control methods, which can better suppress the elastic vibration of flexible arm.