Data-driven mode shape selection and model-based vibration suppression of 3-RRR parallel manipulator with flexible actuation links

The mode shape function is difficult to determine in modeling manipulators with flexible links using the assumed mode method. In this paper, for a planar 3-RRR parallel manipulator with flexible actuation links, we provide a data-driven method to identify the mode shape of the flexible links and propose a model-based controller for the vibration suppression. By deriving the inverse kinematics of the studied mechanism in analytical form, the dynamic model is established by using the assumed mode method. To select the mode shape function, the software of multi-body system dynamics is used to simulate the dynamic behavior of the mechanism, and then the data-driven method which combines the DMD and SINDy algorithms is employed to identify the reasonable mode shape functions for the flexible links. To suppress the vibration of the flexible links, a state observer for the end-effector is constructed by a neural network, and the model-based control law is designed on this basis. In comparison with the model-free controller, the proposed controller with developed dynamic model has promising performance in terms of tracking accuracy and vibration suppression

Stepwise Model Reconstruction of Robotic Manipulator Based on Data-Driven Method

Research on dynamics of robotic manipulators provides promising support for model-based control. In general, rigorous first-principles-based dynamics modeling and accurate identification of mechanism parameters are critical to achieving high precision in model-based control, while data-driven model reconstruction provides alternative approaches of the above process. Taking the level of activation of data as an indicator, this paper classifies the collected robotic manipulator data by means of K-means clustering algorithm. With the fundamental prior knowledge, we find the corresponding dynamical properties behind the classified data separately. Afterwards, the sparse identification of nonlinear dynamics (SINDy) method is used to reconstruct the dynamics model of the robotic manipulator step by step according to the activation level of the classified data. The simulation results show that the proposed method not only reduces the complexity of the basis function library, enabling the application of SINDy method to multi-degree-of-freedom robotic manipulators, but also decreases the influence of data noise on the regression results. Finally, the dynamic control based on the reconfigured model is deployed on the experimental platform, and the experimental results prove the effectiveness of the proposed method.Comment: 8 pages, 11 figure

Dynamic Modeling and Model-Based Control with Neural Network-Based Compensation of a Five Degrees-of-Freedom Parallel Mechanism

In this paper, a spatial parallel mechanism with five degrees of freedom is studied in order to provide a promising dynamic model for the control design. According to the inverse kinematics of the mechanism, the dynamic model is derived by using the Lagrangian method, and the co-simulation using MSC ADAMS and MATLAB/Simulink is adopted to verify the established dynamic model. Then the pre-trained deep neural network (DNN) is introduced to predict the real-time state of the end-effector of the mechanism. Compared to the traditional Newton’s method, the DNN method reduces the cost of the forward kinematics calculation while ensuring prediction accuracy, which enables the dynamic compensation based on feedback signals. Furthermore, the computed torque control with DNN-based feedback compensation is implemented for the trajectory tracking of the mechanism. The simulations show that, in the most complicated case that involves friction and external disturbance, the proposed controller has better tracking performance. The results indicate the necessity of dynamic modeling in the design of control with high precision