No abstract

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.

In this article, we propose a cascade control framework to attenuate the residual vibration of the underactuated manipulator. The control framework is divided into two phases. In the first phase, a path generator trained by the reinforcement learning produces the leading signal for the tracking controller. In the second phase, the leading signal stabilizes the underactuated manipulator, and the adaptive proportional derivative controller is implemented to reduce the vibration. In the process, a novel path planning method is proposed to improve exploration efficiency, and a negative reward is introduced to avoid unsafe strategies and simulation instability. The effectiveness of the proposed control scheme is verified in the simulations of the double pendulum crane and the two-link flexible manipulator.