Research on Improved PD Control of Flexible Manipulator

In order to achieve accurate trajectory tracking of flexible arm and improve the speed of position tracking, a PD (Proportion Differentiation) controller based on NNs(neural networks) is proposed.-Firstly, the dynamic model of a single-link flexible manipulator with load is established based on lumped method and assumed mode method. Moreover, the optimization of PD parameters is achieved by NNs, which improve the performance of PD control. Finally, simulations are given with the different load under the normal and the improved PD controller, which proves that the PD controller based on NNs has strong robustness and adapability.

[1]  Quan Hu,et al.  Maneuver and Active Vibration Suppression of Free-Flying Space Robot , 2018, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Fumitoshi Matsuno,et al.  Modeling and feedback control of a flexible arm , 1985, J. Field Robotics.

[3]  Ran Wang,et al.  Fuzzy Neural Network PID Control Based on RBF Neural Network for Variable Configuration Spacecraft , 2018, 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC).

[4]  Tingting Zhang,et al.  Hybrid Control Scheme Consisting of Adaptive and Optimal Controllers for Flexible-Base Flexible-Joint Space Manipulator with Uncertain Parameters , 2017, International Conference on Intelligent Human-Machine Systems and Cybernetics.

[5]  Li Chen,et al.  Robust Backstepping Control Based on State Observer and Elastic Vibration Suppressing of Free-Floating Space Manipulator with Flexible Joints , 2012 .

[6]  Shailaja Kurode,et al.  Observer based control of flexible link manipulator using discrete sliding modes , 2013, 2013 IEEE International Conference on Control Applications (CCA).

[7]  Hao Wang,et al.  Study on Deformation and Contact Force Stability of a Novel Flexible Self-Adaptive Picking Manipulator , 2018, 2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC).

[8]  Changyin Sun,et al.  Neural Network Control of a Flexible Robotic Manipulator Using the Lumped Spring-Mass Model , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[9]  Lou Jun-qian Modeling and Active Vibration Control of an Intelligent Flexible Manipulator System , 2014 .

[10]  Hongliang Ren,et al.  Motion Planning Based on Learning From Demonstration for Multiple-Segment Flexible Soft Robots Actuated by Electroactive Polymers , 2016, IEEE Robotics and Automation Letters.