Tip tracking control of a rigid-flexible manipulator based on deflection estimation using neural networks: Application to needle insertion

2011 ◽  
Author(s):  
A. N. Irani ◽  
H. A. Talebi
Keyword(s):  
Neural Networks ◽  
Tracking Control ◽  
Needle Insertion ◽  
Flexible Manipulator

Decentralized adaptive tracking control for high-order interconnected stochastic nonlinear time-varying delay systems with stochastic input-to-state stable inverse dynamics by neural networks

2019 ◽  
Vol 41 (13) ◽  
pp. 3612-3625 ◽  
Author(s):  
Wang Qian ◽  
Wang Qiangde ◽  
Wei Chunling ◽  
Zhang Zhengqiang
Keyword(s):  
Neural Networks ◽  
Tracking Control ◽  
Large Scale ◽  
Inverse Dynamics ◽  
Small Neighborhood ◽  
High Order ◽  
Interconnected Systems ◽  
Delay Systems ◽  
Rbf Neural Networks ◽  
Stochastic Input

The paper solves the problem of a decentralized adaptive state-feedback neural tracking control for a class of stochastic nonlinear high-order interconnected systems. Under the assumptions that the inverse dynamics of the subsystems are stochastic input-to-state stable (SISS) and for the controller design, Radial basis function (RBF) neural networks (NN) are used to cope with the packaged unknown system dynamics and stochastic uncertainties. Besides, the appropriate Lyapunov-Krosovskii functions and parameters are constructed for a class of large-scale high-order stochastic nonlinear strong interconnected systems with inverse dynamics. It has been proved that the actual controller can be designed so as to guarantee that all the signals in the closed-loop systems remain semi-globally uniformly ultimately bounded, and the tracking errors eventually converge in the small neighborhood of origin. Simulation example has been proposed to show the effectiveness of our results.

A Robust Trajectory Tracking Control Scheme of Two-Link Flexible Manipulator

2011 ◽  
Vol 328-330 ◽  
pp. 2108-2112
Author(s):  
Jing Shuang Lu ◽  
Chun Mei Du ◽  
Rui Zhou ◽  
Na Li
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Vertical Plane ◽  
Flexible Manipulator ◽  
System Model ◽  
Error Signal ◽  
Dynamics Model ◽  
Trajectory Tracking Control ◽  
Control Scheme ◽  
Simulation Results

A simple dynamics model is established based on the two-link flexible manipulator moving within the vertical plane, and a robust simple control scheme is put forward. The advantages of this scheme are simple and good robustness. Only the error signal is needed when designing the control scheme and the acquirement of control signal does not depend on the system model. The simulation results show that this method has a good robustness and stability.

Control of a two-link flexible manipulator using neural networks

2007 ◽  
Author(s):  
Minoru Sasaki ◽  
Haruki Murasawa ◽  
Satoshi Ito
Keyword(s):  
Neural Networks ◽  
Flexible Manipulator

Dynamic trajectory-tracking control method of robotic transcranial magnetic stimulation with end-effector gravity compensation based on force sensors

2018 ◽  
Vol 45 (6) ◽  
pp. 722-731
Author(s):  
ZeCai Lin ◽  
Wang Xin ◽  
Jian Yang ◽  
Zhang QingPei ◽  
Lu ZongJie
Keyword(s):  
Neural Networks ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Magnetic Stimulation ◽  
Control Method ◽  
Gravity Compensation ◽  
Trajectory Tracking Control ◽  
End Effector ◽  
Content Type ◽  
Dynamic Trajectory

Purpose This paper aims to propose a dynamic trajectory-tracking control method for robotic transcranial magnetic stimulation (TMS), based on force sensors, which follows the dynamic movement of the patient’s head during treatment. Design/methodology/approach First, end-effector gravity compensation methods based on kinematics and back-propagation (BP) neural networks are presented and compared. Second, a dynamic trajectory-tracking method is tested using force/position hybrid control. Finally, an adaptive proportional-derivative (PD) controller is adopted to make pose corrections. All the methods are designed for robotic TMS systems. Findings The gravity compensation method, based on BP neural networks for end-effectors, is proposed due to the different zero drifts in different sensors’ postures, modeling errors in the kinematics and the effects of other uncertain factors on the accuracy of gravity compensation. Results indicate that accuracy is improved using this method and the computing load is significantly reduced. The pose correction of the robotic manipulator can be achieved using an adaptive PD hybrid force/position controller. Originality/value A BP neural network-based gravity compensation method is developed and compared with traditional kinematic methods. The adaptive PD control strategy is designed to make the necessary pose corrections more effectively. The proposed methods are verified on a robotic TMS system. Experimental results indicate that the system is effective and flexible for the dynamic trajectory-tracking control of manipulator applications.

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