scholarly journals Adaptive Trajectory Tracking Control for Quadrotors with Disturbances by Using Generalized Regression Neural Networks

2021 ◽  
Author(s):  
Ivan Lopez-Sanchez ◽  
Francisco Rossomando ◽  
Ricardo Pérez-Alcocer ◽  
Carlos Soria ◽  
Ricardo Carelli ◽  
...  
Keyword(s):  
Neural Networks ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Trajectory Tracking Control ◽  
Generalized Regression Neural Networks ◽  
Generalized Regression

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.

Observer-based adaptive neural sliding mode trajectory tracking control for remotely operated vehicles with thruster constraints

2021 ◽  
pp. 014233122110048
Author(s):  
Zhenzhong Chu ◽  
Yunsai Chen ◽  
Daqi Zhu ◽  
Mingjun Zhang
Keyword(s):  
Neural Networks ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Auxiliary System ◽  
Rbf Neural Networks ◽  
Remotely Operated Vehicle ◽  
Trajectory Tracking Control ◽  
Control Approach ◽  
Saturation Factor

For a class of remotely operated vehicle (ROV) systems with thruster constraints, immeasurable states, and unknown nonlinearities, the trajectory tracking control problem was discussed in this paper. The unknown nonlinear functions were approximated by radial basis function (RBF) neural networks. An adaptive state observer based on neural networks was designed and the immeasurable states were estimated. Considering the problem of thruster saturation constraints, an auxiliary system for saturation compensation was designed and a saturation factor was constructed by the auxiliary system state. By applying the backstepping design method, an adaptive neural sliding mode trajectory tracking controller was developed, in which the saturation factor is contained in adaptive laws. It was proved that the uniformly ultimately bounded (UUB) of trajectory tracking errors can be obtained. Finally, the effectiveness of the proposed trajectory tracking control approach was checked by simulations.

Trajectory Tracking Control Applied to an Electro-Hydraulic Actuator With Uncertain Parameters.

2019 ◽  
Author(s):  
Josiel Gouvêa ◽  
Carlos Alberto Correia ◽  
Alessandro Zachi ◽  
Wallace Moreira Bessa
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Uncertain Parameters ◽  
Hydraulic Actuator ◽  
Trajectory Tracking Control
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