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.

Trajectory tracking control of robotic transcranial magnetic stimulation

2019 ◽  
Vol 12 (2) ◽  
pp. 245-259 ◽  
Author(s):  
Zecai Lin ◽  
Xin Wang ◽  
Jian Yang
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Augmented Reality ◽  
Trajectory Tracking ◽  
Force Control ◽  
Tracking Control ◽  
Magnetic Stimulation ◽  
Control Method ◽  
Trajectory Tracking Control ◽  
Content Type ◽  
Augmented Reality Technology

Purpose Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique. Based on the unique functions of TMS, it has been widely used in clinical, scientific research and other fields. Nowadays, the robot-assisted automatic TMS has become the trend. In order to simplify the operation procedures of robotic TMS and reduce the costs, the purpose of this paper is to apply the marker-based augmented-reality technology to robotic TMS system. Design/methodology/approach By using the marker of ARToolKitPlus library and monocular camera, the patient’s head is positioned in real time. Furthermore, the force control is applied to keep contact between the coil and subject’s head. Findings The authors fuse with visual positioning which is based on augmented-reality and force-control technologies to track the movements of the patient’s head, bring the coil closer to the stimulation site and increase treatment effects. Experimental results indicate that the trajectory tracking control of robotic TMS system designed in this paper is practical and flexible. Originality/value This paper provides a trajectory tracking control method for the robotic TMS. The marker-based augmented-reality technology is implemented which simplifies the operation procedures of robotic TMS as well as reduce the costs. During the treatment process, the patients would wear an AR glasses, which can help patients relax through virtual scenes and reduce the uncomfortableness produce by treatment.

Robust finite-time trajectory tracking control for a space manipulator with parametric uncertainties and external disturbances

2021 ◽  
pp. 095441002110147
Author(s):  
Qijia Yao
Keyword(s):  
Trajectory Tracking ◽  
Finite Time ◽  
Tracking Control ◽  
Disturbance Observer ◽  
Control Method ◽  
Parametric Uncertainties ◽  
External Disturbances ◽  
Trajectory Tracking Control ◽  
Space Manipulator ◽  
Tracking Controller

Space manipulator is considered as one of the most promising technologies for future space activities owing to its important role in various on-orbit serving missions. In this study, a robust finite-time tracking control method is proposed for the rapid and accurate trajectory tracking control of an attitude-controlled free-flying space manipulator in the presence of parametric uncertainties and external disturbances. First, a baseline finite-time tracking controller is designed to track the desired position of the space manipulator based on the homogeneous method. Then, a finite-time disturbance observer is designed to accurately estimate the lumped uncertainties. Finally, a robust finite-time tracking controller is developed by integrating the baseline finite-time tracking controller with the finite-time disturbance observer. Rigorous theoretical analysis for the global finite-time stability of the whole closed-loop system is provided. The proposed robust finite-time tracking controller has a relatively simple structure and can guarantee the position and velocity tracking errors converge to zero in finite time even subject to lumped uncertainties. To the best of the authors’ knowledge, there are really limited existing controllers can achieve such excellent performance under the same conditions. Numerical simulations illustrate the effectiveness and superiority of the proposed control method.

Real-time trajectory tracking control of Stewart platform using fractional order fuzzy PID controller optimized by particle swarm algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zafer Bingul ◽  
Oguzhan Karahan
Keyword(s):  
Dynamic Model ◽  
Fractional Order ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Fuzzy Pid ◽  
Simulation Environment ◽  
Trajectory Tracking Control ◽  
Content Type ◽  
Control Approach ◽  
Coupling Dynamic

Purpose The purpose of this paper is to address a fractional order fuzzy PID (FOFPID) control approach for solving the problem of enhancing high precision tracking performance and robustness against to different reference trajectories of a 6-DOF Stewart Platform (SP) in joint space. Design/methodology/approach For the optimal design of the proposed control approach, tuning of the controller parameters including membership functions and input-output scaling factors along with the fractional order rate of error and fractional order integral of control signal is tuned with off-line by using particle swarm optimization (PSO) algorithm. For achieving this off-line optimization in the simulation environment, very accurate dynamic model of SP which has more complicated dynamical characteristics is required. Therefore, the coupling dynamic model of multi-rigid-body system is developed by Lagrange-Euler approach. For completeness, the mathematical model of the actuators is established and integrated with the dynamic model of SP mechanical system to state electromechanical coupling dynamic model. To study the validness of the proposed FOFPID controller, using this accurate dynamic model of the SP, other published control approaches such as the PID control, FOPID control and fuzzy PID control are also optimized with PSO in simulation environment. To compare trajectory tracking performance and effectiveness of the tuned controllers, the real time validation trajectory tracking experiments are conducted using the experimental setup of the SP by applying the optimum parameters of the controllers. The credibility of the results obtained with the controllers tuned in simulation environment is examined using statistical analysis. Findings The experimental results clearly demonstrate that the proposed optimal FOFPID controller can improve the control performance and reduce reference trajectory tracking errors of the SP. Also, the proposed PSO optimized FOFPID control strategy outperforms other control schemes in terms of the different difficulty levels of the given trajectories. Originality/value To the best of the authors’ knowledge, such a motion controller incorporating the fractional order approach to the fuzzy is first time applied in trajectory tracking control of SP.

A self-tuning trajectory tracking controller for wheeled mobile robots

2019 ◽  
Vol 46 (6) ◽  
pp. 828-838 ◽  
Author(s):  
Pouya Panahandeh ◽  
Khalil Alipour ◽  
Bahram Tarvirdizadeh ◽  
Alireza Hadi
Keyword(s):  
Neural Networks ◽  
Convergence Rate ◽  
Mobile Robots ◽  
Trajectory Tracking ◽  
Suggested Method ◽  
Wheeled Mobile Robots ◽  
Trajectory Tracking Control ◽  
Content Type ◽  
Tracking Controller ◽  
Trajectory Tracking Controller

Purpose Trajectory tracking is a common problem in the field of mobile robots which has attracted a lot of attention in the past two decades. Therefore, besides the search for new controllers to achieve a better performance, improvement and optimization of existing control rules are necessary. Trajectory tracking control laws usually contain constant gains which affect greatly the robot’s performance. Design/methodology/approach In this paper, a method based on neural networks is introduced to automatically upgrade the gains of a well-known trajectory tracking controller of wheeled mobile robots. The suggested method speeds up the convergence rate of the main controller. Findings Simulations and experiments are performed to assess the ability of the suggested scheme. The obtained results show the effectiveness of the proposed method. Originality/value In this paper, a method based on neural networks is introduced to automatically upgrade the gains of a well-known trajectory tracking controller of wheeled mobile robots. The suggested method speeds up the convergence rate of the main controller.

Dynamic modeling and trajectory tracking control method of segmented linkage cable-driven hyper-redundant robot

2020 ◽  
Vol 101 (1) ◽  
pp. 233-253
Author(s):  
Jianqing Peng ◽  
Wenfu Xu ◽  
Taiwei Yang ◽  
Zhonghua Hu ◽  
Bin Liang
Keyword(s):  
Dynamic Modeling ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Control Method ◽  
Trajectory Tracking Control ◽  
Redundant Robot
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