scholarly journals Fuzzy Torque Control of the Bionic Flexible Manipulator Actuated by Pneumatic Muscle Actuators

2019 ◽  
Vol 32 (1) ◽  
Author(s):  
Kai Liu ◽  
Yining Chen ◽  
Jiaqi Xu ◽  
Yang Wu ◽  
Yonghua Lu ◽  
...  
Keyword(s):  
Trajectory Tracking ◽  
Control Algorithm ◽  
Kinematic Model ◽  
Torque Control ◽  
Tracking Performance ◽  
Flexible Manipulator ◽  
Strong Nonlinearity ◽  
Tracking Accuracy ◽  
Pneumatic Muscle ◽  
Computed Torque

Abstract A bionic flexible manipulator driven by pneumatic muscle actuator (PMA) can better reflect the flexibility of the mechanism. Current research on PMA mainly focuses on the modeling and control strategy of the pneumatic manipulator system. Compared with traditional electro-hydraulic actuators, the structure of PMA is simple but possesses strong nonlinearity and flexibility, which leads to the difficulty in improving the control accuracy. In this paper, the configuration design of a bionic flexible manipulator is performed by human physiological map, the kinematic model of the mechanism is established, and the dynamics is analyzed by Lagrange method. A fuzzy torque control algorithm is designed based on the computed torque method, where the fuzzy control theory is applied. The hardware experimental system is established. Through the co-simulation contrast test on MATLAB and ADAMS, it is found that the fuzzy torque control algorithm has better tracking performance and higher tracking accuracy than the computed torque method, and is applied to the entity control test. The experimental results show that the fuzzy torque algorithm can better control the trajectory tracking movement of the bionic flexible manipulator. This research proposes a fuzzy torque control algorithm which can compensate the error more effectively, and possesses the preferred trajectory tracking performance.

scholarly journals The use of a cascaded Kinect and electromyography gesture decoding algorithm in an initial robot-aided hand neurorehabilitation

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401775196 ◽  
Author(s):  
Ping Wang ◽  
Yabo Wang ◽  
He Huang ◽  
Feng Ru ◽  
Quan Pan
Keyword(s):  
Control Algorithm ◽  
Motion Tracking ◽  
Simulation Experiment ◽  
Tracking Error ◽  
Torque Control ◽  
Tracking Accuracy ◽  
Rehabilitation Robots ◽  
Machine Interface ◽  
Repetitive Activities ◽  
Active Assistance

In order to improve the neurological recovery of hand neurorehabilitation, target-oriented, intensive, repetitive activities of daily living are used, such as training with recognition of hand gestures during robot-aided exercise. In this article, a cascade control algorithm integrating electromyography bio-feedback into hand gesture recognition is proposed. The outer loop is the trajectory motion tracking with Kinect-based gesture decoding classifier, and the inner loop is torque control with electromyography bio-feedback in the real time. This proposed method improves the tracking accuracy. The tracking error is effectively reduced from 70.56 to 28.07 in the simulation experiment. The initial test proves that the proposed method with additional torque control allows active assistance on the human–machine interface of other rehabilitation robots in future.

A New Control Approach to Nonlinear Systems Undergoing Changes in a System Parameter

2000 ◽  
Author(s):  
Hyuk C. Nho ◽  
Peter Meckl
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Sudden Change ◽  
Torque Control ◽  
Tracking Performance ◽  
Control Architecture ◽  
Model Based ◽  
Payload Uncertainty ◽  
Payload Mass ◽  
Computed Torque

Abstract Conventional model-based computed torque control fails to produce good trajectory tracking performance in the presence of payload uncertainty and modeling error. The problem is how to provide accurate dynamics information to the controller. A new control architecture that incorporates a neural network, fuzzy logic and a simple proportional-derivative (PD) controller is proposed to control an articulated robot carrying a variable payload. A feedforward (multilayer) neural network is trained off-line to capture the nonlinear inverse dynamics of the system. The network is placed in the feedforward path to minimize tracking error. The network receives the same input signals as conventional computed torque as well as the payload mass estimate, which comes from a fuzzy logic mass estimator. The fuzzy logic, trained off-line to optimize the membership function, is developed to estimate the changing payload mass. The fuzzy logic estimator is based on joint acceleration error to improve the speed of detection and estimation of payload mass change. The effectiveness of the proposed architecture is demonstrated by experiment on a two-link planar manipulator with changing payload mass. Experiment results show that this control architecture achieves excellent tracking performance in the presence of payload uncertainty. The results of the control architecture are also compared with those of a model-based control architecture. This approach can be employed in any nonlinear mechanical system with a sudden change in a parameter.

Computed Torque Control of a Caterpillar Robot Manipulator Using Neural Network

2016 ◽  
Vol 15 ◽  
pp. 106-118 ◽  
Author(s):  
Mehran Rahmani ◽  
Ahmad Ghanbari
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Control Method ◽  
Robot Manipulator ◽  
Setting Time ◽  
Torque Control ◽  
Learning Ability ◽  
Control Effort ◽  
Better Than ◽  
Computed Torque

This paper presents a neural computed torque controller, which employs to a Caterpillar robot manipulator. A description to exert a control method application neural network for nonlinear PD computed torque controller to a two sub-mechanisms Caterpillar robot manipulator. A nonlinear PD computed torque controller is obtained via utilizing a popular computed torque controller and using neural networks. The proposed controller has some advantages such as low control effort, high trajectory tracking and learning ability. The joint angles of two sub-mechanisms have been obtained by using the numerical simulations. The discovered figures show that the performance of the neural computed torque controller is better than a conventional computed torque controller in trajectory tracking and reduction of setting time. Finally, snapshots of gain sequences are demonstrated.

Dynamic Modeling and Computed Torque Control of Flexure Jointed TVC Systems

2018 ◽  
Author(s):  
Ahmet Aydogan ◽  
Eric Rogers ◽  
Ozgur Hasturk
Keyword(s):  
Dynamic Modeling ◽  
Vertical Direction ◽  
Vertical Motion ◽  
Torque Control ◽  
Tracking Accuracy ◽  
Computed Torque Control ◽  
Early Results ◽  
Simulation Based ◽  
Thrust Vector ◽  
Computed Torque

Thrust Vector Control (TVC) is one means of controlling air vehicles to follow a desired flight path where, in particular, those that are flexure jointed are currently the most commonly used. Often, dynamic modeling of such systems is for the case where a universal gimbal joint is present, which neglects uncertainties in the dynamics, such as vertical motion of the pivot point of nozzle and misalignment. This paper gives early results on a new approach to dynamic modeling of TVC systems that includes one more degree of freedom compared to previously reported models and also enables the flexure jointed structure to move along vertical direction on the flight axis. A Computed Torque Control Law (CTCL) is then designed for the new resulting model with the potential for higher tracking accuracy and lower feedback gains. A simulation based case study is given to demonstrate the new design.

Adaptive Control of Nonlinear Dynamic SCARA Type of Manipulators

Robotica ◽  
1991 ◽  
Vol 9 (3) ◽  
pp. 319-326 ◽  
Author(s):  
Devendra P. Garg
Keyword(s):  
Adaptive Control ◽  
Trajectory Tracking ◽  
Control Strategy ◽  
Nonlinear Dynamic ◽  
Torque Control ◽  
Control Action ◽  
Relative Importance ◽  
Computed Torque Control ◽  
Torque Control Strategy ◽  
Computed Torque

SUMMARYThis paper deals with an investigation of the relative importance of robotic characteristics typically associated with nonlinear manipulators. An IBM 7540 SCARA type of robot is used for simulation, and results are presented for decentralized proportional plus derivative control action applied to individual robot joints, and the use of an adaptive computed torque control strategy is illustrated. The influence of variations in payload and robot parameters on trajectory tracking is also shown.

scholarly journals Model-assisted extended state observer-based computed torque control for trajectory tracking of uncertain robotic manipulator systems

2018 ◽  
Vol 15 (5) ◽  
pp. 172988141880173 ◽  
Author(s):  
Chao Chen ◽  
Chengrui Zhang ◽  
Tianliang Hu ◽  
Hepeng Ni ◽  
WeiChao Luo
Keyword(s):  
Trajectory Tracking ◽  
Robotic Manipulators ◽  
State Observer ◽  
Torque Control ◽  
Extended State Observer ◽  
Effective Control ◽  
Extended State ◽  
Uncertain Dynamics ◽  
Computed Torque Control ◽  
Computed Torque

Computed torque control is an effective control scheme for trajectory tracking of robotic manipulators. However, computed torque control requires precise dynamic models of robotic manipulators and is severely affected by uncertain dynamics. Thus, a new scheme that combines a computed torque control and a novel model-assisted extended state observer is developed for the robust tracking control of robotic manipulators subject to structured and unstructured uncertainties to overcome the disadvantages of computed torque control and exploit its merits. The model-assisted extended state observer is designed to estimate and compensate these uncertain dynamics as a lumped disturbance online, which further improves the disturbance rejection property of a robotic system. Global uniform ultimate boundedness stability with an exponential convergence of a closed-loop system is verified through Lyapunov method. Simulations are performed on a two degree-of-freedom manipulator to verify the effectiveness and superiority of the proposed controller.

Development of a model reference computed torque controller for a human lower extremity exoskeleton robot

2021 ◽  
pp. 095965182110090
Author(s):  
SK Hasan ◽  
Anoop K Dhingra
Keyword(s):  
Control System ◽  
Physical Therapy ◽  
Lower Extremity ◽  
Torque Control ◽  
Joint Torque ◽  
Tracking Performance ◽  
Model Reference ◽  
Exoskeleton Robot ◽  
The Stability ◽  
Computed Torque

Exoskeleton robot–based neurorehabilitation has received a lot of attention recently due to positive evidence supporting its ability to provide different forms of physical therapy and in helping evaluate the patient recovery rate accurately. The performance of exoskeleton robot–based physical therapy depends on the accuracy of the motion control system. While the computed torque control scheme based on inverse dynamics is ideal from a theoretical perspective, the stability and tracking performance strongly depends on the model accuracy. Expecting a deterministic payload for a rehabilitation robot is impractical, which makes the computed torque controller unrealistic for such an application. In this article, a 7-degree-of-freedom human lower extremity dynamic model is developed using the Lagrange method and a novel Model Reference Computed Torque Controller is utilized for control. The computed torque controller is used to estimate the joint torque requirements for tracking a trajectory. Calculated joint torques are applied to a similarly structured plant with different parameters. The deviation of the plant from the model is calculated. A proportional–integral–derivative controller is employed to force the plant to behave like the robot model. A realistic friction model is incorporated to simulate joint friction in the plant. The stability and tracking performance of the control system is presented for sequential as well as simultaneous joint movements. To verify the robustness of the developed controller, analysis of variance statistical technique is used.

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