Adaptive Fuzzy Computed-Torque Control for Robot Manipulator with Uncertain Dynamics

Chen, Yuan; Ma, Guangying; Lin, Shudai; Gao, Jun

doi:10.5772/54643

Cited by 31 publications

(19 citation statements)

References 16 publications

(16 reference statements)

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“…Singh and Sukavanam 10 designed a robust adaptive NN-based hybrid position or force control scheme for robot manipulators in the presence of model uncertainties and external disturbances. Chen et al 11 proposed two types of adaptive control scheme combining conventional computed-torque control and different fuzzy compensators for the robust tracking control of robot manipulators with structured and unstructured uncertainties. Sefriti et al 12 provided a method of NN sliding mode control design for the robust tracking control of electrically driven two-links robot manipulators.…”

Section: Robot Hand Controlmentioning

confidence: 99%

Improved adaptive neural network control for humanoid robot hand in workspace

Liu

Zheng

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part C:

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In order to improve the control performance of humanoid robot hand in workspace, an adaptive control method based on improved neural network was proposed. rival-penalized competitive learning and recursive orthogonal least-squares algorithms were applied to reinforce the learning capability of Gaussian radial basis function neural network and realize the real-time of neural network. Moreover, an improved neural network model for humanoid robot hand was established with Ge-Lee matrix and its operator, and a controller was designed. Finally, an example of humanoid robot hand finger was provided. The results showed that the proposed control method could effectively control the unknown nonlinear dynamic properties and load disturbances of the finger with a much smaller tracking errors.

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Section: Robot Hand Controlmentioning

confidence: 99%

Improved adaptive neural network control for humanoid robot hand in workspace

Liu

Zheng

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…In addition to kinematic modeling and trajectory planning, the solving of dynamic tasks of industrial robot arms has become an intensively researched area in the last decade (Al-Mashhadany 2010; Chen et al 2014;Yaskevich 2014). The torque control of dynamically handled robot arms is studied along with the kinematics oriented design of trajectories (Chen et al 2012;Peng et al 2009;Sciavicco and Siciliano 2000). Our research uses the torque control capabilities of the MATLAB Robotics System Toolbox, similarly to the studies of Maged et al (2015) and Piltan et al (2012).…”

Section: Case Studymentioning

confidence: 99%

“…Sci. 2019, 8, FOR PEER REVIEW 13 of 21 dynamically handled robot arms is studied along with the kinematics oriented design of trajectories (Chen et al 2012;Peng et al 2009;Sciavicco and Siciliano 2000). Our research uses the torque control capabilities of the MATLAB Robotics System Toolbox, similarly to the studies of Maged et al (2015) and Piltan et al (2012).…”

Section: The Taskmentioning

confidence: 99%

Economic, Social Impacts and Operation of Smart Factories in Industry 4.0 Focusing on Simulation and Artificial Intelligence of Collaborating Robots

2019

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Smart Factory is a complex system that integrates the main elements of the Industry 4.0 concept (e.g., autonomous robots, Internet of Things, and Big data). In Smart Factories intelligent robots, tools, and smart workpieces communicate and collaborate with each other continuously, which results in self-organizing and self-optimizing production. The significance of Smart Factories is to make production more competitive, efficient, flexible and sustainable. The purpose of the study is not only the introduction of the concept and operation of the Smart Factories, but at the same time to show the application of Simulation and Artificial Intelligence (AI) methods in practice. The significance of the study is that the economic and social operational requirements and impacts of Smart Factories are summarized and the characteristics of the traditional factory and the Smart Factory are compared. The most significant added value of the research is that a real case study is introduced for Simulation of the operation of two collaborating robots applying AI. Quantitative research methods are used, such as numerical and graphical modeling and Simulation, 3D design, furthermore executing Tabu Search in the space of trajectories, but in some aspects the work included fundamental methods, like suggesting an original whip-lashing analog for designing robot trajectories. The conclusion of the case study is that—due to using Simulation and AI methods—the motion path of the robot arm is improved, resulting in more than five percent time-savings, which leads to a significant improvement in productivity. It can be concluded that the establishment of Smart Factories will be essential in the future and the application of Simulation and AI methods for collaborating robots are needed for efficient and optimal operation of production processes.

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“…The control scheme adopted in this study is the computed torque control scheme which has been used widely, in industrial application, for robot manipulator control [4,[12][13][14][15]. It is based on the concept of inner and outer-loop control strategy which provide feedback linearization of the dynamic equation given in (10).…”

Section: B Pid-computed Torque Controlmentioning

confidence: 99%

Intelligent trajectory conversion and inverse dynamic control of a 3-DOF neuro-rehabilitation platform

Sado

Sidek

Yusuf

2015

2015 10th Asian Control Conference (ASCC)

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Tracking a desired trajectory in joint space has been favored in several robot manipulators and end-effector control scheme due to the simplicity and high sampling rate offered by the joint space scheme. This usually require the trajectory conversion process, of the desired position, velocity, and acceleration, from Cartesian space to joint space using conventional inverse kinematics solutions which have been known to have several limitations and which often pose a big challenge, computationally, and even prohibitive, to achieve, for some robot designs. In this study, an intelligent approach to the inverse kinematics problem using adaptive neuro-fuzzy inference system (ANFIS) is proposed for control of a 3-DOF end-effector based neurorehabilitation platform. The joint positions, velocities, andaccelerations are achieved/predicted by means of the ANFIS networks which is trained with data obtained from the forward kinematics, velocity Jacobian and the differential of the velocity kinematics equations. Simulation studies have shown that the proposed intelligent techniques has simplified both the trajectory conversion process and the control framework while tracking is achieve to a high degree of accuracy.

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Adaptive Fuzzy Computed-Torque Control for Robot Manipulator with Uncertain Dynamics

Cited by 31 publications

References 16 publications

Improved adaptive neural network control for humanoid robot hand in workspace

Improved adaptive neural network control for humanoid robot hand in workspace

Economic, Social Impacts and Operation of Smart Factories in Industry 4.0 Focusing on Simulation and Artificial Intelligence of Collaborating Robots

Intelligent trajectory conversion and inverse dynamic control of a 3-DOF neuro-rehabilitation platform

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