A Sensorless Hand Guiding Scheme Based on Model Identification and Control for Industrial Robot

2019 ◽  
Vol 15 (9) ◽  
pp. 5204-5213 ◽  
Author(s):  
Shaolin Zhang ◽  
Shuo Wang ◽  
Fengshui Jing ◽  
Min Tan
1993 ◽  
Vol 32 (7) ◽  
pp. 1275-1296 ◽  
Author(s):  
James B. Rawlings ◽  
Stephen M. Miller ◽  
Walter R. Witkowski

Author(s):  
Brian A. Weiss ◽  
Guixiu Qiao

Manufacturing work cell operations are typically complex, especially when considering machine tools or industrial robot systems. The execution of these manufacturing operations require the integration of layers of hardware and software. The integration of monitoring, diagnostic, and prognostic technologies (collectively known as prognostics and health management (PHM)) can aid manufacturers in maintaining the performance of machine tools and robot systems by providing intelligence to enhance maintenance and control strategies. PHM can improve asset availability, product quality, and overall productivity. It is unlikely that a manufacturer has the capability to implement PHM in every element of their system. This limitation makes it imperative that the manufacturer understand the complexity of their system. For example, a typical robot systems include a robot, end-effector(s), and any equipment, devices, or sensors required for the robot to perform its task. Each of these elements is bound, both physically and functionally, to one another and thereby holds a measure of influence. This paper focuses on research to decompose a work cell into a hierarchical structure to understand the physical and functional relationships among the system’s critical elements. These relationships will be leveraged to identify areas of risk, which would drive a manufacturer to implement PHM within specific areas.


Author(s):  
Scott Dana ◽  
Joseph Yutzy ◽  
Douglas E. Adams

One of the primary challenges in diagnostic health monitoring and control of wind turbines is compensating for the variable nature of wind loads. Given the sometimes large variations in wind speed, direction, and other operational variables (like wind shear), this paper proposes a data-driven, online rotor model identification approach. A 2 m diameter horizontal axis wind turbine rotor is first tested using experimental modal analysis techniques. Through the use of the Complex Mode Indication Function, the dominant natural frequencies and mode shapes of dynamic response of the rotor are estimated (including repeated and pseudo-repeated roots). The free dynamic response properties of the stationary rotor are compared to the forced response of the operational rotor while it is being subjected to wind and rotordynamic loads. It is demonstrated that both narrowband (rotordynamic) and broadband (wind driven) responses are amplified near resonant frequencies of the rotor. Blade loads in the flap direction of the rotor are also estimated through matrix inversion for a simulated set of rotor blade input forces and for the operational loading state of the wind turbine in a steady state condition. The analytical estimates are shown to be accurate at frequencies for which the ordinary coherence functions are near unity. The loads in operation are shown to be largest at points mid-way along the span of the blade and on one of the three blades suggesting this method could be used for usage monitoring. Based on these results, it is proposed that a measurement of upstream wind velocity will provide enhanced models for diagnostics and control by providing a leading indicator of disturbances in the loads.


1989 ◽  
Vol 42 (4) ◽  
pp. 117-128 ◽  
Author(s):  
S. S. Rao ◽  
P. K. Bhatti

Robotics is a relatively new and evolving technology being applied to manufacturing automation and is fast replacing the special-purpose machines or hard automation as it is often called. Demands for higher productivity, better and uniform quality products, and better working environments are primary reasons for its development. An industrial robot is a multifunctional and computer-controlled mechanical manipulator exhibiting a complex and highly nonlinear behavior. Even though most current robots have anthropomorphic configurations, they have far inferior manipulating abilities compared to humans. A great deal of research effort is presently being directed toward improving their overall performance by using optimal mechanical structures and control strategies. The optimal design of robot manipulators can include kinematic performance characteristics such as workspace, accuracy, repeatability, and redundancy. The static load capacity as well as dynamic criteria such as generalized inertia ellipsoid, dynamic manipulability, and vibratory response have also been considered in the design stages. The optimal control problems typically involve trajectory planning, time-optimal control, energy-optimal control, and mixed-optimal control. The constraints in a robot manipulator design problem usually involve link stresses, actuator torques, elastic deformation of links, and collision avoidance. This paper presents a review of the literature on the issues of optimum design and control of robotic manipulators and also the various optimization techniques currently available for application to robotics.


2015 ◽  
Vol 762 ◽  
pp. 255-260 ◽  
Author(s):  
Mircea Murar ◽  
Stelian Brad

In the context of latest technological revolution, Industry 4.0, connectivity and therefore access and control of cyber-physical systems and resources from any place, at any time by any means represent a technological enabler of crucial importance. The first part of this paperwork contains a brief introduction of cyber-physical systems and IoT concepts, together with a review of major IoT providers. The second part introduces an approach towards achieving connectivity and remote control of task selection for a dual-arm industrial robot using a commercially available IoT infrastructure and technology provided by ioBridge. Within the third part, details about experimental testing and evaluation of the selected solutions are presented. The last part is allocated for conclusions and further research directions.


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