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Full-Text Articles in Mechanical Engineering
Optimal And Hierarchical Formation Control For Uav, Xiaohua Wang, S. N. Balakrishnan
Optimal And Hierarchical Formation Control For Uav, Xiaohua Wang, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
In this paper, optimal and hierarchical control concepts are investigated for cooperative formation flying of aircrafts. The airplanes are modeled as point mass and represented by double integrators. And all the planes are considered to be in a plane. For demonstration of the concepts, a task of forming a square from arbitrary initial conditions is presented to four airplanes. The final position that each airplane has to reach is unknown to them. The goal for the team is abstracted in the top layer. The system is modeled as a two layer hierarchical system in which the global information comes from …
Hierarchical Optimal Force-Position Control Of A Turning Process, B. Pandurangan, Robert G. Landers, S. N. Balakrishnan
Hierarchical Optimal Force-Position Control Of A Turning Process, B. Pandurangan, Robert G. Landers, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Machining process control technologies are currently not well integrated into machine tool controllers and, thus, servomechanism dynamics are often ignored when designing and implementing process controllers. In this brief, a hierarchical controller is developed that simultaneously regulates the servomechanism motions and cutting forces in a turning operation. The force process and servomechanism system are separated into high and low levels, respectively, in the hierarchy. The high-level goal is to maintain a constant cutting force to maximize productivity while not violating a spindle power constraint. This goal is systematically propagated to the lower level and combined with the low-level goal to …
Hierarchical Optimal Control Of A Turning Process - Linearization Approach, Anand Dasgupta, B. Pandurangan, Robert G. Landers, S. N. Balakrishnan
Hierarchical Optimal Control Of A Turning Process - Linearization Approach, Anand Dasgupta, B. Pandurangan, Robert G. Landers, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Machining process control technologies are currently not well integrated into machine tool controllers and, thus, servomechanism dynamics are often ignored when designing and implementing process controllers. In this paper, a hierarchical controller is developed that simultaneously regulates the servomechanism positions and cutting forces in a lathing operation. The force process and servomechanism system are separated into high and low levels, respectively, in the hierarchy. The high level goal is to maintain a constant cutting force to maximize productivity while not violating a spindle power constraint. This goal is systematically propagated to the lower level and combined with the low level …
Hierarchical Neurocontroller Architecture For Robotic Manipulation, Xavier J. R. Avula, Luis C. Rabelo
Hierarchical Neurocontroller Architecture For Robotic Manipulation, Xavier J. R. Avula, Luis C. Rabelo
Chemical and Biochemical Engineering Faculty Research & Creative Works
A hierarchical neurocontroller architecture consisting of two artificial neural network systems for the manipulation of a robotic arm is presented. The higher-level network system participates in the delineation of the robot arm workspace and coordinates transformation and the motion decision-making process. The lower-level network provides the correct sequence of control actions. A straightforward example illustrates the architecture''s capabilities, including speed, adaptability, and computational efficiency
Hierarchical Neurocontroller Architecture For Intelligent Robotic Manipulation, Xavier J. R. Avula, Luis C. Rabelo
Hierarchical Neurocontroller Architecture For Intelligent Robotic Manipulation, Xavier J. R. Avula, Luis C. Rabelo
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A hierarchical neurocontroller architecture consisting of two artificial neural network systems for the manipulation of a robotic arm is presented. The higher-level neural system participates in the delineation of the robot arm workspace and coordinates transformation and the motion decision-making process. The lower one provides the correct sequence of control actions. The capabilities, including speed, adaptability, and computational efficiency, of the developed architecture are illustrated by an example.