Iterative Learning Control of a Robotic Arm Experiment Platform with Input Constraint
This paper addresses the vibration control and the trajectory tracking control of a robotic arm system with input constraint. A hyperbolic tangent function and a saturation function are adopted to tackle the input constraint. By defining a composite energy function, a dual-loop iterative learning co...
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| Published in | IEEE transactions on industrial electronics (1982) Vol. 65; no. 1; pp. 664 - 672 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper addresses the vibration control and the trajectory tracking control of a robotic arm system with input constraint. A hyperbolic tangent function and a saturation function are adopted to tackle the input constraint. By defining a composite energy function, a dual-loop iterative learning control (ILC) law is designed by integrating a restrained learning law and a saturated feedback law. For the closed-loop system, the angle displacements are asymptotically regulated to track a prescribed constant trajectory and the elastic displacements are asymptotically suppressed to zero along the iteration axis. Simulation and experimental results are provided to illustrate the effectiveness of the designed ILC law. |
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| AbstractList | This paper addresses the vibration control and the trajectory tracking control of a robotic arm system with input constraint. A hyperbolic tangent function and a saturation function are adopted to tackle the input constraint. By defining a composite energy function, a dual-loop iterative learning control (ILC) law is designed by integrating a restrained learning law and a saturated feedback law. For the closed-loop system, the angle displacements are asymptotically regulated to track a prescribed constant trajectory and the elastic displacements are asymptotically suppressed to zero along the iteration axis. Simulation and experimental results are provided to illustrate the effectiveness of the designed ILC law. |
| Author | Tingting Meng Wei He |
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| SubjectTerms | Asymptotic properties Boundary control Convergence Elastic properties Feedback control Hyperbolic functions input constraint Iterative learning control iterative learning control (ILC) Laws Learning Manipulators Mathematical model Robot arms Robot control robotic arm system Tracking control Trajectory Trajectory control Vibration control Vibrations |
| Title | Iterative Learning Control of a Robotic Arm Experiment Platform with Input Constraint |
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