Name
Title | ||
|---|---|---|
CMAC adaptive control of flexible-joint robots using backstepping with tuning functions |
Abstract | ||
|---|---|---|
A neural network used in a direct-adaptive control scheme can achieve trajectory tracking of a (highly) flexible joint robot holding an unknown payload without need for many learning repetitions. A modification of the Lyapunov stable nonlinear control method known as backstepping with tuning functions is derived to achieve this. Specifically, the introduction of appropriate weightings of the different tuning-function terms results in high performance. Also, a robust redesign of the tuning function method is presented to account for the uniform approximation (modeling) error of the neural network. This computationally burdensome method is made practical by taking advantage of the efficient structure of the CMAC neural network. Simulations with a (highly) flexible-joint robot show immediate compensation for a payload with performance nearly recovered after five seconds. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/ROBOT.2004.1307465 | Robotics and Automation, 2004. Proceedings. ICRA '04. 2004 IEEE International Conference |
Keywords | Field | DocType |
Lyapunov methods,adaptive control,cerebellar model arithmetic computers,neurocontrollers,nonlinear control systems,robots,Lyapunov stable nonlinear control,adaptive control,backstepping,cerebellar model articulation controller,flexible joint robot,neural network,trajectory tracking,tuning function method | Lyapunov function,Backstepping,Control theory,Nonlinear control,Control engineering,Adaptive control,Engineering,Robot,Artificial neural network,Trajectory,Payload | Conference |
Volume | ISSN | ISBN |
3 | 1050-4729 | 0-7803-8232-3 |
Citations | PageRank | References |
5 | 0.51 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| C. J. Macnab | 1 | 40 | 10.34 |
| D'Eleuterio, G.M.T. | 2 | 84 | 11.83 |
| Max Q.-H. Meng | 3 | 1477 | 202.72 |