| contributor author | G. A. Larsen | |
| contributor author | A. Donmez | |
| contributor author | S. Cetinkunt | |
| date accessioned | 2017-05-08T23:46:47Z | |
| date available | 2017-05-08T23:46:47Z | |
| date copyright | September, 1995 | |
| date issued | 1995 | |
| identifier issn | 0022-0434 | |
| identifier other | JDSMAA-26216#415_1.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/115060 | |
| description abstract | Precision requirements in ultra-precision machining are often given in the order of micrometers or sub-micrometers. Machining at these levels requires precise control of the position and speed of the machine tool axes. Furthermore, in machining of brittle materials, extremely low feed rates of the machine tool axes are required. At these low feed rates there is a large and erratic friction characteristic in the drive system which standard PID controllers are unable to deal with. In order to achieve the desired accuracies, friction must be accurately compensated in the real-time servo control algorithm. A learning controller based on the CMAC algorithm is studied for this task. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | CMAC Neural Network Control for High Precision Motion Control in the Presence of Large Friction | |
| type | Journal Paper | |
| journal volume | 117 | |
| journal issue | 3 | |
| journal title | Journal of Dynamic Systems, Measurement, and Control | |
| identifier doi | 10.1115/1.2799133 | |
| journal fristpage | 415 | |
| journal lastpage | 420 | |
| identifier eissn | 1528-9028 | |
| keywords | Friction | |
| keywords | Motion control | |
| keywords | Accuracy | |
| keywords | Artificial neural networks | |
| keywords | Machining | |
| keywords | Machine tools | |
| keywords | Control equipment | |
| keywords | Algorithms | |
| keywords | Servomechanisms AND Brittleness | |
| tree | Journal of Dynamic Systems, Measurement, and Control:;1995:;volume( 117 ):;issue: 003 | |
| contenttype | Fulltext | |