Adaptive RBF network control for robot manipulators | ||
| Journal of AI and Data Mining | ||
| مقاله 24، دوره 2، شماره 2، مهر 2014، صفحه 159-166 اصل مقاله (1022.25 K) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2014.246 | ||
| نویسندگان | ||
| M. M. Fateh* 1؛ Seyed M. Ahmadi2؛ S. Khorashadizadeh2 | ||
| 1School of Electrical and Robatics Engineering, Shahrood University of Technology, Iran | ||
| 2Shahrood University | ||
| چکیده | ||
| TThe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function Network as an uncertainty estimator. The proposed network includes a hidden layer with one node, two inputs and a single output. In comparison with other model-free estimators such as multilayer neural networks and fuzzy systems, the proposed estimator is simpler, less computational and more effective. The weights of the RBF network are tuned online using an adaptation law derived by stability analysis. Despite the majority of previous control approaches which are the torque-based control, the proposed control design is the voltage-based control. Simulations and comparisons with a robust neural network control approach show the efficiency of the proposed control approach applied on the articulated robot manipulator driven by permanent magnet DC motors. | ||
| کلیدواژهها | ||
| Adaptive uncertainty estimator؛ RBF network control؛ robust control؛ electrically driven robot manipulators | ||
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آمار تعداد مشاهده مقاله: 4,462 تعداد دریافت فایل اصل مقاله: 4,564 |
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