دانلود رایگان مقاله انگلیسی کنترل دستکاری ربات با استفاده از شبکه های عصبی: یک نظرسنجی - الزویر 2018

abstract

Robot manipulators are playing increasingly significant roles in scientific researches and engineering applications in recent years. Using manipulators to save labors and increase accuracies are becoming common practices in industry. Neural networks, which feature high-speed parallel distributed processing, and can be readily implemented by hardware, have been recognized as a powerful tool for real-time processing and successfully applied widely in various control systems. Particularly, using neural networks for the control of robot manipulators have attracted much attention and various related schemes and methods have been proposed and investigated. In this paper, we make a review of research progress about controlling manipulators by means of neural networks. The problem foundation of manipulator control and the theoretical ideas on using neural network to solve this problem are first analyzed and then the latest progresses on this topic in recent years are described and reviewed in detail. Finally, toward practical applications, some potential directions possibly deserving investigation in controlling manipulators by neural networks are pointed out and discussed.

6. Conclusions

In summary, great achievements for the control of manipulators by means of neural networks have been gained in the last two decades. However, there are still many new problems to be solved. All these future developments will accompany the development of the advanced manufacture and material for various kinds of robot manipulators as well as the mathematical theory for constructing and developing neural networks. Keeping in mind, different kinds of neural networks have their own feasible ranges, and one cannot expect that only a few existing results on neural networks can tackle all the control problems existing in different manipulators with different tasks. Every class of neural networks, for example, feedforward neural networks, recurrent neural networks, dual neural networks as well as their modifications, has their own advantages, which has considered different tradeoffs between computational complexity and efficiency for the control of robot manipulators. Finally, two possible future research directions on control of robot manipulators using neural networks are pointed out, which may open a door to the research on this topic.