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Neural learning enhanced teleoperation control of robots with uncertainties

For most teleoperation tasks, it is desired that the telerobot manipulator follows timely and precisely the reference motion set at the master side. However, the conventional control approach may not guarantee the desired performance when there are dynamic uncertainties, especially when there is a notable variation of the telerobot's payload. In this paper, a neural learning based compensation mechanism has been exploited to overcome the effect of the unknown payload as well as uncertainties associated with the telerobot model and the environment. Guaranteed transient performance has been theoretically established. The deterministic learning technique has been employed, such that the neural learned knowledge can be efficiently reused. We performed comparative experiments and demonstrate the effectiveness of the proposed design techniques.