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In this study, a novel adaptive neural network control scheme is proposed to resolve the tracking control problem for flexible-joint robots with random noises. More precisely, the controlled system in this study is a multi-input and multi-output stochastic nonlinear system, employing the traditional backstepping design to study such a system will greatly increase the amount of calculation. To resolve this problem, the command filtered technology is applied to the adaptive neural network design framework. More importantly, with the aid of the event-triggered strategy, the proposed control algorithm can reduce the communication burden to a certain extent. Besides, the proposed method can also ensure that the tracking error converges to a small neighborhood of the origin. Finally, the simulation example is given to verify the effectiveness of the proposed algorithm.

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This paper investigates the issue of finite-time tracking control for flexible-joint robots. In the design scheme, the unknown continuous function is identified by a fuzzy system. By introducing the command filter technique, “explosion of complexity” problem which arises from repeated differentiation of virtual controllers is avoided. Meanwhile, errors resulting from the first-order filters can be reduced with the introduced compensation signal. Besides, the proposed method ensures that the tracking performance could be achieved within a limited time. Eventually, the simulation is given to demonstrate the effectiveness of the proposed scheme.