This paper investigates a finite-time formation control problem for multiple networked quadrotors. A novel distributed control approach is presented under the leader-follower formation framework, and the approach is developed based on the finite-time Lyapunov theory and the homogeneous system theory such that all quadrotors form and maintain a desired geometric pattern within finite time while tracking a reference trajectory. The designed control law is composed of a dynamic observer, a position controller and an attitude controller, in which the observer is adopted to provide estimates of the leader quadrotor information for each follower quadrotor, and the controllers are in a cascade structure. It is shown that the finite-time leaderfollower formation of a group of quadrotors can be achieved via the distributed control approach, and the cascade control architecture conforms to quadrotor dynamic characteristics. The constructive procedures on how to synthesize such a control law are also given. The effectiveness of the proposed control approach is verified by the simulation. INDEX TERMS Finite-time control, formation control, unmanned aerial vehicles.

Ternary neural networks (TNNs) are potential for network acceleration by reducing the full-precision weights in network to ternary ones, e.g., {-1,0,1}. However, existing TNNs are mostly calculated based on rule-of-thumb quantization methods by simply thresholding operations, which causes a significant accuracy loss. In this paper, we introduce a stem-residual framework which provides new insight into Ternary quantization, termed Residual Quantization (TRQ), to achieve more powerful TNNs. Rather than directly thresholding operations, TRQ recursively performs quantization on full-precision weights for a refined reconstruction by combining the binarized stem and residual parts. With such a unique quantization process, TRQ endows the quantizer with high flexibility and precision. Our TRQ is generic, which can be easily extended to multiple bits through recursively encoded residual for a better recognition accuracy. Extensive experimental results demonstrate that the proposed method yields great recognition accuracy while being accelerated.

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