Quantized Consensus Control for Multi-UAVs Based on Prescribed Performance

A distributed adaptive control approach based on a prescribed performance is proposed for multiple unmanned aerial vehicles (multi-UAVs) with disturbances and input quantization. The tracking error is converted into the consensus error by relying on the communication topology so that the distributed controller can be implemented. The radial basic function (RBF) neural network with composite learning is used to estimate the unknown nonlinear functions, and adaptive compensation terms are applied to compensate for the errors caused by filters and disturbances. A hysteretic quantizer is introduced to process the control input signal to relax the channel burden, and an estimator is implemented to solve the problem where the quantizer parameters are unknown. An improved Barrier Lyapunov function (BLF) is developed to ensure that the consensus error satisfies the prescribed performance requirements. Stability analysis proves that the tracking error and all signals in the closed-loop systems are bounded. Finally, all follower UAVs can track the virtual leader and maintain the given formation. A numerical simulation is provided to validate the effectiveness of the proposed control approach.