Distributed Control for Coordinated Tracking of Fixed-Wing Unmanned Aerial Vehicles under Model Uncertainty and Disturbances

In this paper, we consider a control problem where a group of fixed-wing unmanned aerial vehicles (UAVs) with uncertain dynamics tracks the target vehicle cooperatively in the case of external disturbance. Based on the Gaussian process regression, a data-driven model is established, whose uniform error is bounded with probability. Then a learning-based consensus protocol for multi-UAVs is designed. The stability of the system is proven via Lyapunov function, and the tracking error is guaranteed to be bounded with a high probability. Finally, the effectiveness of the proposed method is shown in the numerical simulation.

Distributed Adaptive Coordinated Control of Multiple Euler–Lagrange Systems considering Output Constraints and Time Delays

In this paper, we mainly investigate the coordinated tracking control issues of multiple Euler–Lagrange systems considering constant communication delays and output constraints. Firstly, we devise a distributed observer to ensure that every agent can get the information of the virtual leader. In order to handle uncertain problems, the neural network technique is adopted to estimate the unknown dynamics. Then, we utilize an asymmetric barrier Lyapunov function in the control design to guarantee the output errors satisfy the time-varying output constraints. Two distributed adaptive coordinated control schemes are proposed to guarantee that the followers can track the leader accurately. The first scheme makes the tracking errors between followers and leader be uniformly ultimately bounded, and the second scheme further improves the tracking accuracy. Finally, we utilize a group of manipulator networks simulation experiments to verify the validity of the proposed distributed control laws.