In this article, a fixed-time disturbance observer-based model predictive control algorithm is proposed for trajectory tracking of quadrotor in the presence of disturbances. First, a novel multivariable fixed-time disturbance observer is proposed to estimate the lumped disturbances. The bi-limit homogeneity and Lyapunov techniques are employed to ensure the convergence of estimation error within a fixed convergence time, independent of the initial estimation error. Then, an observer-based model predictive control strategy is formulated to achieve robust trajectory tracking of quadrotor, attenuating the lumped disturbances and model uncertainties. Finally, simulations and real-world experiments are provided to illustrate the effectiveness of the proposed method.

This article focuses on event-triggered consensus control for multiagent systems subject to sensor faults or noises. First, a descriptor state observer with a low-pass filtering characteristic being developed for each agent using output information. The convergence regions of estimation errors can be reduced by a nonsingular suppression matrix. Leader-follower event-triggered consensus protocols with continuous-time communication are designed for multiagent systems based on the estimated states. By virtue of the Jordan form of the Laplacian matrix, the stability conditions are derived by using the Lyapunov analysis. Then, new self-triggered consensus protocols are designed for the multiagent systems to remove the requirement of the continuous monitoring triggering condition and continuous communication simultaneously. The triggering interval is proved greater than 0, and the Zeno behavior is excluded for all agents. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed design.

A safe and rapid formation generation is of great importance for the cooperation performance of the multiple unmanned helicopters. The trajectory optimization and control is the key problem in the process of the formation generation. To achieve this goal, a novel safe formation generation strategy is proposed through trajectory optimization and tracking control. First, the rapidly-exploring random tree (RRT) algorithm is applied to obtain the initial guess to satisfy the requirement of rapid solution. Then, the Gauss pseudospectral method is used to transform the optimal control problem to the nonlinear programming (NLP) problem and the sequence quadratic programming (SQP) method is utilized to gain the optimal trajectory combined with the initial guess. Second, a finite-time sliding mode controller is proposed to ensure the finite time trajectory tracking in the presence of model parameter uncertainties and unknown external disturbances. Finally, the numerical simulation is provided to show the effectiveness of the proposed formation generation strategy for unmanned helicopters formation.