This article addresses the high‐accuracy intelligent trajectory tracking control problem of a quadrotor unmanned aerial vehicle (UAV) subject to external disturbances. The tracking error systems are first reestablished by utilizing the feedforward control technique to compensate for the raw error dynamics of the quadrotor UAV. Then, two novel appointed‐fixed‐time observers are designed for the processed error systems to reconstruct the disturbance forces and torques, respectively. And the observation errors can converge to origin within the appointed time defined by users or designers. Subsequently, two novel control policies are developed utilizing reinforcement learning methodology, which can balance the control cost and control performance. Meanwhile, two critic neural networks are used to replace the traditional actor‐critic networks for approximating the solutions of Hamilton–Jacobi–Bellman equations. More specifically, two novel weight update laws are developed. They can not only update the weights of the critic neural networks online, but also avoid utilizing the persistent excitation condition innovatively. And that the ultimately uniformly bounded stability of the whole control system is proved according to Lyapunov method by utilizing the proposed reinforcement learning‐based control polices. Finally, simulation results are presented to illustrate the effectiveness and superior performances of the developed control scheme.

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This paper investigates the problem of trajectory tracking control for quadrotor unmanned aerial vehicle (UAV) in the presence of dynamic obstacles and external disturbance forces/torques. More specifically, two new sliding mode disturbance observers are firstly designed to estimate the external disturbances, in which the observation errors can converge to zero in finite time. Furthermore, utilizing the observation information, a new sliding mode surface-like variable-based position tracking control scheme and a novel nonsingular terminal sliding mode-based attitude synchronization control scheme are developed to drive the UAV tracking the reference trajectory with obstacle avoiding. Moreover, the tracking errors of the close-loop control system can converge to zero within finite time by the analyses of Lyapunov methodology. Finally, the numerical simulation results are presented to illustrate the effectiveness of the proposed control schemes.

The attitude tracking control problem of rigid spacecraft subjected to external disturbance and prescribed performance is studied. A fast fixed‐time stability theorem is firstly developed. A continuous disturbance observer is then designed with its estimation error can be driven into a tiny neighborhood containing zero within fixed time. Moreover, the spacecraft attitude tracking error constrained by prescribed performance function is transformed into an unconstrained dynamics. A disturbance observer‐based prescribed performance tracking controller is designed to stabilize the unconstrained dynamics within fixed time. It also means that the attitude tracking errors meet the prescribed performance as the stabilization of the unconstrained dynamics. Spacecraft simulation examples are finally given to validate the effectiveness of the proposed controller.

This article investigates the distributed prescribed-time formation control problem for the second-order multi-agent systems (MASs) subject to matched and mismatched disturbances in time-varying formation case. As a stepping stone, novel distributed estimators are first proposed to accurately estimate the position and velocity information of the leader within prescribed time. Subsequently, the nominal control protocol is presented for the MAS in the absence of the disturbances to deal with the time-varying formation control problem, and the prescribed-time stability of the system is guaranteed. For system subject to matched and mismatched disturbances, the observer-based integral sliding mode formation control protocol is then presented such that the desired trajectories can be tracked and the desired patterns can be achieved within the prescribed settling time. By adopting the sinusoidal function to the control scheme, the singularity-like problem is solved if there exist measurement errors in the agents. Finally, a numerical simulation example utilizing unmanned aerial vehicles modeled by Simscape and Solidworks is presented to show the efficiency of the proposed scheme.

An offline and online bi-level structure-based dynamic path planning algorithm is proposed for an unmanned aerial vehicle (UAV) in low-altitude complex urban environment. First, an improved Hunger Games Search (HGS) algorithm is developed to generate an offline optimized path under the UAV’s performance constraints and the known static obstacles’ constraints. The individuals of the proposed algorithm will be divided into multiple groups to increase the population diversity. And then, a dynamic grouping strategy and a quantum-behaved behavior are proposed to solve the premature convergence’s problem and the imbalance problem between exploration and exploitation ability in HGS. To improve the dynamic obstacle avoidance efficiency of the algorithm, the dynamic obstacles are classified into three categories: newly added no-fly zone, known and unknown dynamic obstacles. Then, utilizing the information of the offline optimized path and the airborne sensors, three kinds of online planning strategies—an improved rapid-exploring random tree (RRT), a changing speed strategy, and a novel three-dimensional rolling windows—are introduced to dynamically update the path or speed of the UAV. Simulation results indicated that the improved HGS can enhance the performances of the traditional HGS and outperform other compared algorithms on the benchmark functions. Meanwhile, the online planning strategies can effectively achieve dynamic obstacle avoidance within the constraints of offline path. More specially, the planning time and angles of the local path to avoid the no-fly-zone’s influence are improved by 11.3% and 56.8% through utilizing the improved RRT.

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