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In this brief, a global finite-time attitude control algorithm is proposed for flexible spacecraft without angular velocity measurement. First, a global finite-time observer is designed to estimate unknown angular velocity, eliminating the assumption that the derivative of attitude angle or initial angular velocity is bounded. Based on the proposed observer, a non-overestimated adaptive terminal sliding mode control (ATSMC) algorithm is designed to realize global finite-time attitude control. The main feature of this brief is that the upper bound of lumped uncertainty considered in this brief is related to angular velocity rather than constant, which is more practical. The stability of entire closed-loop system is guaranteed by Lyapunov function. Finally, numerical simulations are given to illustrate the efficiency of the proposed control algorithm.

This article investigates the robust optimal consensus for nonlinear multiagent systems (MASs) through the local adaptive dynamic programming (ADP) approach and the event-triggered control method. Due to the nonlinearities in dynamics, the first part defines a novel measurement error to construct a distributed integral sliding-mode controller, and the consensus errors can approximately converge to the origin in a fixed time. Then, a modified cost function with augmented control is proposed to deal with the unmatched disturbances for the event-based optimal consensus controller. Specifically, a single network local ADP structure with novel concurrent learning is presented to approximate the optimal consensus policies, which guarantees the robustness of the MASs and the uniform ultimate boundedness (UUB) of the neural network (NN) weights’ estimation error and relaxes the requirement of initial admissible control. Finally, an illustrative simulation verifies the effectiveness of the method.

In this article, a finite-time event-triggered disturbance observer (FTETDO) is first designed to handle external disturbance of multiple quadrotor UAVs (QUAVs). The designed FTETDO can effectively reduce update number of observer and the estimation error achieves practical finite-time stability without Zeno behavior. Then a distributed finite-time tracking control strategy is proposed to realize the practical finite-time stability of multiple QUAVs formation. In order to overcome the computational explosion problem, the finite-time filter is employed and the error compensation mechanism is developed to eliminate the influence of filter error on tracking error. Based on the Lyapunov theory, it is proved that the designed control strategy can guarantee practical finite-time stability of the closed-loop system. Finally, simulation is carried out to testify the efficiency of the developed control scheme.

Real-time localization and mapping for micro aerial vehicles (MAV) is a challenging problem, due to the limitation of the onboard computational power. In this article, a tightly coupled light detection and ranging (LiDAR)-inertial odometry is developed, which achieves high accuracy, real-time trajectories estimation for MAV utilizing only onboard sensors and a low-power onboard computer. The key idea of the proposed method is to integrate the IMU measurements, correct LiDAR matching measurements, LiDAR matching outliers into one nonlinear and noncontinuous objective function, and formulate the localization and mapping problem as a stochastic optimization problem. To deal with the nonlinear and noncontinuous objective function, a gradient-free optimization method is proposed to solve the stochastic optimization problem with a single parallel iteration. The novel constructed objective function and gradient-free optimization algorithm enable the proposed LiDAR-inertial odometry to achieve high accuracy and low time consumption. The effectiveness of the proposed method is demonstrated through various scenarios, including public datasets and real-world flight experiments.

As one of the tiniest flying objects, unmanned aerial vehicles (UAVs) are often expanded as the “swarm” to execute missions. In this article, we investigate the multiquadcopter and target pursuit-evasion game in the obstacles environment. For high-quality simulation of the urban environment, we propose the pursuit-evasion scenario (PES) framework to create the environment with a physics engine, which enables quadcopter agents to take actions and interact with the environment. On this basis, we construct multiagent coronal bidirectionally coordinated with target prediction network (CBC-TP Net) with a vectorized extension of multiagent deep deterministic policy gradient (MADDPG) formulation to ensure the effectiveness of the damaged “swarm” system in pursuit-evasion mission. Unlike traditional reinforcement learning, we design a target prediction network (TP Net) innovatively in the common framework to imitate the way of human thinking: situation prediction is always before decision-making. The experiments of the pursuit-evasion game are conducted to verify the state-of-the-art performance of the proposed strategy, both in the normal and antidamaged situations.