Formation flight of uncrewed aerial vehicles (UAVs) poses significant challenges in terms of safety and formation keeping, particularly in cluttered environments. However, existing methods often struggle to simultaneously satisfy these two critical requirements. To address this issue, this paper proposes a sampling-based trajectory planning method with a hierarchical structure for formation flight in dense obstacle environments. To ensure reliable sharing of local sensing information among UAVs, each UAV generates a safe flight corridor (SFC), which is transmitted to the leader UAV. Subsequently, a sampling-based formation guidance path generation method is designed as the front-end strategy, steering the formation to fly in the desired shape safely with the formation connectivity provided by the SFCs. Furthermore, a model predictive path integral (MPPI) based distributed trajectory optimization method is developed as the back-end part, which ensures the smoothness, safety and dynamics feasibility of the executable trajectory. To validate the efficiency of the developed algorithm, comprehensive simulation comparisons are conducted. The supplementary simulation video can be seen at https://www.youtube.com/watch?v=W2hXrjhl864

This article investigates secure bipartite consensus control of nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks, and designs an event-based dynamic quantized sliding mode control scheme. In order to ensure the stability of MASs in the process of achieving bipartite consensus, combined with the online adjustment strategy of quantitative sensitivity parameters and the designed event-triggered mechanism, the constraints of quantitative measurement saturation parameters and event-triggered threshold parameters are given. Moreover, it is proved that Zeno behavior does not occur at the zoom-out/zoom-in stage. Then, combined with reasonable assumptions about the frequency and duration of DoS attacks, appropriate controller parameters are redesigned, and the stability of the system is proved by Lyapunov stability theory and mathematical induction. Finally, the effectiveness of the proposed method is illustrated by a simulation example.

Quadrotor swarm with advanced coordination capabilities has garnered widespread attention, yet efficient and reliable motion planning remains a challenge. This paper presents a hierarchical motion planning framework for quadrotor swarm autonomous navigation in unknown, cluttered scenes. Specifically, we take a step forward sampling of multiple neighbors in the lattice graph and generate a group of paths. The infeasible paths are excluded and the one with the minimum cost is chosen from the remaining. Taking it as a guiding path, a gradient-free trajectory optimization method based on model predictive path integral (MPPI) is developed to produce the execution trajectory. Compared to gradient-descent approaches, it is capable of dealing with non-continuous and non-convex constraints. Additionally, the proposed method is deployed on GPUs in parallel to enhance efficiency. Extensive simulations demonstrate the robustness and effectiveness of the proposed motion planning framework.

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 novel adaptive multivariable super-twisting-based Lyapunov redesign (AMSTLR) algorithm is proposed for reusable launch vehicle (RLV) attitude control system subject to unknown disturbance and uncertain control coefficient. In the method, a continuous adaptive control algorithm integrating Lyapunov redesign (LR) method and adaptive super-twisting (AST) method is provided. The proposed method enables the finite-time attitude tracking of RLV, without requiring precise knowledge of the upper bound of the uncertainties. Additionally, a rigorous stability analysis of the presented algorithm is provided utilizing Lyapunov technique. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control scheme.

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.

The large-angle attitude maneuver trajectory design and tracking control for flexible spacecraft with slosh structure under attitude measurement errors and actuator faults are investigated in this article. Considering that rapid attitude maneuvers can lead to severe flexible vibrations and liquid sloshing, which in turn affects system stability. Therefore, under the requirement of fast maneuvering, the attitude trajectory is optimized to maximize the system’s stability by establishing a new indicator function, while satisfying multiple physical constraints. Furthermore, the novel adaptive multivariable command filtering backstepping fault-tolerant controller is proposed for the coupled spacecraft, to achieve the finite-time attitude tracking to the optimized trajectory, despite the measurement errors and actuator faults. The merits lie in the modified auxiliary signals and virtual commands design, which can compensate the filter errors, caused by the applied filters to deal with the “explosion of terms” of conventional backstepping control, in finite time with better control performance. The numerical simulations are performed to verify the effectiveness of the proposed method.

The distributed, real-time algorithms for multiple pursuers cooperating to capture an evader are developed in an obstacle-free and an obstacle-cluttered environment, respectively. The developed algorithm is based on the idea of planning the control action within its safe, collision-free region for each robot. We initially present a greedy capturing strategy for an obstacle-free environment based on the Buffered Voronoi Cell (BVC). For an environment with obstacles, the obstacle-aware BVC (OABVC) is defined as the safe region, which considers the physical radius of each robot, and dynamically weights the Voronoi boundary between robot and obstacle to make it tangent to the obstacle. Each robot continually computes its safe cells and plans its control actions in a recursion fashion. In both cases, the pursuers successfully capture the evader with only relative positions of neighboring robots. A rigorous proof is provided to ensure the collision and obstacle avoidance during the pursuit-evasion games. Simulation results are presented to demonstrate the efficiency of the developed algorithms.

A new finite-time dynamic allocation and control scheme is developed in this article for multiple agents tracking a moving target. Based on a competitive manner, the dynamic allocation is achieved by k-winners-take-all (k-WTA), which can be realized by a novel finite-time dual neural network with the adaptive-gain activation function. Then, a finite-time disturbance compensation-based control law is proposed for agents to conduct capturing task or return to the specified point in vigilance. The finite-time stability of the system is guaranteed through Lyapunov analysis. Finally, the efficiency of the proposed scheme is illustrated by simulations in which the situation with higher target velocity than trackers is considered.

A novel adaptive multivariable control algorithm is proposed for reusable launch vehicle (RLV) subject to bounded but unknown disturbances. In the proposed algorithm, a time-varying nonoverestimated adaptive gain is designed based on the prescribed performance function. The proposed method is very simple and easy implementation, and the Lyapunov-based technique is utilized to guarantee that the reentry attitude tracking errors are confined in some prescribed regions independent of the upper bound of the disturbances. Finally, the efficiency of the developed algorithm is illustrated by simulation results.

The attitude control of an underactuated reusable launch vehicle (RLV) in the reentry phase involving nonminimum phase problem and control input constraints is investigated in this article. To address the nonminimum phase problem, an approach combining output redefinition and robust backstepping is proposed, where a synthetic output is constructed using the combination of the original output and the internal states to obtain stable zero dynamics, and then robust backstepping is performed on the new output. Besides, the ideal internal dynamics are obtained by using optimal bounded inversion, which are incorporated into the controller as the reference trajectories for the internal states to improve the output tracking accuracy. To cope with the control input constraints, a simple and useful anti-windup strategy is proposed by using feedback error clipping, which is shown to be very effective in mitigating control input saturation. Numerical simulations are given to validate the effectiveness of the proposed method.

In this article, active vibration suppression and attitude control for flexible spacecraft under model uncertainty and external disturbance are investigated. First, an adaptive disturbance observer (ADO) and flexible vibration observer (FVO) are proposed to estimate the lumped uncertainty and flexible vibration. It is shown that the proposed ADO is nonoverestimated and the first derivative upper bound of disturbance is unknown. Then, based on the proposed observers, a novel controller is designed to suppress flexible vibration and realize attitude control. The remarkable feature of the designed algorithm is that flexible vibration is suppressed and attitude tracking is guaranteed simultaneously without intelligent materials which are used to suppress flexible vibration in the previous work. In addition, high-precision attitude control can be achieved. The rigorous proof of closed-loop system stability is presented using Lyapunov techniques. Finally, numerical simulations are given to illustrate the efficiency of the proposed algorithm.

This article addresses the robust fixed-time stabilization control problem for generic linear systems with both matched and mismatched disturbances. A new observer-based fixed-time control technique is proposed to solve this robust stabilization problem, provided that the system matrix pair$(A,B)$is controllable. The ultimate boundedness of the closed-loop system in the presence of mismatched disturbances is proven. An upper bound of the convergence time is provided, which is irrelevant to initial conditions. Finally, a simulation example is presented to show the efficiency of the proposed control design method.

An event-triggered attitude control algorithm is developed for quadrotor unmanned aerial vehicles (UAVs) subject to external disturbances. In this article, first an event-triggered supertwisting stabilizing control strategy for a class of second-order nonlinear systems is proposed. Then, a Lyapunov-based stability analysis is provided for the closed-loop system, and the Zeno-free execution of triggering sequence is guaranteed via rigorous analysis. Furthermore, the proposed control strategy is applied on attitude control of UAVs to reduce the computing cost without degrading the performance of the system. Finally, the efficiency of the developed method is validated by numerical simulation.

The precision output tracking problem of multi-input-multi-output (MIMO) uncertain nonlinear nonminimum phase systems is investigated in this paper. The challenge lies in the difficulty in solving the ideal internal dynamics (IID), which is a bounded solution for the uncertain zero dynamics. First, the experience replay technique is applied to identify the uncertain parameters in the zero dynamics. It uses the recorded past data concurrently with current data which can greatly speed up the identification convergence. With the identified parameters, a novel approach called optimal bounded inversion is proposed to obtain the IID by solving a trajectory optimization problem using GPOPS-II. The boundedness of the IID is guaranteed by setting state constraints and the feasibility is achieved by minimizing the initial condition mismatch. Moreover, a piecewise IID updating scheme is adopted to reduce the computational burden. The benchmark nonminimum phase system, a vertical take-off and landing (VTOL) aircraft is used to validate the effectiveness of the proposed method.

An integrated fault estimation (FE) and fault-tolerant tracking control (FTTC) strategy is developed for Lipschitz nonlinear multiagent systems subject to actuator faults, external disturbance, and uncertainties. First, for each agent, a corresponding unknown input observer with reduced/full order is constructed to obtain the FE. Then, a state/output feedback FTTC strategy is proposed based on the integral sliding-mode technique and adaptive super-twisting algorithm. The observer and controller gains are obtained simultaneously via H∞optimization with a linear matrix inequality formulation. Finally, the comparison simulations are provided to demonstrate the effectiveness of the proposed algorithm.

This paper investigates the problem of attitude tracking control for reusable launch vehicles (RLVs) with aeroservoelastic characteristic and disturbances in reentry phase. An aeroservoelastic model for reentry RLV is first established, where the synthetic disturbances are considered as well. Then a novel disturbance observer combined with fuzzy logic system is constructed by using both fuzzy approximation error and observation error to provide the disturbance estimation. The fuzzy disturbance observer (FDO) can be employed to the control scheme to counteract the perturbations and guarantee the estimation errors for converging to a small neighborhood around origin. Incorporated with the designed FDOs, the adaptive sliding mode technique is proposed for developing attitude angle and angular rate subsystem controllers, respectively, to ensure the reentry attitude tracking performance. Rigorous proof shows that the uniform stability of the closed-loop system under the proposed control law can be guaranteed by using Lyapunov technique. Finally, numerical simulations are conducted in this paper to illustrate the effectiveness of the developed technique for the RLV.

This paper addresses the fixed-time leader-follower consensus problem for second-order multiagent systems without velocity measurement. A new continuous fixed-time distributed observer-based consensus protocol is developed to achieve consensus in a bounded finite time fully independent of initial condition. A rigorous stability proof of the multiagent systems by output feedback control is presented based on the bi-limit homogeneity and the Lyapunov technique. Finally, the efficiency of the proposed methodology is illustrated by numerical simulation.