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In the process of unmanned aerial vehicles (UAV) cluster coordination, communication obstruction may cause situational misjudgement and increase the risk of decision‐making errors. Therefore, based on evolutionary game theory, this research aimed to study the behaviour decision‐making selection problem in the process of UAV swarm missions in limited communication networks. First, the evolution process of the UAV swarm confrontation game is analyzed and the decision‐making risks associated with the communication network of the UAV swarm during coordination, which are caused by the complex confrontation environment are identified. Second, the decision‐making risks and mechanisms of UAV swarm communication is discussed and an evolutionary game mathematical model for the cooperative strategy of a UAV swarm under communication constraints is established. Finally, the influence of various key parameters is simulated and analyzed, such as the initial strategy probability of both parties, amount of platform information data, information analysis and utilization ability, and information interaction coefficient, on the evolution of the UAV swarm strategy. This research provides a theoretical basis and feasible reference for the improvement of UAV mission decision response mechanisms and system model construction, which have important theoretical value and practical significance. [ABSTRACT FROM AUTHOR]

This article proposes a fixed‐time singularity‐free neural control scheme for hypersonic flight vehicle (HFV) considering asymmetric time‐varying angle of attack (AOA) constraint based on integral barrier Lyapunov function (IBLF). In contrast to the state‐of‐the‐art fixed‐time control methods for HFV with AOA constraint, the most distinguishing feature of our control design lies in designing a direct AOA‐constrained control scheme without needing error transformation and the singularity issue is avoided. The IBLF is adopted to confine the AOA within a predefined asymmetric time‐varying compact set all the time. Moreover, the tracking errors of flight states and the estimate errors of approximations are ensured to converge into the bounded compact sets within fixed time, which can be preset by selecting the design parameters properly. Comparative simulations verify the superiority of the developed methodology. [ABSTRACT FROM AUTHOR]

This paper proposes a flight risk analysis method that combines risk assessment and visual deduction to study the causes of flight accidents, specifically the loss of control caused by failure factors. The goal is to explore the impact of these failure factors on loss-of-control events and illustrate the risk evolution under different scenarios in a clear and intuitive manner. To achieve this, the paper develops a failure scenario tree to guide flight simulations under different loss-of-control scenarios. The next step involves developing a multi-parameters risk assessment method that can quantify flight risk at each time step of the flight simulation. This assessment method uses entropy weight and a grey correlation algorithm to assign variable weights to the different parameters. Finally, the paper presents the visual deduction of the risk evolution process under different loss-of-control scenarios using a risk tree that concisely represents the time-series risk assessment results and failure logical chains. Taking three common failure factors (actuator failure, engine failure, and wing icing) as cases, the paper designs 25 different loss-of-control scenarios to demonstrate the flight risk analysis method. By comparing the risk evolution process under different loss-of-control scenarios, the paper explores the impact of the failure factors on flight safety. The analysis results indicate that this method combines risk analysis from both individual and global perspectives, enabling effective analysis of risk evolution in loss-of-control events. [ABSTRACT FROM AUTHOR]

A collision avoidance method that is specifically tailored for UAVs (unmanned aerial vehicles) operating in converging airspace is proposed. The method is based on ADS-B messages and it aims to detect and resolve conflicts between UAVs. The proposed method involves two main steps. First, a UAV conflict-sensing scheme is developed, which utilizes ADS-B information flow path and analyzes the message format information. Second, an unscented Kalman filter is used to predict UAV trajectories based on the acquired ADS-B information. The predicted information is then used to determine potential conflict scenarios, and different deconfliction strategies are selected accordingly. These strategies include speed regulation, direction regulation, and compound deconfliction, and are mathematically validated using the velocity obstacle method. The feasibility and effectiveness of the proposed method are evaluated through simulation, and it is concluded that the method can significantly improve the conflict resolution capability of UAV flights. This research provides a valuable contribution to the field of UAV collision avoidance, and can serve as a theoretical foundation for further advancements in this area. [ABSTRACT FROM AUTHOR]

Summary: This article addresses the distributed adaptive synchronization tracking control problem for multiple flying‐wing UAVs with predefined accuracy and unknown input nonlinearities. Considering that the multiple flying‐wing unmanned aerial vehicles (UAVs) system is a large‐scale nonlinear and strongly coupled system, the control schemes in existing literature which only consider longitudinal, lateral or attitude control may not be applicable in practical engineering. Thus, a full‐state flying‐wing UAV model containing both translational and rotational motions is established, whose dynamics are six‐degree‐of‐freedom with twelve‐state‐variables. Furthermore, to handle the problem that the prior knowledge of the actuator nonlinearities of multi‐UAV system are difficult to obtain, a more general input nonlinear model is established and the adaptive boundary estimation technique is developed. Finally, a performance‐oriented controller is proposed by incorporating a series of smooth functions into adaptive neural control design and Lyapunov analysis. Theoretical analysis and simulation results show that the presented control scheme can guarantee the satisfactory transient tracking error performance, and the tracking error converges to a user‐defined interval ultimately. [ABSTRACT FROM AUTHOR]

This article investigates the fault‐tolerant attitude control problem for the tailless flying wing aircraft subject to time‐varying flight envelope constraints in the presence of the matched/mismatched disturbance, uncertain parameters, and time‐varying actuator failures. To handle these problems and improve the convergence rate of the attitude control system, a nonsingular fixed‐time convergent robust fault‐tolerant control methodology is proposed. First, the time‐varying barrier Lyapunov functions is introduced to confine the flight envelope within the predefined time‐varying compact set while ensuring the transient performance of the attitude tracking error. Subsequently, the fixed‐time sliding mode observer is designed to compensate for the matched/mismatched disturbance, uncertainties, and time‐varying actuator failures, meanwhile, by introducing an intermediate control law in the attitude controller, the singularity problem is avoided without utilizing any filters or continuous and differentiable piecewise functions. Further, it has been analytically proved that all signals of the closed‐loop system are bounded and converge to the residual set around the origin in a fixed time. Finally, simulations are conducted to illustrate the superiorities of the proposed scheme. [ABSTRACT FROM AUTHOR]

Aiming at the problem of the wide range and great difficulty in the future of battlefield airspace control, based on the unique advantages of an airspace grid system in an airspace grid representation and time–space binary computing, this paper designs a pre-clustering method for mission airspace based on airspace location correlation under the condition of future large-scale air combat missions in order to realize the block control of battlefield airspace. This method reduces the whole 3D battlefield space projection to a 2D plane and regards the task airspace projection as "obstacles" in the task area; Multi-A * algorithm is used to generate the airspace clustering line surrounding the task airspace, and the airspace association clustering problem is transformed into a multiple "start point-end point" path planning problem with autonomous optimization. Through the experiment, it was found that clustering the airspace can effectively improve the management and control efficiency of large-scale battlefield airspace. [ABSTRACT FROM AUTHOR]

This article proposes an output-feedback fixed-time trajectory tracking control methodology for hypersonic flight vehicles subject to asymmetric output constraints. In contrast to the state of the art, the most distinguishing feature of our control design lies in avoiding using conventional recursive design methods (e.g., backstepping technique, dynamic surface control, etc.) and in not relying on full-state availability. In the velocity control loop, an asymmetric integral barrier Lyapunov function is adopted to confine velocity variable within a well-defined compact set all the time. In the altitude control loop, after utilizing its cascaded property and proposing a novel scaling function, the original constrained system is transformed to an unconstrained one, which facilitates the control design and stability analysis. Moreover, the proposed control algorithm only involves one fuzzy logic approximator as well as one fixed-time differentiator in the transformed system and guarantees that the tracking errors of velocity and altitude converge into the user-defined residual sets within fixed time. Several comparative simulations have been conducted to highlight the superiorities of the developed method. [ABSTRACT FROM AUTHOR]

This article proposes a fuzzy adaptive fault-tolerant control strategy solving the fast fixed-time constrained tracking problem for hypersonic flight vehicles. To handle the actuator faults, a two-step design method is first proposed that only needs to estimate the upper and lower bounds of actuator parameters, under which the adverse influences of actuator faults (e.g., lock-in-place, loss of effectiveness, etc.) have been compensated via an adaptive fashion instead of conventional robust ways. In comparison with the state-of-the-art, a new fixed-time corollary that has proved a smaller upper bound of convergence time under the same condition of conventional fixed-time stability is presented. To avoid singularity issues often encountered in fixed-time designs, a piecewise but differentiable switching control laws whose continuity and differentiability are guaranteed everywhere through an appropriate design is introduced. Such a design not only preserves the continuity and differentiability of virtual and actual control laws, but also ensures the continuity of their time derivatives. Fuzzy logic systems are exploited to tackle continuous unknown dynamics and asymmetric time-varying barrier functions are utilized to confine flight states within some predefined compact sets all the time provided their initial conditions remain therein. This property is of great significance in dealing with the challenge that the operating regions of the flight state variables are asymmetric and time-varying in practice, especially when executing various flight missions. Comparative simulations have been performed to validate the effectiveness of the proposed control scheme in terms of fixed-time convergence, smoothness, and time-varying constraints satisfaction. [ABSTRACT FROM AUTHOR]

Responsive and highly controlled autonomous vehicles (AVs) on the road easily form platoons with small inter-vehicle gaps to increase traffic throughput and reduce energy consumption. Unlike human drivers, AVs typically employ discrete quantization signals and only generate finite discrete acceleration. Finite discrete acceleration easily causes unstable inter-vehicle gaps and inter-vehicle collisions, especially when there are inaccurate quantization parameters due to quantizer aging. Based on the backstepping technique, this paper proposes the cooperative constrained control algorithm under unknown quantization parameters for AVs with nonuniform input quantization and motion uncertainties. The control laws of each AV in the platoon only depend on the state information of its consecutive AVs. The position trajectories of consecutive AVs are constructed as inter-vehicle collision-free constraints to avoid emergency braking and inter-vehicle collisions. In addition, the consistent characteristic of two types of nonuniform quantizers (i.e., logarithmic quantizers and hysteresis quantizers) is proposed, and unknown disturbances in vehicle motion are compensated. The Lyapunov based local stability and string stability are proved. Numerical simulation shows that the control algorithm is applicable to AVs with either logarithmic quantizers or hysteresis quantizers. Compared with the previous control algorithms without considering input quantization and inter-vehicle collision avoidance, the control algorithm proposed reduces inter-vehicle spacing errors, avoids high acceleration, and avoids inter-vehicle collisions. [ABSTRACT FROM AUTHOR]

A new fixed-time fuzzy adaptive fault-tolerant control methodology is proposed for the longitudinal dynamics of hypersonic flight vehicles (HFVs) in the presence of actuator faults, uncertain dynamics, and external disturbances. In contrast with the conventional fixed-time control schemes that typically contain the fractional powers of errors in their designs, this work develops a low-complexity control structure in the sense of removing the dependence on the need of abovementioned fractional power terms by means of prescribed performance control (PPC) method. Different from the most existing PPC approaches where the initial conditions of tracking errors are required to be known, the newly proposed prescribed performance function (PPF) can relax such restrictions through choosing properly small initial values of PPF. Fuzzy logic systems (FLSs) are employed to handle unknown dynamics, and minimal learning parameter (MLP) technique is incorporated into the design for the purpose of alleviating computation burden. Closed-loop stability is rigorously proved via Lyapunov stability theory, and simulation results are eventually given to validate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive neural control to solve the tracking problem of a class of pure-feedback systems with non-differentiable non-affine functions in the presence of unknown periodically time-varying disturbances. To handle with the design difficulty from non-affine structure of pure-feedback system, a continuous and positive control gain function is constructed to model the periodically disturbed non-affine function as a form that facilitates the control design. As a result, the non-affine function is not necessary to be differentiable with respect to control variables or input. In addition, the bounds of non-affine function are unknown functions, and some appropriate compact sets are introduced to investigate the bounds of non-affine function so as to cope with the difficulty from these unknown bounds. It is proven that the closed-loop control system is semi-globally uniformly ultimately bounded by choosing the appropriate design parameters. Finally, comparative simulations are provided to illustrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This article aims to realize event-triggered constrained consensus tracking for high-order nonlinear multiagent networks subject to full-state constraints. The main challenge of achieving such goals lies in the fact that the standard designs [e.g., backstepping, event-triggered control, and barrier Lyapunov functions (BLFs)] successfully developed for low-order dynamics fail to work for high-order dynamics. To tackle these issues, a novel high-order event-triggered mechanism is devised to update the actual control input, lowering the communication and computation burden. More precisely, compared with the conventional event-triggered mechanism, not only the amplitudes of control signals and a fixed threshold are considered but a monotonically decreasing function is introduced to allow a relatively big threshold, while guaranteeing consensus tracking error to be small. Then, a high-order tan-type BLF working for both constrained and unconstrained scenarios is incorporated into the distributed adding-one-power-integrator design for the purpose of confining full states within some compact sets all the time. A finite-time convergent differentiator (FTCD) is introduced to circumvent the “explosion of complexity.” The consensus tracking error is shown to eventually converge to a residual set whose size can be adjusted as small as desired through choosing appropriate design parameters. Comparative simulations have been conducted to highlight the superiorities of the developed scheme. [ABSTRACT FROM AUTHOR]

Cooperative group formation control of manned/unmanned aircraft vehicles (MAV/UAVs) using a hierarchical framework can be more efficient and flexible than centralized control strategies. In this paper, a two-level hierarchical-interaction-based cooperative control strategy is proposed for the MAV/UAVs group formation. At the upper level, combined with the nonlinear disturbance observer (NDO) and dynamic surface control (DSC) algorithm, a trajectory tracking problem with external disturbances for MAV is formulated. At the lower level, the leader-following formation controller is utilized to deal with the sub-formation keeping control problem for UAVs, based on the sliding mode disturbance observer and fast terminal sliding mode control law, and the robust performance and control accuracy are effectively improved. Moreover, the overall stability of the MAV/UAVs system is demonstrated using Lyapunov theory. The proposed approach is evaluated by simulation under the ground penetration combat mission for MAV/UAVs, and the performance is compared with that of other control strategies. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) can be used in swarms to achieve multiple tasks cooperatively. Multi-UAV and multi-target cooperative task assignments are difficult. To solve the problem of unbalanced, phased, cooperative assignment between UAVs and tasks, we establish an unbalanced, phased task assignment model that considers the constraints of task execution, time, and target task execution demand. Based on an improved consensus-based bundle algorithm (CBBA), we propose a two-tier task bidding mechanism. According to dynamic changes in new tasks, we study a dynamic assignment strategy and propose a mechanism based on task continuity adjustment and time windows. Finally, a simulation experiment is used to verify the feasibility and effectiveness of the proposed allocation method in multi-UAV target assignment scenarios. The results show that the dynamic task assignment strategy can efficiently assign random new tasks as they arise. [ABSTRACT FROM AUTHOR]

In contrast with most existing results concerning unmanned aerial vehicles (UAVs) wherein material points or only attitude/longitudinal dynamics are considered, this article proposes a distributed fixed-time fault-tolerant control methodology for networked fixed-wing UAVs whose dynamics are six-degree-of-freedom with twelf-state-variables subject to actuator faults and full-state constraints. More precisely, state transformations with the scaling function are devised to keep the involved velocity and attitude within their corresponding constraints. The fixed-time property is obtained in the sense of guaranteeing that the settling time is lower bounded by a positive constant, which is independent of initial states. The actuator faults as well as the network induced errors are handled via the bound estimation approach and well-defined smooth functions. By strict Lyapunov arguments, all closed-loop signals are proved to be semiglobally uniformly ultimately bounded, and the tracking errors of velocity and attitude converge to the residual sets around origin within a fixed time. [ABSTRACT FROM AUTHOR]

This work investigates the consensus tracking problem for high-power nonlinear multiagent systems with partially unknown control directions. The main challenge of considering such dynamics lies in the fact that their linearized dynamics contain uncontrollable modes, making the standard backstepping technique fail; also, the presence of mixed unknown control directions (some being known and some being unknown) requires a piecewise Nussbaum function that exploits the a priori knowledge of the known control directions. The piecewise Nussbaum function technique leaves some open problems, such as Can the technique handle multiagent dynamics beyond the standard backstepping procedure? and Can the technique handle more than one control direction for each agent? In this work, we propose a hybrid Nussbaum technique that can handle uncertain agents with high-power dynamics where the backstepping procedure fails, with nonsmooth behaviors (switching and quantization), and with multiple unknown control directions for each agent. [ABSTRACT FROM AUTHOR]

This article proposes a fuzzy adaptive design solving the finite-time constrained tracking for hypersonic flight vehicles (HFVs). Actuator dynamics and asymmetric time-varying constraints are considered when solving this problem. The main features of the proposed design lie in 1) introducing a novel piecewise but differentiable switching control law, with an appropriate design thought to avoid the singularity issues typical of finite-time control; 2) handling actuator magnitude, bandwidth, and rate constraints, thanks to the introduction of an auxiliary compensating system counteracting the adverse effects caused by actuator physical constraints, while guaranteeing the closed-loop stability; and 3) handling asymmetric time-varying state constraints, thanks to the introduction of tan-type barrier Lyapunov functions working for both constrained and unconstrained scenarios. Comparative simulation results illustrate the effectiveness of the proposed strategy over existing methods for HFVs in terms of convergence, smoothness, actuator performance, and constraints satisfaction. [ABSTRACT FROM AUTHOR]

Compared with most existing results concerning unmanned aerial vehicles (UAVs) wherein two-degree or only attitude/longitudinal dynamics are considered, this paper proposes an event-based fault-tolerant coordinated control (FTC) for multiple fixed-wing UAVs such that the consensus tracking of velocity and attitude is achieved in the presence of actuator faults, external disturbances and modeling uncertainties. More precisely, as opposed to static event-triggered communication mechanisms, a dynamic event-triggered communication mechanism (DECM) is devised to schedule the connected communications while avoiding the unnecessary information exchanges among UAVs, which reduces the communication burden and saves on the network resources. Meanwhile, the Zeno phenomenon is excluded in terms of guaranteeing that the period between two consecutive triggering communication is lower bounded by a positive constant. Moreover, the actuator fault, external disturbance as well as model uncertainty are treated as the lumped disturbances and estimated via the disturbance observer technique. By strict Lyapunov arguments, all closed-loop signals are proved to be uniformly ultimately bounded (UUB) and the tracking errors of velocity and attitude converge to a residual set around origin. Finally, simulation results are presented to illustrate the validity and superiority of proposed event-based control scheme. [ABSTRACT FROM AUTHOR]

In contrast with most existing results concerning unmanned aerial vehicles (UAVs) wherein two-degree or only attitude/longitudinal dynamics are considered, this article proposes an event-triggered cooperative synchronization fault-tolerant control (FTC) methodology for multiple fixed-wing UAVs whose dynamics are six-degree-of-freedom (DOF) with twelf-state-variables subject to actuator faults, modeling uncertainties, and external disturbances. More precisely, an event-triggering mechanism is devised to determine the time instants of updating control signals, which reduces the signal transmission burden, while saving on system resources. The Zeno phenomenon is excluded in the sense of guaranteeing that the time between two consecutive switchings is lower bounded by a positive constant. The actuator faults as well as the network induced errors are handled via the bound estimation approach and some well-defined smooth functions. By strict Lyapunov arguments, all closed-loop signals are proved to be semi-globally uniformly ultimately bounded (SGUUB) and the synchronization tracking errors of speed and attitude converge to a residual set around origin whose size can be made arbitrarily small through selecting appropriate design parameters. [ABSTRACT FROM AUTHOR]

This work proposes a nonsingular adaptive fixed‐time switching control method for a class of strict‐feedback nonlinear dynamics subject to full state constraints. The peculiarity of this design lies in overcoming the singularity issue that typically appears in the existing backstepping‐based fixed‐time control methods caused by the iterative differentiation of fractional power terms as tracking errors approach to zero, while guaranteeing the nonviolation of full state constraints. Crucial in solving such singularity issue is to skillfully introduce a smooth switching between fractional power and integer power terms, which guarantees that fractional power term is confined within a positive interval all the time. An asymmetric time‐varying barrier Lyapunov function is delicately incorporated into control design, rendering state variables to be within prescribed time‐varying bounds. Besides, radial basis function neural network is employed to handle system unknown nonlinearities. It is rigorously proved that all the closed‐loop signals eventually converge to small regions around origin within fixed‐time. Comparative simulation results are finally given to validate the effectiveness and superiority of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This article addresses and solves the adaptive asymptotic tracking for a class of uncertain switched positive linear dynamics (also known in the literature as compartmental models) subject to dwell-time constraints. Compared to the state-of-the-art, the innovative feature of this method is to attain for the first time asymptotic set-point tracking, while guaranteeing non-negativity of the systems states. To achieve asymptotic tracking, an interpolated Lyapunov function is adopted, which is nonincreasing at the switching instants and decreasing in two consecutive switching instants. Such Lyapunov function results in a novel adaptive law with time-varying adaptive gains, as opposed to state-of-the-art laws with fixed positive adaptive gains. The developed design is applicable to classes of compartmental systems compatible with those proposed in the literature: an example involving the infusion of anesthesia is conducted to show that the proposed method can achieve better performance than existing methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel set-invariance adaptive dynamic surface control (DSC) design for a larger class of uncertain large-scale nonlinear input-saturated systems. The peculiarity of this class is that no a priori bound on the continuous control gain functions is assumed (i.e., their boundedness cannot be assumed before obtaining system stability). This requires a new design. Differently from the available methods, the proposed design involves the construction of appropriate invariant sets for the closed-loop trajectories, which allows to remove the restrictive assumption of a priori bounds of the control gain functions. Furthermore, we show that such set-invariance design can handle input constraints in the form of input saturation. In line with the DSC methodology, semi-globally uniformly ultimate boundedness is proven: however, differently from the standard methodology, stability analysis requires the combination of Lyapunov and invariant set theories. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive fuzzy dynamic surface control (DSC) method for an extended class of periodically disturbed strict-feedback nonlinear systems. The peculiarity of this extended class is that the control gain functions are not bounded a priori but simply taken to be continuous and with a known sign. In contrast with existing strategies, controllability must be guaranteed by constructing appropriate compact sets ensuring that all trajectories in the closed-loop system never leave these sets. We manage to do this by means of invariant set theory in combination with the Lyapunov theory. In other words, boundedness is achieved a posteriori as a result of stability analysis. The approximator composed of fuzzy logic systems and Fourier series expansion is constructed to deal with the unknown periodic disturbance terms. [ABSTRACT FROM AUTHOR]

This article addresses a flight-stability problem for the multiple unmanned aerial vehicles cooperative formation flight in the process of the closed and high-speed flight. The main objective is to design a cooperative formation controller with known external factors, and this controller can keep the consensus of attitude and position and reduce the communication delay between any two unmanned aerial vehicles and increase unmanned aerial vehicles formation cruise time under the known external factors. Known external factors are taken into consideration, and longitude maneuvers using nonlinear thrust vectors were employed with unsteady aerodynamic models, according to the attitude and position of unmanned aerial vehicles, which were employed as corresponding input signals for studying the dynamic characteristics of unmanned aerial vehicles formation flight. In addition, the relative distance between any two unmanned aerial vehicles was not allowed to exceed their safe distance so that the controller could perform collision avoidance. An analysis of formation flight distance error shows that it converged to a fixed value that well ensured unmanned aerial vehicles formation flight stability. The experimental results show that the controller can improve the speed of a closed formation effectively and maintain the stability of formation flight, which provides a method for closed formation flight controller design and collision avoidance for any two unmanned aerial vehicles. Meanwhile, the effectiveness of proposed controller is fully proved by semi-physical simulation platform. [ABSTRACT FROM AUTHOR]

This work removes the critical assumptions of continuity, differentiability, and state-independent boundedness, which are typical of compounded disturbances in disturbance observer-based adaptive designs. Crucial in removing such assumptions are a novel observer-based design with state-dependent gain in place of a constant one, and a novel set-invariance design. The designs use different a priori knowledge of the disturbance, but they can both handle state-dependent (e.g., possibly unbounded) disturbances, as well as non-smooth (e.g., non-differentiable and jump discontinuous) disturbances. The tracking error is proven to be as small as desired by appropriately choosing design parameters. For the second design, which uses the least a priori knowledge of the disturbance, stability is proven by enhancing Lyapunov theory with an invariant-set mechanism, so as to construct an appropriate compact set resulting an invariant set for the closed-loop trajectories. [ABSTRACT FROM AUTHOR]