This paper explores the optimal consensus problem involving fully actuated Euler-Lagrange systems with parametric uncertainties. We propose a distributed adaptive control algorithm that incorporates a novel dynamical auxiliary system to generate reference positions. Nonlinear transformation functions for the position tracking error, along with smooth compensating terms, are introduced and integrated into the reference velocity design. The proposed control framework is distinctive in that it only requires relative position measurements from neighboring agents and does not rely on any global information, thus enabling a fully distributed implementation. A theoretical analysis demonstrates that the proposed control protocol ensures the asymptotic convergence of all agents to the optimal solution of the total cost function and the boundedness of all closed-loop signals. Simulation results for two-link revolute joint manipulators are presented to validate the effectiveness of the proposed approach.

Hypersonic flight vehicle faces critical challenges of control from highly nonlinear and time-varying uncertainties, which impose stringent requirements for real-time parameter adaptation under safety constraints. This paper proposes a reinforcement learning-based adaptive tracking control algorithm to address these issues. The crucial contributions of our design, as opposed to the state-of-the-art approaches, lie in three aspects: (a) a hybrid design of model-based control and reinforcement learning to alleviate the safety, stability and generalization issues of learning-based methods specifically for the demanding hypersonic flight environment; (b) the establishment of a reinforcement learning-based optimization framework that dynamically adjusts control parameters in a real-time optimal fashion to improve the tracking performance under dynamic uncertainties and flight regime transitions, which is substantially different from most conventional methods with constant parameters; (c) the theoretical analysis of both the closed-loop stability of the adaptive control and the convergence performance of the learning algorithm, which distinguishes our design from most existing reinforcement learning-based methods that have no stability or convergence guarantee and is particularly critical for safety-critical hypersonic flight vehicle applications. Numerical simulations show that the proposed method achieves a reduction in the integral of tracking error of 8.31% under model perturbations and 34.3% under changing reference trajectories, compared to the baseline method, while maintaining comparable control energy consumption.

Hypersonic flight vehicles (HFVs) exhibit complex nonlinear dynamics and time-varying uncertainties, placing stringent demands on the adaptive learning capabilities of flight control systems. Meanwhile, the limited onboard communication and computational resources further require efficient resource utilization. To address these issues, this article proposes a dynamic event-triggered control (DETC) scheme based on actor–critic framework for HFVs, achieving synergistic optimization of control performance and resource conservation. First, an optimal event-triggered controller is developed within actor–critic framework to regulate the frequency of control input updates. A static event-triggered condition is formulated, and the stability of the closed-loop system is analyzed, with Zeno behavior eliminated, ensuring both stability and feasibility. Building on this, a dynamic variable is introduced, leading to a dynamic event-triggered condition that adaptively adjusts the trigger threshold based on real-time system states. Unlike static event-triggered mechanisms with fixed thresholds, this mechanism further optimizes the frequency of control input updates, achieving more efficient resource utilization while maintaining the desired control performance. Moreover, rigorous theoretical analysis demonstrates that the closed-loop system is stable and Zeno behavior is eliminated under the DETC scheme. Finally, the effectiveness of our proposed DETC scheme is verified through a simulation example.

Using the backstepping technique, this paper formulates innovative adaptive finite-time stabilizing controllers for uncertain nonlinear systems featuring nonuniform input quantization and asymmetric, time-varying output constraints. These novel controllers leverage the consistent characteristics of both hysteresis quantizers and logarithmic quantizers. Quantization errors, when consistent, become unbounded and contingent on control input, rendering them incompatible with the growth conditions of nonlinear systems. Consequently, the developed adaptive controllers eliminate the reliance on growth conditions, effectively addressing the impact of unbounded quantization errors on finite-time stability. This adaptability allows the controllers to function effectively with systems employing either hysteresis quantizers or logarithmic quantizers. The paper establishes the convergence of these controllers through the finite-time Lyapunov stability theorem. It also provides a comprehensive guideline for tuning settling time, enabling fine-grained control over finite-time convergence and adjustable tracking error performance. Additionally, the controllers rigorously maintain system output within predefined limits. Their effectiveness and low computational burden are demonstrated through three comparative numerical simulations and a practical simulation in collision-free trajectory tracking control of an autonomous vehicle platoon using the vehicle motion software CarSim. These simulations confirm the advanced performance of the adaptive controllers.Note to Practitioners—This paper introduces an innovative approach to control uncertain nonlinear systems encountering intricate input quantization and output constraints. Employing the sophisticated backstepping technique, the authors present adaptive finite-time-stabilizing controllers engineered to address nonuniform input quantization and asymmetric, time-varying output restrictions. What distinguishes these controllers is their reliance on the consistent behavior exhibited by hysteresis and logarithmic quantizers. This unique feature equips them to effectively counteract unbounded quantization errors influenced by control input. Most notably, these controllers eliminate the conventional growth conditions typically demanded by nonlinear systems. As a result, they extend their applicability to a broad spectrum of systems employing either hysteresis or logarithmic quantizers. The research also provides practitioners with a valuable guideline for precisely adjusting settling time. This enables the attainment of desired convergence rates while permitting adaptable tracking error performance. Additionally, these controllers guarantee that the system’s output adheres to predefined limits. The practical significance of this study is highlighted through three comparative numerical simulations and a real-world application simulation. This real-world simulation involves collision-free trajectory tracking control of an autonomous vehicle platoon, executed using the vehicle motion software CarSim. These simulations unequivocally demonstrate the effectiveness and low computational burden of the developed controllers, thereby establishing them as a valuable resource for practitioners facing complex control challenges in various domains.

This paper endeavors to address the output-feedback bipartite consensus issue for uncertain high-order multi-agent systems (MASs) through the utilization of a dynamic event-triggered control methodology. To achieve this goal, a novel output-feedback distributed control strategy is developed for each follower agent. This strategy utilizes a bounded time-varying gain along with a predefined-time compensator. This combined approach facilitates the achievement of bipartite consensus within a specified timeframe. The key highlight of this methodology lies in the introduction of a newly devised time-varying gain, which not only enhances the convergence rate but also significantly improves transient performance. Additionally, a new dynamic event-triggered mechanism is developed for each agent, leveraging the newly introduced predefined-time internal dynamic variable. This mechanism ensures the achievement of predefined-time bipartite consensus while significantly reducing communication frequency. Comparative simulations are conducted to validate the superiority of the obtained results. Note to Practitioners—For real physical agent systems, it is impractical to gather all the state information. Hence, there is a critical need to investigate output feedback consensus algorithms that eliminate reliance on state information. However, existing output feedback consensus algorithms require the continuous transmission of control instructions to actuators, resulting in high communication costs and transmission burden. Additionally, in practical requirements, designers are eager for multiple agents to achieve the required stable performance at a predefined time rather than infinite time. For this reason, this paper presents a new output-feedback event-triggered control methodology to achieve a predefined-time bipartite consensus of uncertain nonlinear MASs. To improve the system’s convergence speed and transient performance, we introduce a novel time-varying gain. Next, we devise a dynamic event-triggering control for each agent utilizing the newly introduced predefined-time internal dynamic variable. This approach effectively reduces communication frequency while ensuring predefined-time bipartite consensus.

This paper presents a comprehensive mathematical approach to address the multi-player and multi-objective pursuit-evasion games problem, incorporating coalition structure constraints from a cooperation-competition perspective. Social interaction networks are developed to approximate priority communication alliances based on individual preferences, establishing a multi-connected topology and decision space for the games. An N-player variable-sum differential game model, featuring autonomous obstacle avoidance, is formulated by integrating kinematic constraints and the social forces method. Rigorous proofs are provided for the uniqueness of payoff distribution, the stability of alliance structures, and the convergence of many-to-many differential games to Nash Equilibrium. Simulation and experimental results are presented to validate the effectiveness and performance of the proposed method.

A coordinated control challenge is addressed in 6-degree-of-freedom (6-DOF) fixed-wing multiple autonomous aerial vehicle (multi-AAV) systems under communication and state constraints. The primary obstacle in achieving this goal arises from managing frequent information interactions and the assurance that UAV states converge within prescribed bounds. On the one hand, a novel dynamic self-triggering mechanism is effectively proposed. Unlike current state-of-the-art approaches, the proposed dynamic self-triggering communication mechanism features a larger triggering threshold and eliminates the need for continuous monitoring of system state information. This reduces the demand on system communication and sensor resources. On the other hand, a new time-varying constraint bounded function is introduced to effectively relax restrictions on the initial system state. Then, the coordinated translational/rotational controllers are designed to ensure minimal consensus tracking error. Semi-physical simulations highlight the effectiveness of the proposed control algorithm. Note to Practitioners—In actual environment, the multi-UAVs flight always requires inter-communication to ensure the stable performance of the entire formation. However, period-based communication leads to a waste of communication resources. The event-triggering communication mechanism lowers the communication frequency of UAVs, thereby reducing energy consumption. Nevertheless, most existing control results on event-triggered communication overlook the fact that continuous monitoring of state information still causes unnecessary energy consumption. To further investigate the problem, a dynamic self-triggering mechanism is proposed in this study, which can determine the subsequent triggered moment based on the state information of the current triggered moment. In addition, the state of UAVs due to safety and physical constraints ought to be constrained. Therefore, a prescribed-time constrained control strategy is proposed, which not only improves the transient performance (e.g. small overshoot and fast adjustment time), but also ensures that the UAV state converges within a given constraint bound.

This paper proposes an approach for fixed-time (FxT) adaptive optimized formation control of nonlinear multi-agent systems (MASs) with unknown nonlinear dynamics and full-state constraints. To address system uncertainty and state constraints while achieving optimality in FxT settings, the paper presents a novel adaptive estimation and analysis. The proposed approach first introduces a tan-type nonlinear mapping to handle state constraints, eliminating the feasibility condition of the conventional barrier Lyapunov function method. Next, the actual optimal controller is iteratively designed using the identifier-actor-critic structure and optimized backstepping method, with neural approximators used to learn system uncertainty. Finally, a monotonically decreasing function is constructed to prove that the designed actor-critic update laws have an upper bound, which is essential for stability analysis. The proposed scheme can ensure that the formation is realized at a fixed time while optimizing a given performance index and meeting the constraint requirements. The simulation results verify the effectiveness of the proposed approachNote to Practitioners—There are inevitable system constraints and model uncertainties in actual physical systems, which may degrade the operational performance of the system. In addition, for practical requirements, designers are eager for multiple agents to achieve the required formation performance at a fast convergence speed. While meeting the requirements of system constraints, it is of practical and theoretical significance to improve the convergence speed and robustness of MASs formation and ensure optimal performance. For this reason, this paper focuses on proposing an effective adaptive FxT-optimized formation control scheme to enhance system performance and convergence speed, which combines nonlinear mapping to address full-state constraints. To achieve optimality under FxT settings, an optimal controller with learning laws is first designed using an identifier-actor-critic structure, in which the identifier is used to learn uncertainty. Then, by constructing a quadratic function, it is proved that the estimation error of the learning law is bounded, thereby forming a new adaptive estimation and analysis scheme.

Notwithstanding the successful design of state-of-the-art cooperative control protocols to accomplish formation tracking for multiple unmanned aerial vehicles (UAVs), the assurance of performance optimality cannot be guaranteed in the face of complex disturbances affecting these multi-UAV systems. In order to surmount this challenge, this research endeavor aims to establish a feedforward-feedback learning-based optimal control methodology to facilitate cooperative UAV formation tracking in the presence of intricate disturbances. To be more precise, by leveraging backstepping-based feedback control, the problem of UAV formation tracking is transformed into an equivalent optimal regulation problem. Consequently, a learning-based feedforward control scheme is devised, wherein the cooperative policy iteration algorithm is formulated based on a two-player zero-sum game. The critic-only echo state network (ESN) is employed to approximate the optimal feedforward control policies, with the inclusion of an online adaptive tuning law and compensation terms to alleviate the persistence of excitation condition and eliminate the need for an initial admissible control. As a result, the closed-loop stability is guaranteed in terms of uniformly ultimately boundedness for tracking errors and ESN weights.Note to Practitioners—In real-world scenarios, the flight of multiple UAVs is invariably affected by intricate disturbances, resulting in compromised tracking precision. There is an urgent need to enhance resistance to disturbances and ensure optimal performance for cooperative formation tracking of multiple UAVs. Beyond the capabilities of model-based controllers, the integration of reinforcement learning has shown promise in achieving robust control actions. By introducing the cooperative policy iteration algorithm based on a two-player zero-sum game, the tracking performances of UAV formation can be further optimized. In order to facilitate the practical application of reinforcement learning in UAV systems, our proposed algorithm addresses the persistency of excitation condition by incorporating innovative compensation terms into the ESN tuning law. Furthermore, we resolve the requirement for initial admissible control by introducing a novel piecewise compensation term into the ESN tuning law, which is based on a newly proposed Lyapunov function.

The artificial potential function (APF) is a widely applied algorithm in collision-free formation control in multiagent systems (MASs). However, it suffers from oscillations and acceleration surges, particularly when the current formation and the desired one conflict. To address this problem and enhance collision-free formation control in MAS, this article introduces the time-derivative of APFs. This approach unifies attractive and repulsive APFs. The gradients of the APFs transform potential and kinetic energy, and the time-derivative of the APF gradients serve as damping terms to dissipate energy. This article discusses the general properties of APFs and introduces a time-variant formation tracking scheme that encompasses existing algorithms as specific instances. Then, a collision-free formation control algorithm is presented. This article gives proof of its Lyapunov stability and collision avoidance ability, followed by a maneuverability analysis from the geometry perspective. By incorporating the time-derivatives of repulsive APF gradients as damping terms, the proposed method mitigates oscillations and acceleration surges caused by conflicting attractive and repulsive effects.

An adaptive fuzzy dynamic event-triggered control approach is proposed for a fleet of fixed-wing unmanned aerial vehicles (UAVs) operating under jointly connected switching topologies. The primary challenge lies in addressing asynchronous switching topologies caused by topology identification delays. To tackle this, asynchronous distributed observers are constructed, and topological switching rules are designed, ensuring that all follower UAVs can estimate the leader UAV's state by leveraging asynchronous distributed state errors. Additionally, a novel dynamic event-triggering mechanism is introduced. Compared to state-of-the-art methods, the proposed triggering function directly couples the external state variable with the last triggered value, dynamically regulates the triggered interval based on the control performance, and minimizes the number of occurrences while maintaining the system performance. An adaptive fuzzy translational and rotational controller is further developed to enable the follower UAVs to accurately track the state of the leader UAV while ensuring that all closed-loop states remain globally uniformly ultimately bounded (GUUB). The proposed strategies are validated for effectiveness and superiority through a semi-physical simulation platform

Global control problem for uncertain nonlinear systems with unknown high-order odd-rational powers, additive sensor faults and fully unknown nonlinearities is investigated. By introducing a novel prescribed-performance transformation, the initial error of each subsystem can be confined in the constrained area for arbitrary system initialization. Then, the designed controller can guarantee global stability of the studied system for all initial system states, and can allow the system nonlinearities to be completely unknown. The odd-rational-power terms are divided into two parts appropriately by using mathematical tools, which facilitates the control design under sensor faults and odd-rational powers while the odd-rational powers are allowed to be unknown. It is proved that the proposed controller can guarantee the global stability of nonlinear systems with unknown system nonlinearities under odd-rational powers and sensor faults. Finally, the advantages and effectiveness of the proposed method are highlighted by both numerical and semi-physical simulations.

This article delves into the adaptive event-triggered control approach for a fleet of underactuated unmanned surface vehicles (USVs) that are interconnected through shared switching topologies. The methodology put forth addresses the asynchronous control challenge within this networked framework. By incorporating an event-triggered mechanism, we have crafted the distributed asynchronous observers to approximate the reference signal, capitalizing on the dynamics of distributed errors. Leveraging fuzzy logical systems, we have devised control inputs for the underactuated USVs by employing the backstepping technique. Utilizing Lyapunov stability theory, we have rigorously demonstrated that these control inputs facilitate tracking of the reference signal by the USVs, contingent upon their relative positions. This article concludes with a validation of the proposed control strategy, showcasing its effectiveness in practical scenarios.

Extensive utilization of deep reinforcement learning (DRL) policy networks in diverse continuous control tasks has raised questions regarding performance degradation in expansive state spaces where the input state norm is larger than that in the training environment. This paper aims to uncover the underlying factors contributing to such performance deterioration when dealing with expanded state spaces, using a novel analysis technique known as state division. In contrast to prior approaches that employ state division merely as a post-hoc explanatory tool, our methodology delves into the intrinsic characteristics of DRL policy networks. Specifically, we demonstrate that the expansion of state space induces the activation function $\tanh$ to exhibit saturability, resulting in the transformation of the state division boundary from nonlinear to linear. Our analysis centers on the paradigm of the double-integrator system, revealing that this gradual shift towards linearity imparts a control behavior reminiscent of bang-bang control. However, the inherent linearity of the division boundary prevents the attainment of an ideal bang-bang control, thereby introducing unavoidable overshooting. Our experimental investigations, employing diverse RL algorithms, establish that this performance phenomenon stems from inherent attributes of the DRL policy network, remaining consistent across various optimization algorithms.

This paper proposes a three-layer unmanned combat aerial vehicle (UCAV) dogfight frame where Deep reinforcement learning (DRL) is responsible for high-level maneuver decision. A four-channel low-level control law is firstly constructed, followed by a library containing eight basic flight maneuvers (BFMs). Double deep Q network (DDQN) is applied for BFM selection in UCAV dogfight, where the opponent strategy during the training process is constructed with DT. Our simulation result shows that, the agent can achieve a win rate of 85.75% against the DT strategy, and positive results when facing various unseen opponents. Based on the proposed frame, interpretability of the DRL-based dogfight is significantly improved. The agent performs yo-yo to adjust its turn rate and gain higher maneuverability. Emergence of “Dive and Chase” behavior also indicates the agent can generate a novel tactic that utilizes the drawback of its opponent.

Presents corrections to the paper, (Correction for "Consensus in High-Power Multiagent Systems With Mixed Unknown Control Directions via Hybrid Nussbaum-Based Control").

This article focuses on fuzzy prescribed performance stabilization of high-order discrete-time systems with multiple unknown nonlinearities. A novel back-steeping framework is employed to devise the unique actual control protocol, while the series of virtual controllers associated with the existing back-stepping are not necessary for the proposed approach. Furthermore, as to the considered high-order system whose subsystems contain unknown dynamics, only one fuzzy approximator is used to directly estimate the final control law. This results in a low-computational model-free design procedure. In particular, a finite-time performance function is developed to sustain the system output within a constraint envelope to satisfy the desired prescribed performance in the discrete-time domain. Finally, the stability of a closed-loop system and the reachability of prescribed performance are proved via Lyapunov synthesis, and the efficiency of the explored method is verified via numerical simulation.

The perturbations in model parameters of hypersonic flight vehicles (HFVs) are highly likely to induce fluctuations in control error, which can potentially render the existing prescribed performance control (PPC) singular and pose a threat to flight safety. Therefore, our objective is to propose an adaptive fuzzy safety control protocol for HFVs that aims to achieve adaptable prescribed behaviors in the presence of parameter perturbations. To accomplish this, we initially develop a novel error-sensing system for timely detection and forecasting of error fluctuations. Building upon this foundation, we further define an adjustment mechanism that appropriately adjusts the upper envelope upward and the lower envelope downward at regular intervals. In contrast to existing fixed PPC approaches, the proposed sensing and adjustment mechanism enables both velocity and altitude tracking errors to satisfy a new type of adaptable prescribed qualities, thereby ensuring safe flight control of HFVs. In addition, we explore low-computational-burden fuzzy approximation techniques that minimize the required online adaptive parameters while guaranteeing excellent real-time control performance. Finally, comparative simulations are conducted to validate the proposed method.

This study introduces a novel adaptive fuzzy fault-tolerant controller designed for tailless aircraft's attitude tracking problem, even in the absence of angular velocity measurements. The controller addresses challenges posed by external disturbances, model uncertainties, and actuator faults. Initially, we propose a fixed-time sliding mode differentiator to estimate unmeasured angular velocity. Model uncertainties and external disturbances are approximated using fuzzy logic systems. Subsequently, a nonsingular fixed-time adaptive fuzzy fault-tolerant control scheme is developed based on the backstepping theory, which actively mitigates the impact of actuator faults, resulting in superior attitude tracking performance. The theoretical foundation of the proposed control scheme guarantees the boundedness and convergence of all closed-loop attitude control system states to a confined region around the origin within a fixed time. Notably, this convergence is achieved regardless of initial errors. Finally, simulation examples are presented to verify the effectiveness of the estimator and controller.

This article introduces an adaptive fuzzy control methodology employing dynamic event-triggered communication for underactuated multiple unmanned surface vehicles (USVs) with modeling uncertainties. The key innovations of the proposed formation control strategy can be summarized as follows: 1) each USV is equipped with a dynamic event-triggered mechanism, ensuring that the controller and neighboring USVs receive position and yaw angle information only when this mechanism is triggered, enhancing communication efficiency; 2) distributed filters are implemented to continuous the event-triggered information; and 3) by employing the fuzzy logical systems (FLSs) to identify the unknown modeling uncertainties, local observers are designed to estimate unavailable velocity and yaw rate. Based on the dynamic event-triggered mechanism, distributed filters and local observers, a nondifferentiable-free backstepping procedure is proposed. The closed-loop stability is proven through Lyapunov stability theory, and Zeno behavior of the dynamic event-triggered mechanism is demonstrated through reductio. Simulation results are presented to validate the effectiveness of the proposed control strategy.

A fuzzy-neural-approximation-based pseudo nonaffine control protocol is proposed for waverider vehicles (WVs), which is capable of guaranteeing tracking errors with desired prescribed performance and rejecting the obstacle of fragility inherent to the traditional prescribed performance control (PPC). The pseudo control is defined to approximate the nonaffine dynamics of WVs, while there is no need of model affinization. Furthermore, fuzzy neural approximators are combined with the adaptive compensation strategy to resist both system uncertainties and external disturbances. Especially, a new type of nonfragile prescribed performance, being able to self-adjust its prescribed funnel, is proposed to remedy the fragility defect associated with the existing PPC. Finally, the realizability of the spurred prescribed performance is proved via stability proof, and the superiority of the addressed design is tested by compared simulations.

This article attempts to realize zero-error constrained tracking for hypersonic flight vehicles (HFVs) subject to unknown control directions and asymmetric flight state constraints. The main challenges of reaching such goals consist in that addressing multiple unknown control directions requires novel conditional inequalities encompassing the summation of multiple Nussbaum integral terms, and in that the summation of conditional inequality may be bounded even when each term approaches infinity individually, but with opposite signs. To handle this challenge, novel Nussbaum functions that are designed in such a way that their signs keep the same on some periods of time are incorporated into the control design, which not only ensures the boundedness of multiple Nussbaum integral terms but preserves that velocity and altitude tracking errors eventually converge to zero. Fuzzy-logic systems (FLSs) are exploited to approximate model uncertainties. Asymmetric integral barrier Lyapunov functions (IBLFs) are adopted to handle the fact that the operating regions of flight state variables are asymmetric in practice, while ensuring the validity of fuzzy-logic approximators. Comparative simulations validate the effectiveness of our proposed methodology in guaranteeing convergence, smoothness, constraints satisfaction, and in handling unknown control directions.

Addressing for the consensus control of multi-agent system (MAS) under the conditions that the directed communication topologies are switching and system nonlinearities are completely unknown, a global consensus control with fully distributed manner is proposed combining with fuzzy logic systems (FLS). FLS are used to approximate the unknown disturbances to enhance system robustness. To deal with the switching topologies of MAS, a reconstructing mechanism using a novel piecewise differentiable function is firstly proposed for the state and consensus errors of agents, which renders the state and consensus errors zero at each switching time instant and facilitates to the control design based on barrier functions, against the unexpected controller action at switching time instant. Incorporating with this reconstructing mechanism, a novel distributed controllers is designed, which is featured with low-complexity structure, fully distributed manner and adaptively reconstructing ability. These technical attempts contribute to some new results. Firstly, the global consensus of MAS with switching directed topologies and unknown nonlinearities is firstly achieved. Secondly, the consensus errors can be explicitly regulated to a prespecified arbitrary small residual set in the sense that the range of the set is the predefined functions given by designer. Finally, simulation results demonstrate the claims.

In this article, a specified-time event-triggered fuzzy adaptive control algorithm is developed to solve the consensus tracking control problem for stochastic high-order nonlinear multiagent networks, which is intrinsically challenging due to the existence of stochasticity and high-order (positive odd integers greater than one) terms. More precisely, a novel specified-time performance function is incorporated into the time-varying high-order tan-type barrier Lyapunov function to guarantee that the tracking errors remain under time-varying constraints within specified time. Combining fuzzy logic systems with the adding on power integrator technique, an adaptive approximation policy is introduced to handle the system uncertainties. Moreover, a new switching threshold event-triggered mechanism is devised to determine the control signals updating instants, which reduces the transmission and computation burden, while resizing the triggering threshold in real time. The Zeno phenomenon is excluded by guaranteeing that the triggering intervals is lower bounded by a positive constant. Two simulation examples are provided to demonstrate the effectiveness of the designed algorithm.

This article proposes an output-feedback control protocol for hypersonic flight vehicle (HFV) swarms considering dynamic event-triggered communication. The peculiarities of the proposed method over existing ones consist in the following: 1) While carrying out scheduled maneuvers, the outputs of follower HFVs converge inside the convex hull spanned by leader HFVs whose task is to maintain a geometric space configuration; 2) a simple nonrecursive output-feedback design is established without involving any intermediate control laws or requiring full-state information; 3) an error-dependent monotonically decreasing exponential term is incorporated into the dynamic event-triggered threshold to reduce the communication bandwidth while preserving the desired track performance and excluding Zeno behavior. Comparative simulation results validate the effectiveness of the proposed methodology.

This article addresses the event-triggered optimal tracking control problem for leader-follower spacecraft formation flying system using the adaptive dynamic programming technique. In order to solve the Hamilton–Jacobi–Bellman equation, a single-critic neural network (NN) is developed to approximate the optimal cost function. Moreover, by combining the parameter projection rule and gradient descent algorithm, a semiglobal adaptive update law is derived to tune the critic NN. In doing so, a continuous near optimal tracking controller is presented. Subsequently, an input-state-dependent event-triggered mechanism is designed to ensure that the near optimal tracking controller is implemented only when specific events occur, which significantly reduces the execution frequency of the control command. Remarkably, benefiting from the construction of an input-based triggering error, the conventional assumption on the Lipschitz continuity of the controller is tactfully removed, thus erasing the computable demand on the unknown Lipschitz constants. Rigorous analysis on the system stability and Zeno-free behavior is provided successively. Finally, numerical simulations on two formation satellites in low Earth orbit validate the effectiveness of the theoretical scheme.

Autonomous vehicle (AV) platoons, especially those with the bidirectional communication topology, have significant practical value, as they not only increase link capacity and reduce vehicle energy consumption, but also reduce the consumption of communication resources. Small gaps between AVs in a platoon easily lead to emergency braking or even collisions between consecutive AVs. This paper applies barrier Lyapunov functions to collision avoidance between AVs in a bidirectional platoon during trajectory tracking. Based on backstepping technique, an adaptive collision-free platoon trajectory tracking control algorithm is developed to distributedly design control laws for each AV in the platoon. The control algorithm does not need to introduce additional car-following models to simulate AV driving, and only needs to integrate the position trajectories of consecutive AVs to avoid inter-vehicle collisions. Two sign functions are introduced into the control laws of each AV to ensure strong string stability for bidirectional AV platoons. Moreover, uncertainties and external disturbances in vehicle motion are effectively compensated by introducing adaptation laws. Strong string stability is rigorously proved. CarSIM-based comparison simulations verify the effectiveness of the proposed control algorithm in avoiding inter-vehicle collisions, compensating for uncertainties in vehicle motion, and suppressing the amplification of spacing errors along the platoon.

This article tackles the distributed output-feedback containment control task for networked hypersonic flight vehicles (HFVs) in the presence of limited communication resources. Distinctive with the state-of-the-art control methodologies designed for single/multiple HFVs, the distinguishing features of our design lie in that: 1) a novel switching event-triggered information transmission mechanism embedded with an error-dependent monotonically decreasing power term is devised in such a way that the frequencies of data transmission among distinct HFVs can be significantly reduced, while at the same time maintaining desired consensus tracking errors; 2) a new set of error variables is delicately formulated such that the containment control task is realized in the sense that all follower HFVs are ensured to converge to a dynamic convex hull formed by leader HFVs; and 3) the priori knowledge of flight state variables is not required during the control design. To be precise, a high-gain observer is resorted to tackle the challenge that the attitude angles information is normally difficult to be obtained precisely in a practical hypersonic regime. Comparative simulation tests are finally carried out to validate the superiorities of the obtained theoretical findings.

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.

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.

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.

This work investigates a reduced-complexity adaptive methodology to consensus tracking for a team of uncertain high-order nonlinear systems with switched (possibly asynchronous) dynamics. It is well known that high-order nonlinear systems are intrinsically challenging as feedback linearization and backstepping methods successfully developed for low-order systems fail to work. Even the adding-one-power-integrator methodology, well explored for the single-agent high-order case, presents some complexity issues and is unsuited for distributed control. At the core of the proposed distributed methodology is a newly proposed definition for separable functions: this definition allows the formulation of a separation-based lemma to handle the high-order terms with reduced complexity in the control design. Complexity is reduced in a twofold sense: the control gain of each virtual control law does not have to be incorporated in the next virtual control law iteratively, thus leading to a simpler expression of the control laws; the power of the virtual and actual control laws increases only proportionally (rather than exponentially) with the order of the systems, dramatically reducing high-gain issues.

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.

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.

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 noa prioribound 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 ofa prioribounds 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.

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.

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.

Aiming at the collision between the unmanned aerial vehicles (UAVs) in tracking motion target due to the inconsistency information, we design the unmanned aerial vehicle (UAV) formation cooperative tracking controller and analyze the flight-stability of the designed controller in the case of only knowing the UAV local information. Recently, a Lyapunov guidance vector field approach is proposed to achieve the desired circular trajectory in the paper. The path planning of a single UAV and the Multi-UAVs cooperative formation tracking motion target are simultaneously studied. In the ideal case, a guidance vector field method is proposed for the heading convergence, which has the advantage of analyzing and solving the collision problem between the unmanned aerial vehicles (UAVs). Further, a variable airspeed controller is used to maintain the UAV cooperative formation flight in a circular orbit, and an adaptive estimate is introduced to ensure the flight-stability of a circular orbit in the case of unknown wind and moving targets. In the process of the UAV formation tracking motion targets, we use a variable airspeed controller to achieve the desired angular spacing. In this paper, the designed controller laws are decentralized based on the local information. Meanwhile, the simulation shows that the designed controller has a good the flight-stability in the process of tracking motion target.