This article investigates a conflict-constrained control method for switched multiagent systems with nonaffine nonlinear faults. Existing studies on state constraints often assume that the reference signal always stays within the constraint set. However, in practice, constraints may be dynamically detected during system operation and conflict with predefined reference signals, causing brief violations of the constraint boundaries. When the reference signal cannot remain within the prescribed range, many backstepping control methods based on barrier Lyapunov function and nonlinear transformations become ineffective. To address this, a new safe reference signal is constructed by using a virtual circle approach, and a conflict-constrained control method is proposed. By combining a common Lyapunov function and the radial basis function neural network, the effects of switching behavior and nonaffine nonlinear faults can be effectively compensated. A shift function is also introduced in the coordinate transformation to further relax constraints on initial values. The proposed method is validated through multiple simulation experiments.

Most output-constrained methods necessitate reference command within a predefined safe region, without considering cases where the command itself may conflict with safety boundaries. To handle this problem, this article proposes a predefined-time safe cooperative control scheme for multiagent systems under output constraints, privacy preservation and unknown disturbances. At the communication layer, an encryption-decryption mechanism is developed to safeguard information exchange among agents, preventing internal states from being identified by eavesdroppers. At the control layer, to ensure strict adherence to output constraints regardless of whether the original command complies with safety limits, an improved boundary protection method is explored to generate a safety reference trajectory, which is subsequently used in the controller design. Adaptive laws are then formulated to counteract the effects of unknown nonlinearities and disturbances. Finally, by leveraging predefined-time stability theory, a predefined-time safe cooperative controller is designed to ensure error convergence within a user-defined settling time. Theoretical analysis rigorously confirms the closed-loop stability, and simulations verify the effectiveness of the proposed method.

In this paper, a predictor-based fuzzy safety control framework is proposed for uncertain nonlinear systems with reference-independent output constraints. A safety-compliant reference regulation mechanism is developed to reshape potentially infeasible references into safety reference signals, thereby eliminating the conventional admissible-reference assumption. A novel dual-constraint framework is developed to simultaneously enforce output constraints and prescribed performance in a unified manner, allowing explicit boundary construction without undesirable coupling. To handle uncertainty, a predictor-based simplified fuzzy approximator is constructed, in which prediction errors rather than tracking errors are employed to update the norm of weight vector, substantially reducing computation complexity and alleviating transient chattering. Meanwhile, the use of prediction errors also decouples control and estimation loops, thereby simplifying the stability analysis. Furthermore, a new transient analysis theorem is established to quantify chattering degree and explicitly reveal the influence of design parameters. Rigorous Lyapunov-based analysis proves that all closed-loop signals remain bounded, and both safety and performance constraints can be strictly satisfied. Simulation results validate the usefulness and superiority the proposed method.

The integrated design of perception and enclosing control against an unknown target is a promising enabler for autonomous vehicles performing surveillance, entrapment, and escort missions. To address this challenge, this article develops an estimator–controller framework for target localization and enclosing under communication and measurement constraints. First, to improve the convergence performance, a novel fixed-time estimator is designed using bearing-only information, ensuring that target position is reconstructed within a fixed time. Then, to overcome the limitations of prior works that mainly rely on circular or elliptical orbits, a geometric path generator is exploited to synthesize arbitrary-shaped smooth orbits, based on which a fixed-time controller is constructed to guide the vehicle to surround the target along the prescribed path. For multivehicle scenarios, an arc length-guided decentralized formation strategy is proposed to maintain prespecified arc length separation between adjacent vehicles without relying on communication, which is particularly suitable for communication-denied environments. Finally, the fixed-time stability in the closed-loop system is rigorously proved by Lyapunov-based analysis, and both simulation and experimental results validate the effectiveness of the proposed framework.

This paper proposes some novel algorithms for photovoltaic (PV) energy DC circuit conversion systems to extract maximum power under the constraint of information security. First, a discrete-degree-judging-based composite chaotic mask function generation (DCCG) algorithm is constructed. This algorithm utilizes all the states in the chaotic system to construct a more volatile chaotic signal, effectively increasing the complexity of the mask signal and enhancing the encryption of the photovoltaic energy system. Secondly, for the superposition process of the encrypted signal and the mask signal, a Gaussian-high-dimensional mapping-based adaptive weight calculation (GMAWC) method is designed to adaptively adjust the superposition weights. This adaptive adjustment helps avoid issues of insufficient encryption and excessive noise interference caused by the large value domain variation between the mask signal and the system states. Moreover, an upper bound on the decryption bias tolerated by the PV energy conversion system is explored, ensuring that the algorithmic framework has greater decryption bias tolerance while achieving maximum power extraction. Finally, both theoretical analysis and data results show that the proposed encryption-decryption-control framework has better security, applicability and decryption bias tolerance.

In this article, the tracking control problem for nonlinear systems is investigated. For the first time, a fixed-time dynamic signal is constructed to handle unmodeled dynamics. A novel error-based function is first designed and applied to prescribed performance control, significantly enhancing the transient performance of the system. To mitigate the issue of extensive differential calculations, a modified fixed-time dynamic surface technique is incorporated into the backstepping design process. Combining the backstepping technique with fixed-time stability theory, a fast practical fixed-time controller is proposed to effectively address the control problem. Finally, the proposed scheme is validated for its effectiveness through the practical simulation example.

In this article, a finite-time learning-based optimal tracking problem for nonlinear systems with preassigned performance constraint is investigated. By designing a state predictor, a fuzzy approximator driven by prediction errors rather than tracking errors is formulated to precisely compensate the effect of the unknown uncertainties. The design realizes a decoupling of control and estimation loops, effectively ensuring transient approximation performance and avoiding chattering induced by nonzero initial tracking errors. Then, based on the estimated components, a robust steady-state control scheme embedded with a prescribed performance mechanism is tailored to guarantee that the output state can converge to a predefined range within a preassigned time. This endows the designed controller with a specified time tracking capability independence on control parameters. To make a tradeoff between tracking precision and energy cost, a finite-time learning-based optimal control policy is exploited by utilizing adaptive dynamic programming technique to serve as an adaptive supplementary controller, where single critic neural network is trained for acquiring the solution of the Hamilton–Jacobi–Bellman equation. Compared with the traditional gradient descent method, the established learning law is updated by introducing an auxiliary variable, which enhances learning performance and guarantees finite-time convergence of adaptive weights. Simulation examples examine the effectiveness and superiority of the suggested scheme.

This paper presents a distributed saturation-tolerant fuzzy control scheme for stochastic multiagent systems (MASs) with unknown measurement sensitivity, where the states, consensus errors, and control inputs all are constrained. A concise nonlinear mapping is tactfully devised to impose appropriate constraints on full states without reliance on feasibility conditions. Besides, a novel resilient quantitative prescribed performance control (RQPPC) is developed, which incorporates finite-time performance boundaries with input-relevant dynamic boundaries generated by an auxiliary system, being expected to flexibly adapt to input saturation without violating performance constraints. Building upon the RQPPC, the transient and steady-state behaviors of consensus errors can be quantitatively predesigned free from repeated parameter tuning processes. Uncertain nonlinear terms in the converted system are successfully addressed by fuzzy approximation. The superiority and effectiveness of theproposed algorithms are confirmed by simulations with comparisons.

This article studies a prescribed finite-time consensus problem for uncertain nonlinear multiagent systems (MASs) with event-triggered updates. First, the novel finite-time performance boundaries are proposed to ensure that consensus deviations converge to the predefined steady-state zones within a preassigned time, and by using asymmetrically parallel boundaries to constrain consensus errors to narrow feasible regions, small overshoots of consensus errors are assured. Second, by utilizing the inherent approximation property of fuzzy logic systems (FLSs), a fuzzy state observer is devised to recover the unmeasurable states. Based on the observation outcomes, an improved event-triggered output-feedback controller is synthesized so that the number of control input updates is reduced without incurring an evidently deteriorated control performance. The salient merits of the proposed approach are that all consensus errors are free from great overshoots, while settling time can be explicitly assigned in advance. Finally, two examples are given to verify the validity of theoretical results.

In this article, a new leader-following tracking control approach is investigated for stochastic multiagent systems with multibridge-hole output constraints. The multibridge-hole output constraints mean that the output of the system is constrained in some intervals and unconstrained in other intervals. The constrained and unconstrained intervals can be set arbitrarily. By designing a new shift function to construct the barrier Lyapunov function, the optimal controller is constructed by combining the backstepping technique with the adaptive dynamic programming technique. The model network is used to estimate the unknown disturbances and uncertainty terms in the system. The critic network and the actor network are constructed such that the designed controller adheres to the Bellman optimality principle and gives the optimal solution of the system. The proposed control method is versatile and compatible with various types of output constrained control problems, such as unconstrained control problems, constrained control problems, and delay constrained problems without changing the structure of the controller. Finally, some simulation results are given to verify the effectiveness of the method.

This article investigates an optimized containment control problem for multiagent systems (MASs), where all followers are subject to deferred full-state constraints. A universal nonlinear transformation is proposed for simultaneously handling the cases with and without constraints. Particularly, for the constrained case, initial values of states are flexibly managed to the midpoint between upper and lower boundaries by utilizing a state-shifting function, thus eliminating the initial restriction conditions. By deferred constraints, the state is forced to fall back into the restrictive boundaries within a preassigned time. A neural network (NN)-based reinforcement learning (RL) algorithm is executed under the identifier-critic-actor architecture, where the Hamilton-Jacobi-Bellman (HJB) equation is built in every subsystem to optimize control performance. For actor and critic NNs, updating laws are simplified, since the gradient descent method is performed based on a simple positive function rather than square of Bellman residual error. In view of the Lyapunov stability theorem and graph theory, it is proved that all signals are bounded and the outputs of followers can eventually enter into the convex hull constituted by leaders. Finally, simulations confirm the validity of the proposed approach.

This article analyzes and validates an approach of integration of adaptive dynamic programming (ADP) and adaptive fault-tolerant control (FTC) technique to address the consensus control problem for semi-Markovian jump multiagent systems having actuator bias faults. A semi-Markovian process, a more versatile stochastic process, is employed to characterize the parameter variations that arise from the intricacies of the environment. The reliance on accurate knowledge of system dynamics is overcome through the utilization of an actor-critic neural network structure within the ADP algorithm. A data-driven FTC scheme is introduced, which enables online adjustment and automatic compensation of actuator bias faults. It has been demonstrated that the signals generated by the controlled system exhibit uniform boundedness. Additionally, the followers' states can achieve and maintain consensus with that of the leader. Ultimately, the simulation results are given to demonstrate the efficacy of the designed theoretical findings.

This article investigates the problem of adaptive fuzzy consensus control for a class of interconnected nonlinear multiagent systems. To effectively address the dual requirement of full-state constraints and prescribed performance, we propose a novel unified barrier function that effectively constrains the states and errors separately. This approach eliminates the need for tedious computations, allowing for direct presetting of state and error bounds. The concept of “bridge hole” is introduced, referring to the states' transition from unconstrained to constrained and back to unconstrained. The control method demonstrates the capability to handle multiple bridges, with the number of bridges being able to be${\bm {n}}$or 0. Furthermore, event-triggered mechanisms are also considered among neighbors and the internal event-triggered mechanism in the actuator-to-controller channel, with the aim of minimizing the communication burden. Finally, some simulation results are provided to validate the effectiveness of the approach.

This article presents an adaptive fuzzy bipartite consensus tracking control scheme for nonlinear multiagent systems (MASs) with dead-zone inputs and actuator faults. The consensus problem for unbalanced communication topology of the MASs is difficult to deal with. First, the hierarchical algorithm is introduced to transform the consensus tracking problem for multiagents into the tracking problem of the single agent. Then, the novel error transformation and coordinate transformation are proposed based on the hierarchical design theory, leading to the condition of global Laplacian matrix information unnecessary so that the computational difficulty and communication burden are significantly reduced. Besides, the control performance would be seriously affected by the input constraints such as dead-zone and faults. There is a balance between the input constraints and the output constraints for MASs, but these two constraints are handled independently in the most existing works. Thus, a flexible performance function is developed to balance the system performance and the stability requirements by flexible signal switching. The proposed adaptive fuzzy control scheme ensures that not only the influence of the dead-zone and various types of actuator faults is eliminated, but also the preassigned finite-time tracking performance is achieved. Finally, the simulation experiment is conducted to verify the effectiveness of the established control scheme.

This article investigates a finite-time fuzzy quantized control problem for a class of nonlinear systems considering deferred constraints. Instead of the tracking errors themselves, the auxiliary error variables constructed via the shifting function are employed into nonlogarithm barrier Lyapunov function to perform error constraints, not only making the restrictive conditions in initial phase be removed but also ensuring tracking errors to evolve within the preassigned regions after a given time. Then, to allow for a reduced computational cost concerning fuzzy/neural approximators, a single parameter updating based fuzzy wavelet neural network is devised to approximate the unknown nonlinearity acting on every subsystem. Furthermore, by using hysteresis quantizer to convert continuous control inputs into discrete scalars, a robust fuzzy quantized controller is synthesized with the aid of a novel quantization decomposition scheme, where the problem of constrained data bandwidth is successfully handled without involving chattering in control signals. Finally, simulations confirm the benefits and efficiency of the proposed method.

In this article, the event-triggered saturation-tolerant control problem of nonlinear multiagent systems (MASs) is investigated based on the finite-time fuzzy composite learning approach. Specifically, a novel concept, named as deferred saturation-tolerant prescribed performance control, is proposed, which guarantees the flexible prescribed performance in the face of input saturation, while there are no needs of initial restrictions on distributed errors. Moreover, by extracting weight errors from filtering operations and auxiliary variables, a finite-time fuzzy composite learning rule driven by weight and distributed errors is developed for improving the learning performance and ensuring that unknown nonlinearities are precisely estimated. Then, resorting to event-triggered communication mechanism, signal transmissions among connected agents only occur when triggering conditions are satisfied, contributing to a reduced communication burden. Finally, simulations with comparative studies are provided to confirm the effectiveness and superiority of the proposed method.

This article addresses a fuzzy-quantized elliptical target encircling control of quadrotors with arbitrary-time convergence, consisting of translational and rotational designs. At the translational level, an arbitrary-time elliptical guidance rule is designed to empower quadrotors to move along the predefined elliptical path within a prescribed settling time free from initial conditions. At the rotational level, a fuzzy-quantized attitude regulation protocol is developed to stabilize the attitude deviation, where a quantized fuzzy logic is artfully constructed to online recover uncertainties via updating weights with finite states scheduled by a hysteresis quantizer, greatly reducing signal transmission burden. Finally, the overall system stability is demonstrated via input-to-state stable principle, while not only simulations but also experiments are given to verify the efficacy of suggested approach.

This paper presents an elliptical target encircling control policy of quadrotors subject to uncertainties and aperiodic signals updating based on pure bearing measurements. At the translational level, by resorting to bearing-only data, rather than prior position and velocity information of target, a position estimator is constructed for locating the unknown target. Utilizing the localization result from position estimator, compared to the existing circular surrounding alternatives, a planar elliptical guidance law capable of adapting more sophisticated operational environment, and a longitudinal control law are synchronously established to generate the velocity reference. At the rotational level, an unknown system dynamics estimator (USDE) is introduced to online neutralize total adverse effect induced by exogenous disturbances and internal uncertainties, where high precision estimation and low computational complexity can be guaranteed with only one tuning argument, then an event-triggered robust attitude controller carrying a sampling deviation compensation item is synthesized accomplishing elliptical encircling for a dynamic target without involving Zeno behavior. Finally, stability of closed-loop system is analyzed via input-to-state stable principle, while simulations are given to verify the efficacy of suggested approach.