This article focuses on the design of inverse optimal control (IOC) based on inverse reinforcement learning (IRL) for distributed parameter systems (DPSs) with unknown dynamic parameters. First, considering that the optimal policies may not display the expected performance when they are migrated to real-world DPSs due to model bias, the human-behavior learning (HBL) strategy is utilized to transfer the optimal strategy of the reference systems to the real-world DPSs. Furthermore, to avoid performance degradation caused by predefined reward-weight matrices during the optimal control process of the reference systems, the IRL policy iteration algorithm is employed to realize the IOC of the reference systems, and the equivalent reward-weight matrices and optimal control gains of the reference systems are solved. Finally, the effectiveness and superiority of the algorithms are verified in simulation.

This paper investigates the problem of distributed state monitoring for intelligent connected vehicle (ICV) fleets based on embedded trust levels within a zero-trust network architecture (ZTNA) while considering the impact of unknown input disturbances. The ZTNA significantly enhances the overall security performance of the vehicle fleet. To implement the “never trust, always verify” principle, we first analyze the effects of zero-trust architecture on the communication relationships among intelligent connected vehicles (ICVs). Drawing inspiration from human trust dynamics, rules for establishing and reducing trust levels between vehicles are formulated. A variable-directed graph is employed to represent the communication topology of the entire vehicle fleet. Next, a distributed unknown input monitor structure is proposed using known information and verified global state monitoring results from neighboring vehicles with embedded trust levels. By utilizing selectable matrices to process local and global control inputs, any individual vehicle can monitor the state of the entire fleet. Finally, a simulation analysis of a fleet of six vehicle nodes demonstrates the correctness and effectiveness of the proposed method.

This article investigates the predefined-time composite adaptive fuzzy bipartite consensus control problem of multiple six-rotor uncrewed aerial vehicle systems (UAVs). The command filter is integrated into the backstepping design process to overcome the “complexity explosion” and singularity problems, while the predefined-time error compensation signals are developed to remove the influence of filtering error on the system. Fuzzy logic systems are applied to deal with unknown nonlinearity and unmodeled parts existing in the UAVs' dynamics, with a serial-parallel estimation model being constructed to enhance approximation accuracy. The composite fuzzy adaptive laws are derived, embedding the prediction error of the model to ensure that the prediction error exhibits predefined-time convergence. To implement both cooperative and competitive interactions, a predefined-time adaptive bipartite consensus control scheme is proposed for multi-UAVs based on a signed graph. Furthermore, an auxiliary dynamic variable is introduced for the design of a dynamic event-triggered mechanism, thereby reducing communication resource consumption. The proposed control scheme guarantees convergence of bipartite consensus errors to a small neighborhood around the origin in a predefined time, and explicitly excludes Zeno behavior. A simulation example demonstrates the effectiveness of the developed control algorithm.

This article delves into the cone-invariance, stability, and stabilization issues of linear discrete-time two-dimensional (2-D) systems with state trajectories confined to polyhedral proper cones. Initially, we introduce several novel equivalent conditions that guarantee the cone-invariance of 2-D systems within polyhedral proper cones. Subsequently, the concept of 2-D positive systems (PSs) is extended to cone-invariant systems (CISs), broadening the definitions of the state space. A key innovation lies in establishing a mapping relationship that successfully transforms state trajectories from general polyhedral cones to non-negative orthants, thereby facilitating the equivalence of stability problems between 2-D CISs and 2-D PSs. Furthermore, we tackle the stabilization problem under polyhedral cone constraints and derive an innovative state feedback control law to achieve system stability. Ultimately, simulation examples illustrate the effectiveness and practical applicability of the proposed results.

This study proposes a new adaptive Kalman filter for high-precision cooperative target tracking based on the Singer acceleration model. In cooperative scenarios, the maneuver time constant alpha is known in advance through mission planning or communication between targets, allowing a physically consistent and structure-preserving noise modeling. Traditional approaches often necessitate the estimation of the full noise covariance matrix, which can be computationally intensive and prone to inaccuracies and spurious state correlations. To overcome this limitation, this work elaborates a novel online power spectral density (PSD) estimation scheme. Using known alpha to reduce the number of unknown variables, the new approach can improve the accuracy and reliability of the estimation. Numerical experiments in cooperative target tracking demonstrate that the refined algorithm achieves robust adaptability to dynamic noise that varies over time, provides high-precision state estimates, and maintains low computational complexity.

Recently, the operational domain of uncrewed aerial vehicles (UAVs) has expanded from outdoor environments to indoor spaces such as factories, power plants, and tunnels. In these environments, where the Global Navigation Satellite Systems (GNSS) is unavailable, and the risk of collision with surrounding structures is high, a positioning system with high accuracy and fast update rates is essential. In this article, we propose a novel 3-D positioning algorithm for UAVs operating in a GNSS-denied environment. The proposed algorithm is based on sensor fusion of the inertial measurement unit (IMU) and ultra-wideband-based wireless sensor network (UWB-WSN). The proposed algorithm, referred to as the recursive extended finite-memory positioning (REFMP), features a unique finite-memory (FM) structure that estimates the current position using only a limited set of recent information. This FM structure endows the algorithm with robustness against measurement model errors, linearization errors, and uncertainties in initial position information. We evaluated the performance of the proposed algorithm through both simulations and real-world experiments under various challenging conditions, including UAV collisions with walls, aggressive maneuvers inducing sensor measurement errors, and scenarios with uncertain initial position information. By comparing the proposed algorithm with state-of-the-art UAV positioning algorithms based on IMU and sensor fusion, we demonstrate the superior positioning accuracy and robustness of REFMP under harsh conditions.

This article investigates dynamic event-triggered mechanism (DETM)-based output feedback quantized control designs for positive Markov jump systems (PMJSs) under aperiodic denial-of-service (DoS) attacks, with a joint design of DETM and quantization parameters. First, a novel linear co-positive structure-based DETM is proposed for networked PMJSs under aperiodic DoS attacks and output logarithmic quantization, incorporating periodic sampling to avoid Zeno behavior while reducing redundant data transmission. Notably, the proposed DETM degenerates into the static event-triggered mechanism (SETM) as a special case. Next, considering DETM inactivity during DoS attack intervals, a novel mode-dependent piecewise linear co-positive Lyapunov functional approach incorporating a dynamic variable is developed based on DoS attack active/sleep intervals. Under this framework, sufficient conditions ensuring closed-loop positivity and stochastic stability are derived for PMJSs under aperiodic DoS attacks and output quantization. Furthermore, by applying matrix decomposition techniques to design controller gain matrices, an effective mode-dependent piecewise linear programming (LP)-based output-feedback control scheme is proposed, which co-designs DETM and quantizer parameters. Compared with a traditional linear matrix inequality (LMI)-based control scheme, the LP-based scheme significantly reduces computational complexity while alleviating conservatism in design conditions. Finally, control designs under three special cases are systematically discussed, and some examples are provided to validate the theoretical results.

This paper investigates a fixed-time reinforcement learning framework for containment coordination of networked aerial vehicles in dynamic inspection tasks. The proposed framework integrates sliding mode concepts to enhance the robustness of the system. To address the challenge of limited leader information sharing among followers, a distributed estimation mechanism is designed to reconstruct leader states in a fixed time, thereby eliminating the need for fully informed followers. Based on this, a critic-only adaptive dynamic programming (ADP) framework is employed to learn optimal coordination strategies for both containment behavior and attitude tracking under time-varying environments. Within this ADP framework, the critic neural network (NN) weights are adjusted according to a fixed-time convergent update rule constructed from auxiliary errors associated with the Bellman residual. The framework ensures that the networked aerial vehicle system converges within a fixed time, and the corresponding convergence properties are rigorously verified through comprehensive mathematical analysis. Simulation results demonstrate that the proposed method achieves rapid convergence and improved containment performance in inspection tasks.

This article investigates the leader-following cluster consensus for generic linear heterogeneous multiagent systems (MASs). Unlike the existing research, a novel event-triggered (ET) control mechanism is designed and developed on the transmission side of the agents, over directed communication topologies, to reduce communication load. For this purpose, a variable threshold function as the fully distributed ET condition (ETC) is suggested, which provides a smooth transition and considers both maximum and minimum threshold levels for triggering. A relative-state feedback-based cluster consensus control protocol is designed by considering the cooperative and competitive interaction behavior of agents. Then, the convergence analysis is performed by utilizing the Lyapunov method. This work is then further extended for the ET observer-based output feedback cluster consensus problem. The proposed ETC naturally eliminates the Zeno behavior for each agent. In contrast to existing methods, a variable threshold-based ET scheme, a cooperation-competition network, and an elimination of Zeno behavior for both state-based and output-based methods have been considered for the leader-following cluster consensus. Finally, illustrative examples are used to validate the theoretical results.

This paper addresses the distributed predefined-time (PT) exact consensus tracking problem for nonlinear second-order multi-agent systems subject to disturbances and deception attacks by developing a three-player mixed zero-sum game-based strategy. Different from conventional distributed control methods that typically require separate design of a leader state observer and controller, the proposed approach directly embeds the consensus tracking error associated with the communication topology into a zero-sum differential game framework, without resorting to explicit observer design. On this basis, a distributed control architecture is established without requiring online global topological information or additional observer construction, thus effectively reducing system implementation complexity. Moreover, an adaptive approximation mechanism based on a critic neural network is introduced to estimate the Nash equilibrium strategy online, yielding a realizable approximate optimal control policy. The proposed method not only guarantees the consensus error converges to zero within a PT but also yields a realizable approximate optimal policy in the presence of attacks and disturbances, while ensuring uniform boundedness of all closed-loop signals. Finally, a numerical simulation is provided to demonstrate the effectiveness of the proposed method.

This article investigates the fuzzy event-triggered sliding mode consensus tracking problem for T-S heterogeneous agent networks by a Lie-algebra decoupling approach. To relax the restrictive assumption of identical input matrices in conventional fuzzy sliding mode control (SMC), we employ Lie-algebra controllability conditions to decouple agent dynamics. Based on the decoupled dynamics, a sliding surface is designed to ensure rapid consensus convergence and robustness to uncertainties. Subsequently, a novel event-triggered mechanism (ETM) is proposed by directly utilizing the sliding surface error. This design dynamically adjusts the triggering threshold, effectively reducing transmission resources without disrupting system stability. To mitigate performance degradation during event-triggered intervals, a hybrid controller is developed. It integrates adaptive nonlinear functions with boundary layer techniques, maintaining performance during nontriggering intervals. Furthermore, rigorous analysis based on Lyapunov theory effectively demonstrates the reachability of the sliding surface and the leader-following consensus of the T-S fuzzy multiagent systems (MASs). Concurrently, the exclusion of Zeno behavior is achieved by establishing theoretical constraints on the minimum time interval between successive events. Ultimately, simulation results demonstrate the scheme's effectiveness.

This study develops a $\boldsymbol{q}$ -axis current loop-free positioning technique for permanent magnet synchronous motors (PMSMs), integrating a specially structured proportional-integral (PI) controller, a model-independent cascade observer, and a fourth-order disturbance observer (FO-DOB). The method effectively handles PMSM parameter and load uncertainties and remains practical for industrial use. The core contribution lies in applying pole-zero cancellation (PZC) to both estimation and control loops: 1) the tailored gains of the observers induce PZC, yielding first-order estimation error dynamics independent of the PMSM model; 2) the filtered position output by the observer drives a FO-DOB to achieve triply-damped disturbance estimation; and 3) the combined observer and FO-DOB construct the PI-type controller, whose gain structure assigns a critically damped closed-loop response through PZC. Experimental validation is conducted on a 1-kW PMSM dynamometer.

This paper investigates the adaptive interval type-2 (IT2) fuzzy dynamic memory event-triggered consensus (ETC) problem for nonlinear multi-agent systems (NMASs) under jointly connected topologies. Considering that the topologies may not always remain connected, distributed compensators are developed to estimate the leader’s states. In the design of the proposed scheme, IT2 fuzzy logic systems (FLSs) are employed to better approximate the unknown nonlinear terms. A consensus protocol incorporating a dynamic memory event-triggering mechanism (DMETM) is developed. Then, it is further shown that the proposed schemes guarantee leader-following consensus (LFC) for NMASs under jointly connected topologies while excluding the Zeno phenomenon. At last, simulations on the system of four single-link manipulators and a numerical example are performed to verify the effectiveness of the proposed consensus schemes.

Formation control is a promising technique for the coordinated operation of networked marine surface vehicles (NMSVs) to provide mobility services in the marine Internet of Things (M-IoTs). In practical M-IoTs applications, individual vehicles are typically required to satisfy their own local mission objectives while maintaining cooperative formation behavior with the entire fleet. However, most existing formation control approaches fail to explicitly address the individual formation cost of local vehicles in such scenarios. Moreover, limited communication bandwidth, unreliable link connections, and complex model dynamics further complicate this problem for NMSVs. To address these challenges, this paper develops a non-cooperative dynamic formation game (NCDFG) framework to address the formation problem of NMSVs from the perspective of optimizing individual mission costs of vehicles. To solve the formulated problem, a distributed segment Nash Equilibrium (NE) seeker (DSNES) is first designed to generate the optimal formation trajectories for local vehicles under discrete communications and unreliable links. To ensure the network resource utilization efficiencies, the event-driven communication scheme related to the DSNES is subsequently developed. Notably, to guarantee the NE convergence of DSNES under event-driven communications and possible link interruptions, a segment estimation protocol is integrated to obtain accurate internal states of neighbors between successive communications. Building upon the DSNES, the local NE trajectory tracking controller (NETTC) is finally developed to steer underactuated vehicles moving along their corresponding NE trajectories. Finally, theoretical analysis and semi-physical validations are conducted to demonstrate the effectiveness of the proposed approach.

Enhanced formation flexibility stringently commands on the connection stability of networked systems, while vehicle-equipped facilities limit interaction coherence. Either a prolonged communication range or a malicious attack leaves the networked system suffering from disconnections. Functioned as randomly occurring transmission packet dropouts, we consider the noncooperative game played by networked uncrewed surface vehicles (NUSVs), where game theory enables NUSVs' active mission assessment and thereby enhances flexibility. For loss rejection, a probability assignment event-triggered mechanism (PAETM) is explored under two cases, i.e., loss occurrence obeying a Bernoulli distribution and irregular occurrence. To begin with, PAETM is first proposed for the former. Assigned with specifically designed weights, historically triggered packets are employed to manage individual broadcast timing. By virtue of PAETM, neither explicit nor implicit loss acknowledgment is required, thus meriting acknowledgment-free. In this sequel, irregular loss occurrence, which is more generic, is considered by decomposing it into a piece-wise regular manner. Theoretical analysis and semi-physical simulations are performed to show the effectiveness and superiority of the proposed algorithm. Note to Practitioners-Formation control completed by NUSVs significantly contributes to marine applications. For great environment adaptability, NUSVs prefer to form the desired configuration in a flexible manner, including saved energy and reduced convergence duration. Nevertheless, most formation protocols are designed through the tracking manner, where individual character and autonomous evaluation are neglected. As a solution, the noncooperative game played by NUSVs endows each vehicle with self-adjustable capability. Furthermore, vehicle-equipped communication facilities face unexpected interaction failures when suffering from potential electromagnetic interference and terrain blocking, especially for a flexibility-enhanced system. To prevent such damage, a probability-assigned mechanism is developed in this paper. Thus, both regularly and irregularly occurring inter-vehicle packet dropouts are formulated and tackled in an acknowledgment-free manner.

This paper aims to design a fault detection (FD) filter for continuous two-dimensional (2-D) Markov jump positive systems (MJPSs) with constant state delays that ensures the stochastic stability and L1/L-performances of the filtering augmented system. The L1-gain and L-performances of the delayed continuous 2-D MJPSs are investigated, and their exact values can be calculated. Necessary and sufficient conditions for ensuring the L1-gain performance and L-index of the system are derived. On this basis, sufficient conditions for the existence of the mixed L1/L-FD filter are achieved and then are solved via an iterative algorithm. Finally, a numerical example validates the preceding theoretical findings.

This article proposes a multiloop feedback system that utilizes the virtual damping (VD) injection technique to ensure critically damped input-output behavior for each quadcopter in a multiagent topology. The proposed framework also considers system nonlinearity and model-plant mismatches to ensure robustness while maintaining a simple proportional-derivative (PD) controller design for convenient industrialization. The benefits of the proposed system are threefold. First, the observer allows both the outer and inner loops to feed back the time-derivative components of the position and attitude measurements, without requiring any system model information. Second, the PD-type adaptive controllers for each loop incorporate observer-based VD components into the feedforward and feedback loops to attenuate disturbances. Third, based on a nonlinear combination of two design parameters for VD and the desired convergence rate, the critically damped transfer function for the closed loop is determined using the order-reduction technique. A hardware testbed with the three quadcopters confirmed the practical advantages of the proposed solution in maintaining specified formations.

The proposed multiloop double-integral feedback framework addresses the trajectory tracking problem of two-wheeled mobile robots (TWMRs), achieving critically damped closed-loop behavior despite model-plant mismatches. By explicitly considering direct current motor (DCM) dynamics and load uncertainties, the method enhances robustness under diverse road conditions. The core contributions are threefold: 1) a model-free observer employing artificial damping (AD) injection diagonalizes estimation error dynamics, simplifying parameter tuning; 2) AD-based double-integral controllers in the outer and inner loops drive position and attitude errors toward critically damped responses; and 3) an AD-based proportional-integral controller ensures first-order convergence of DCM speed errors while suppressing load disturbances. Experimental validation on a prototype TWMR platform equipped with a MyRIO1900 controller and OptiTrack positioning confirms substantial improvements in closed-loop performance.

Differential privacy methods have received attention for addressing data privacy in dynamic systems. Designing a novel differential privacy mechanism to achieve higher privacy and exploring the influence of dynamic system properties on privacy are vital issues. This paper investigates a novel differential privacy-preserving mechanism under switching control. System mode switching is developed to improve data privacy, and controllers are designed to weaken the effects of privacy noise on system stability. (i) A dual-differential privacy-preserving (DDPP) mechanism combining global privacy and local privacy is applied to improve the data privacy of dynamic systems. (ii) Implementing local privacy in the model output signal provides higher data availability. Then, more accurate closed-loop models can be obtained than global privacy, and control accuracy can be improved. (iii) A mode-switching law is presented to achieve higher privacy. Moreover, dual-dwell time bounds that ensure the privacy and stability of the privacy-preserving system under switching control are presented. Finally, a numerical simulation is provided to verify the effectiveness of the proposed method. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.