This paper focuses on estimating the reachable set of cone-preserving linear systems subject to constrained exogenous inputs. Specifically, the input signal belongs to the space of essentially bounded vector-valued functions equipped with cone linear absolute norms, or to the space of vector-valued functions with the integral of their cone linear absolute norms being bounded. By adopting an auxiliary function composed of the maximum of a set of linear functions with respect to system states, sufficient conditions are provided to ensure that the reachable set is bounded by a given polyhedron. This treatment does not require the auxiliary function to be positive definite, and it is compatible with unstable systems, which shows a significant improvement over existing methods that use Lyapunov functions. Furthermore, we demonstrate that the proposed method is also suitable for delay systems. Finally, simulation examples are presented to verify the effectiveness of the proposed approach. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

This paper aims at analyzing the stability of linear coupled differential-difference delay systems whose state trajectories are constrained within proper cones. First, a necessary and sufficient condition for the cone-preserving property of the system is given. Based on this condition, we further provide an exact characterization for the exponential stability of the system in terms of a cone program. Furthermore, we demonstrate that this cone program condition is equivalent to the existence of a cone-preserving solution of a matrix equation. This result is novel even for the system exhibiting positivity. In particular, when all system matrices are square and share the same dimension, the corresponding matrix equation reduces to a nonsymmetric algebraic Riccati equation. Finally, a simulation example is employed to verify the effectiveness of the results. (c) 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

This paper investigates the construction of Lyapunov-Krasovskii functionals for discrete-time delay systems whose state trajectories evolve within symmetric cones. Using the scaling point representation on the Euclidean Jordan algebra associated with symmetric cones, we prove that the asymptotic stability of the system implies the existence of a Lyapunov-Krasovskii functional whose construction involves $\mathcal {K}$-nonnegative positive definite matrices ($\mathcal {K}$ represents a symmetric cone). For the case of single-delay systems, these positive definite matrices coincide with a cone-preserving solution of an algebraic Riccati inequality associated with the delay systems. Finally, the validity of the results is verified by considering a system defined on a second-order cone in numerical simulation.

This article concerns motion planning for robots subject to dynamic constraints. Conventional solutions involve two main components: path planning and trajectory optimization. Among these, only the latter of which accounts for the impact of dynamic constraints. These methods, however, often struggle to consider the non-static initial states, leading to the trajectories of low quality. We present a method to tackle this issue by exploiting the properties of B-spline. The proposed method involves the incremental construction of a search tree and a state tree. The former is used to explore the free space and ensure that the obtained trajectories respect the dynamic constraints of the robot. Meanwhile, the latter maintains records for the actual trajectories of the robot and facilitates the extension of the former. In comparison to traditional Rapidly-exploring Random Trees (RRT), our method optimizes the extension of tree by introducing B-spline to obtain smoother trajectories. Through simulation tests, we show that our method outperforms RRT in scenarios involving non-static initial states.

This article explores semiglobal positive stabilization of discrete-time compartmental systems using saturated linear feedback. For compartmental systems with positive stabilizability and irreducible system matrix, a parameterized linear feedback is determined using linear programming, which makes the closed loop without saturation constraints nonnegative, compartmental, and Schur stable. Furthermore, this linear feedback achieves the semiglobal stabilization of the system subject to actuator saturation while maintaining the positivity. For the case when the system matrix is reducible, an iterative algorithm is proposed to determine the parameterized linear feedback. We show that this algorithm can be terminated in finite number of steps if and only if, in the digraph corresponding to the closed loop without saturation nonlinearities, each vertex is outflow connected or has direct outflow to the environment. Finally, simulation examples are given to verify the validity of the results.

Precise estimation of the load swing angle is a prerequisite for high-performance control of quadrotor-suspended-payload systems. Existing model-based filters often suffer from reduced precision due to model simplifications and external disturbances. Pure data-driven methods face generalization challenges and lack physical guarantees. This paper proposes a physics-informed hybrid estimation framework that combines an extended Kalman filter (EKF) and a residual learning long short-term memory (LSTM) network. In this framework, the EKF provides a robust nominal estimate, while the LSTM compensates for model mismatches induced by aerodynamic disturbances and dynamic coupling. Experimental validation reveals that the proposed method effectively mitigates model mismatches, significantly reducing estimation errors. Furthermore, a comparative analysis against deep learning baselines demonstrates that the proposed hybrid approach achieves comparable accuracy while demanding significantly fewer computational resources. The results establish the efficacy of the proposed framework as a lightweight and high-frequency solution for real-time onboard implementation.

This paper investigates the bipartite event-triggered output consensus problem in heterogeneous linear multi-agent systems (MASs) with a leader operating under signed jointly connected digraphs. The research addresses both cooperative and adversarial communication among agents by introducing a novel edge-based bipartite event-triggering mechanism (ETM), as well as a dynamic ETM for communication between the leader and followers. Subsequently, a distributed bipartite compensator utilizing the composite ETMs is proposed to estimate the states of the leader, and serves as a reference for the states of followers. Moreover, a significant feature of the compensator is that it reduces the frequency of communication between the leader and followers. Besides, it is proven that the system with the compensator can exclude Zeno behavior. Furthermore, observers designed to estimate the states of followers, as well as a new distributed control protocol, are proposed to address the output tracking problem of heterogeneous linear MASs. The results demonstrate that, through the proposed protocol, the output tracking error of the closed-loop control system converges to zero exponentially. Finally, the theoretical findings of this study are validated through a numerical example and an application example.

This paper investigates strategies for achieving optimal output synchronization of heterogeneous multi-agent systems in the presence of false data injection attacks. We formulate a performance index with an infinite time horizon using a zero-sum game framework, treating control input and false data injection attack input as two opposing players. Specifically, the control input's objective is to minimize the performance index, while the false data injection attack input aims to maximize it. Adhering to the optimality principle, we derive the optimal control policy, contingent upon the solution to a related algebraic Riccati equation. Moreover, we propose sufficient conditions that ensure the existence of a solution to the algebraic Riccati equation. Additionally, we have devised a data-driven reinforcement learning algorithm to seek the solution, and its convergence is assured. Furthermore, it has been demonstrated that the solution to this game corresponds to a Nash equilibrium point. Finally, the validity of the proposed methodology is substantiated through simulation results.

This paper presents a distributed adaptive horizon planning algorithm for multi-robot formation reconfiguration. First, the formation reconfiguration is addressed as a collaborative trajectory optimization problem. The trajectory of each robot is represented as polynomials, leveraging their inherent extrapolation property to consistently integrate terminal state constraints into the planning framework. Then, the robots independently compute their respective buffered Voronoi cells (BVC) by exchanging position information via sensors or communication, and collaboratively determine an adaptive planning horizon. Within each adaptive planning horizon, the control inputs of each robot are computed in a distributed manner, utilizing only the individual robot's computational unit. Building on this, to accommodate the demands of practical applications, a time scaling factor is introduced, enabling flexible adjustment of the task completion time of formation reconfiguration. Finally, numerical simulations and semi-physical experiment are conducted to demonstrate the effectiveness of the presented algorithm.

This article focuses on estimating the domain of attraction for linear cone-preserving systems subject to saturated linear feedback. The considered system model belongs to a category of control systems whose state trajectories are constrained within proper cones. In terms of cone max norms induced by proper cones, an extended max-separable Lyapunov function is constructed. Furthermore, sufficient criteria are given to show that the level set of this Lyapunov function is contractively invariant for the closed loop through the reproducing property of proper cones, and the controller to enlarge this set is synthesized via iterative algorithms. For the single-input case, we show that once the direction of the upper bound vector of the contractive invariant set is determined, an exact characterization of the maximal contractively invariant set can be given. Finally, two numerical examples are provided to verify the validity of the results.

This paper proposes a multi-robot trajectory planning framework that integrates safety-aware path planning, smooth trajectory optimization, and time coordination. A Safety-Enhanced A* (SEA) algorithm first generates discrete paths with improved safety margins. Key waypoints are then extracted to construct safe corridors, within which piecewise Bézier curves are optimized via quadratic programming (QP) to produce smooth and dynamically feasible trajectories. Inter-robot conflicts are resolved through temporal scaling and time offset adjustments, ensuring collision-free execution without altering spatial trajectories. Simulations demonstrate the effectiveness of the proposed method in complex environments.

This paper focuses on the secure control of positive networked control systems under multi-channel attacks characterized by a Bernoulli distribution. Specifically, the sensor-to-controller channel suffers from false data injection (FDI) attacks, whereas the controller-to-actuator channel suffers from denial of service (DoS) attacks. The objective is to design a static output feedback controller that guarantees the positivity and stochastic stability of the closed-loop system in the presence of DoS and FDI attacks. To conserve communication resources, we employ an event-triggered scheme to reduce the frequency of information transmission. Due to the positivity of the system, the proposed event-triggering mechanism employs the 1-norm form instead of the 2-norm form, which allows that the controller parameter is determined via linear programming rather than LMI technique. Finally, two simulation examples are provided to verify the effectiveness of our methods.

In this study, we employ two data-driven approaches to address the secure control problem for cyber-physical systems when facing false data injection attacks. Firstly, guided by zero-sum game theory and the principle of optimality, we derive the optimal control gain, which hinges on the solution of a corresponding algebraic Riccati equation. Secondly, we present sufficient conditions to guarantee the existence of a solution to the algebraic Riccati equation, which constitutes the first major contributions of this paper. Subsequently, we introduce two data-driven Q-learning algorithms, facilitating model-free control design. The second algorithm represents the second major contribution of this paper, as it not only operates without the need for a system model but also eliminates the requirement for state vectors, making it quite practical. Lastly, the efficacy of the proposed control schemes is confirmed through a case study involving an F-16 aircraft.

In the paper, an inverse source problem in bioluminescence tomography (BLT) is investigated. BLT is a method of light imaging and offers many advantages such as sensitivity, cost-effectiveness, high signal-to-noise ratio and non-destructivity. It thus has promising prospects for many applications such as cancer diagnosis, drug discovery and development as well as gene therapies. In the literature, BLT is extensively studied based on the (stationary) diffusion approximation (DA) equation, where the distribution of peak sources is reconstructed and no solution uniqueness is guaranteed without proper a priori information. In this work, motivated by solution uniqueness, a novel dynamic coupled DA model is proposed. Theoretical analysis including the well-posedness of the forward problem and the solution uniqueness of the inverse problem are given. Based on the new model, iterative inversion algorithms under the framework of regularizing schemes are introduced and applied to reconstruct the smooth and non-smooth sources. We discretize the regularization functional with the finite element method and give the convergence rate of numerical solutions. Several numerical examples are implemented to validate the effectiveness of the new model and the proposed algorithms.

Abstract This paper introduces an adaptive control scheme for quadrotor suspended load systems, to track desired trajectory with variable payload and wind disturbances. The dynamic model of the quadrotor suspended load system is developed, taking into account the impact of the wind field described by the Dryden model. To attenuate the effects of payload variation and wind disturbances on the system, an adaptive control method based on disturbance observers is devised. Additionally, the uniform boundedness of all error signals is demonstrated. The effectiveness of the designed control method is verified through simulations, which serves to strengthen its applicability in practical applications.

Cyber–physical systems (CPSs), which combine computer science, control systems, and physical elements, have become essential in modern industrial and societal contexts. However, their extensive integration presents increasing security challenges, particularly due to recurring cyber attacks. Therefore, it is crucial to explore CPS security control. In this review, we systematically examine the prevalent cyber attacks affecting CPSs, such as denial of service, false data injection, and replay attacks, explaining their impacts on CPSs’ operation and integrity, as well as summarizing classic attack detection methods. Regarding CPSs’ security control approaches, we comprehensively outline protective strategies and technologies, including event-triggered control, switching control, predictive control, and optimal control. These approaches aim to effectively counter various cyber threats and strengthen CPSs’ security and resilience. Lastly, we anticipate future advancements in CPS security control, envisioning strategies to address emerging cyber risks and innovations in intelligent security control techniques.

This paper investigates the bipartite consensus problem for heterogeneous multi-agent systems subjected to Byzantine attacks. Byzantine agents send erroneous signals to their neighbors while utilizing incorrect input signals themselves, posing significant challenges for defense. To defend against Byzantine attacks, we propose a resilient heterogeneous impulsive bipartite consensus algorithm for multi-agent systems. This approach ensures that information transmission occurs exclusively at sampling points, significantly reducing control costs, minimizing communication redundancy, and enhancing system robustness. During each sampling event, agents eliminate the most extreme values from their neighbors and utilize the remaining information to generate the control input. By employing this resilient scheme and leveraging the properties of Sarymsakov matrices, we demonstrate that the proposed impulsive control method effectively limits the impact of Byzantine attacks. We also determine the maximum allowable number of Byzantine agents and the corresponding network robustness required to ensure the agents achieve bipartite consensus. Finally, simulations and experiments validate the effectiveness of the proposed approach.

For dual-rotor turbofan aero-engine rotation speed systems (DTARSSs), we investigate their fault detection filter design (FDFD) problem in this brief. First, according to the internally positive representation (IPR) of the DTARSS, an exact characterization of the L-1/L_ index is obtained by means of linear programming so that the FDFD problem is therefore transformed into a convex optimization problem due to positivity constraints. For convenience of application, we propose a method for determining the fault detection threshold and go over the functional relationship between L-1 gain and L_ index. A numerical simulation based on the DTARSS in typical operating situations illustrates the effectiveness of the proposed approach.

This paper studies the leader-following consensuses of uncertain and nonlinear multi-agent systems against composite attacks (CAs), including denial of service (DoS) attacks and actuation attacks (AAs). A double-layer control framework is formulated, where a digital twin layer (TL) is added beside the traditional cyber-physical layer (CPL), inspired by the recent Digital Twin technology. Consequently, the resilient control task against CAs can be divided into two parts: One is distributed estimation against DoS attacks on the TL, and the other is resilient decentralized tracking control against actuation attacks on the CPL. First, a distributed observer based on switching estimation law against DoS is designed on TL. Second, a distributed model- free adaptive control (DMFAC) protocol based on attack compensation against AAs is designed on CPL. Moreover, the uniformly ultimately bounded convergence of consensus error of the proposed double-layer DMFAC algorithm is strictly proved. Finally, the simulation verifies the effectiveness of the resilient double-layer control scheme.

The rapid development of both hardware and software has promoted the popularization of various real-time applications like health monitoring and intrusion detection that are widely deployed in outsourcing scenarios, e.g., mobile edge computing and cloud computing. In these applications, end devices continuously generate unbounded sequences of data items at a fast rate, i.e., the so-called streaming data. Nevertheless, storing and processing massive amounts of streaming data poses a challenge for resources-restricted end devices. Although outsourcing data items to edge servers or cloud servers is an attractive solution to the above problem, it also brings a new challenge, i.e., how to guarantee the integrity of outsourced data, since streaming data applications are usually sensitive of both location and the corresponding context, and servers are not completely trusted. To this end, the primitive of verifiable data streaming (VDS) protocol was introduced to maintain outsourced streaming data, while preserving its integrity. However, existing VDS constructions mainly use the structure of Merkle hash tree, and inherently have logarithmic costs. Consequently, they are infeasible for real-time applications that are delay sensitive and generate unpredictable size of streaming data. In this paper, we optimize previous VDS protocols from the aspects of communication overhead and computation cost. Specifically, we adopt a technical route different from Merkle hash tree, i.e, combining the digital signature with the cryptographic accumulator. In our construction, we employ Boneh-Lynn-Shacham (BLS) signature to guarantee the integrity of the context and position of each outsourced data item, and adopt an RSA accumulator to invalidate the old signature after the corresponding data item was updated. This immediately yields an optimal VDS construction that has constant costs even under concurrent queries, which is more desirable for those resource-limited mobile devices. In addition, the aggregability of BLS signature makes our VDS construction capable of data auditing, which enables the user to remotely verify the integrity of outsourced streaming data. We provide a formal security proof of the proposed VDS construction under well-studied complexity assumptions in the random oracle model. As a proof-of-concept, we also implement our proposal, and conduct extensive experiments to demonstrate its practicability.