This paper focuses on the Nash Equilibrium (NE) seeking problem in cyber-physical systems (CPSs), where malicious nodes can arbitrarily iterate their actions and estimates within constrained bounds and broadcast such information to their neighboring nodes in the IoT network. Consequently, the NE seeking process of legitimate agents is highly susceptible to malicious interferences, as these agents update their actions and estimates based on information received from neighboring nodes and their own local cost functions. To address this critical issue in IoT scenarios, we build on existing research and propose a resilient NE-seeking algorithm based on physical layer trusted observations tailored for multi-agent systems. Specifically, we put forward a resilience-oriented definition of nominal NE that is adapted to IoT environments, and rigorously prove the convergence of both actions and estimation values of legitimate agents. Finally, numerical simulations are conducted to validate the correctness and effectiveness of our theoretical analysis. Furthermore, we carry out a real-world experiment on a quadrotor UAV swarm system, a typical IoT multi-agent application scenario, to translate our theoretical results into practical IoT applications.

This work considers the prescribed-time control problem of uncertain nonlinear systems with unknown control coefficients via an event-triggering strategy. In contrast to existing event-based prescribed-time works where the control coefficients are supposed to be known, in this work, not only the magnitude but also the sign of the control coefficient is allowed to be unknown. Based on a newly established Nussbaum-type lemma, the underlying problem is addressed by proposing an adaptive prescribed-time control scheme, which is characterized by the joint design of the triggering condition and actual controller. Moreover, the developed control scheme enables the execution time to be extended to infinity, distinguishing itself from those where the valid execution time can only be finite (ended at the prescribed time). It is proved that the closed-loop signals are globally bounded and system states converge to the origin within a prespecified time. Besides, the Zeno-free behavior is ensured. A robotic manipulator is exploited to demonstrate the validity of the theoretical findings.

Neural oil painting synthesis is to sequentially predict brushstroke color and position, forming an oil painting step by step, which could serve as a painting teacher for education and entertainment. Existing methods usually suffer from degraded generalization for real-world photo inputs due to the training-test distribution gap, often manifesting as stroke-induced artifacts (e.g., over-smoothed textures or inconsistent granularity). In an attempt to mitigate this gap, we introduce a domain-agnostic neural painting (DANP) framework that aligns model to the test domain. In particular, we focus on updating affine parameters of normalization layers efficiently, while keeping other parameters frozen. To stabilize adaptation, our framework introduces: (1) Asymmetric Dual-Branch with mirror augmentation for robust feature alignment via geometric transformations, (2) Dual-Branch Interaction Loss combining intra-branch reconstruction and inter-branch consistency, and we also involve an empirical optimization strategy to mitigate gradient oscillations in practice. Experiments on real-world images from diverse domains (e.g., faces, landscapes, and artworks) validate the effectiveness of DANP in resolution-invariant adaptation, decreasing ~11.3% reconstruction error at 512px and ~20.3% at 1024px compared to the baseline model. It is worth noting that our method is compatible with existing methods, e.g., Paint Transformer, and further improve the ~10.3% perceptual quality. Dataset and code will be publicly released at: https://domain-agnostic-neural-oil-painting.github.io/DANP.

This paper investigates the fixed-time sliding mode control (FTSMC) problem for a quadcopter unmanned aerial vehicle (QUAV), which is driven by a lip-reading recognition module. The lip-reading recognition module is consisted of a trained deep neural network with the structure of 2D-Conv+GhostNet+TCN. In order to reduce the communication burden between the remote controller and the QUAV as well as reduce the computation burden in running the lip-reading recognition module, the event-triggered mechanism is introduced to the position controller design. The low-bound of the triggering interval is derived explicitly so that the Zeno phenomenon can be excluded. Furthermore, in order to overcome the main obstacle in high-accuracy control of QUAV, this paper launches a novel wind disturbance rejection approach by using wind field model, which is motivated by the physical dynamic characteristics of the practical wind. Specifically, the wind disturbance is estimated in the designed FTSMC by applying a specific wind field equation with preassigned physical parameters. To further reduce the chattering in the controller, a fitting technique is introduced via a local multivariate linear regression. Finally, both simulation and human-in-the-loop experiment results verify the applicability of the proposed control approach for the lip-reading-driven QUAV system. Note to Practitioners—This research is motivated by the need to design lip-reading-driven QUAV. In noisy environments or when silence is required, the efficiency of traditional human-computer interaction methods such as speech recognition is greatly reduced. Especially for people with damaged vocal cords, speech recognition is not achievable. In addition, it is difficulty to realize high-precision anti-interference control of QUAV with lower computational and communication burdens. In order to solve these problems, this research designs a lip-reading recognition module for QUAV control to cope with various complex application scenarios and realizes high-performance control by FTSMC algorithm. The key of this work to save system resources is to introduce the event-triggered mechanism into the position controller of the QUAV. In addition, this paper introduces the wind field model into the QUAV model to realize the wind disturbance suppression. The lip-reading-driven QUAV proposed in this paper have a wide range of applications, such as controlling QUAV in hazardous environments and improving the efficiency of interaction between human and QUAV.

In this paper, an adaptive neural network control algorithm based on event-triggered reinforcement learning is proposed for a four-wheel independent steering and four-wheel independent driving (4WS4WD) mobile robot. A kinematic model is established based on the kinematic relationship between the robot wheels and the body under the consideration of the effect of slip-turn perturbation. The dynamics model is established using the Lagrangian dynamics equations. An improved performance metric function is designed and approximated using the Critic neural network and the Actor neural network to approximate the unknown long-term performance metric function and controller respectively. A threshold band event triggering is proposed for reducing the consumption of communication and computational resources. It is rigorously demonstrated using Lyapunov analysis that both the neural network error and the system error are up to the final consistent bound. As well as proved that the proposed event-triggered mechanism can eliminate the Zeno phenomenon. Finally, comparative experiments demonstrated the effectiveness of the proposed algorithm.

This paper introduces an event-triggered saturation-tolerant prescribed control (STPC) framework for rigid spacecraft subject to actuator faults and actuator saturation via a fixed-time disturbance observer (FTDO). An FTDO with time-varying observer gains is initially developed to reconstruct the lumped perturbations caused by external disturbances, parameter uncertainties, and actuator faults. A fixed-time auxiliary system is employed to counter the adverse effects of actuator saturation. Additionally, asymmetric prescribed performance and shift functions are skillfully incorporated to handle arbitrary bounded initial conditions. Subsequently, a novel FTDO-based event-triggered STPC strategy is formulated, ensuring that attitude-tracking errors converge to predefined performance bounds within a predetermined time while minimizing unnecessary control signal updates. The practical fixed-time stability of all closed-loop signals is validated, with the strict avoidance of Zeno behavior. Finally, simulation studies are conducted to verify the accuracy and effectiveness of the proposed approach.

This article introduces an innovative approach to tackle the consensus issue for positive multiagent systems (MASs) with input nonlinearities. By analyzing a differential equation model of MASs with sector-bounded nonlinearity, positivity conditions are derived. Furthermore, a distributed state-feedback control protocol is applied to achieve consensus and simultaneously to preserve positivity. An iterative algorithm is used to compute a consensus controller gain. The saturation nonlinearity and its approximated form are also considered. Finally, the effectiveness of proposed method is verified through numerical examples. The findings of this study can potentially contribute to developing control strategies for MASs with nonlinear input dynamics.

state-feedback control problem for networked discrete-time systems with positivity constraint. This problem (for a single positive system), raised recently in the area of positive systems theory, is known to be challenging due to its inherent nonconvexity. In contrast to most works, which only provide sufficient synthesis conditions for a single positive system, we study this problem within a primal-dual scheme, in which necessary and sufficient synthesis conditions are proposed for networked positive systems. Based on the equivalent conditions, we develop a primal-dual iterative algorithm for solution, which helps prevent from converging to a local minimum. In the simulation, two illustrative examples are employed for verification of our proposed results.

This work addresses the problem of reachable set-based consensus for positive multiagent systems affected by typical classes of bounded disturbances. In the presence of disturbances, a reachable set-based consensus with positivity preservation is proposed to ensure that the state of the closed-loop system remains positive while enclosing the reachable set of the defined consensus error with an ellipsoidal bounding region. Sufficient conditions are established to achieve the reachable set-based positive consensus under the energy-bounded or peak-bounded disturbance input. Equivalent design conditions are provided, for which a heuristic algorithm is proposed for computing and optimizing the bounding region. Simulations are conducted to validate the obtained results.

This article investigates the positive consensus problem of a special kind of interconnected positive systems over directed graphs. They are composed of multiple fractional-order continuous-time positive linear systems. Unlike most existing works in the literature, we study this problem for the first time, in which the communication topology of agents is described by a directed graph containing a spanning tree. This is a more general and new scenario due to the interplay between the eigenvalues of the Laplacian matrix and the controller gains, which renders the positivity analysis fairly challenging. Based on the existing results in spectral graph theory, fractional-order systems (FOSs) theory, and positive systems theory, we derive several necessary and/or sufficient conditions on the positive consensus of fractional-order multiagent systems (PCFMAS). It is shown that the protocol, which is designed for a specific graph, can solve the positive consensus problem of agents over an additional set of directed graphs. Finally, a comprehensive comparison study of different approaches is carried out, which shows that the proposed approaches have advantages over the existing ones.

In this article, the non-negative edge consensus problem is addressed for positive networked systems with undirected graphs using state-feedback protocols. In contrast to existing results, the major contributions of this work included: 1) significantly improved criteria of consequentiality and non-negativity, therefore leading to a linear programming approach and 2) necessary and sufficient criteria giving rise to a semidefinite programming approach. Specifically, an improved upper bound is given for the maximum eigenvalue of the Laplacian matrix and the (out-) in-degree of the degree matrix, and an improved consensuability and non-negativevity condition is obtained. The sufficient condition presented only requires the number of edges of a nodal network without the connection topology. Also, with the introduction of slack matrix variables, two equivalent conditions of consensuability and non-negativevity are obtained. In the conditions, the system matrices, controller gain, as well as Lyapunov matrices are separated, which is helpful for parameterization. Based on the results, a semidefinite programming algorithm for the controller is readily developed. Finally, a comprehensive analytical and numerical comparison of three illustrative examples is conducted to show that the proposed results are less conservative than the existing work.

This article addresses the distributed consensus problem for identical continuous-time positive linear systems with state-feedback control. Existing works of such a problem mainly focus on the case where the networked communication topologies are of either undirected and incomplete graphs or strongly connected directed graphs. On the other hand, in this work, the communication topologies of the networked system are described by directed graphs each containing a spanning tree, which is a more general and new scenario due to the interplay between the eigenvalues of the Laplacian matrix and the controller gains. Specifically, the problem involves complex eigenvalues, the Hurwitzness of complex matrices, and positivity constraints, which make analysis difficult in the Laplacian matrix. First, a necessary and sufficient condition for the consensus analysis of directed networked systems with positivity constraints is given, by using positive systems theory and graph theory. Unlike the general Riccati design methods that involve solving an algebraic Riccati equation (ARE), a condition represented by an algebraic Riccati inequality (ARI) is obtained for the existence of a solution. Subsequently, an equivalent condition, which corresponds to the consensus design condition, is derived, and a semidefinite programming algorithm is developed. It is shown that, when a protocol is solved by the algorithm for the networked system on a specific communication graph, there exists a set of graphs such that the positive consensus problem can be solved as well.

This article investigates the nonnegative consensus tracking problem for networked systems with a distributed static output-feedback (SOF) control protocol. The distributed SOF controller design for networked systems presents a more challenging issue compared with the distributed state-feedback controller design. The agents are described by multi-input multi-output (MIMO) positive dynamic systems which may contain uncertain parameters, and the interconnection among the followers is modeled using an undirected connected communication graph. By employing positive systems theory, a series of necessary and sufficient conditions governing the consensus of the nominal, as well as uncertain, networked positive systems, is developed. Semidefinite programming consensus design approaches are proposed for the convergence rate optimization of MIMO agents. In addition, by exploiting the positivity characteristic of the systems, a linear-programming-based design approach is also proposed for the convergence rate optimization of single-input multi-output (SIMO) agents. The proposed approaches and the corresponding theoretical results are validated by case studies.

This article considers the problem of designing lead, lag, and lead-lag compensators for multi-input-multi-output (MIMO) linear systems under the H∞performance measure. This is the first time that the lead, lag and lead-lag compensators are generalized to the MIMO cases by preserving their classical compensator structures. Theoretical results on the stability analysis and synthesis of MIMO systems under the H∞control performance with the proposed lead, lag, and lead-lag compensators are obtained. Then relevant algorithms for designing lead, lag, and lead-lag compensators are provided to determine the compensator parameters. Differing from traditional design methods, which mostly rely on some trial-and-error procedures, the proposed methods are algorithmic and the compensators can be synthesized systematically. Illustrative examples are used to demonstrate the effectiveness and advantages of the proposed methods.