Reinforcement Learning (RL) has demonstrated excellent decision-making potential in platoon coordination problems. However, due to the variability of coordination goals, the complexity of the decision problem, and the time-consumption of trial-and-error in manual design, finding a well performance reward function to guide RL training to solve complex platoon coordination problems remains challenging. In this paper, we formally define the Platoon Coordination Reward Design Problem (PCRDP), extending the RL-based cooperative platoon coordination problem to incorporate automated reward function generation. To address PCRDP, we propose a Large Language Model (LLM)-based Platoon coordination Reward Design (PCRD) framework, which systematically automates reward function discovery through LLM-driven initialization and iterative optimization. In this method, LLM first initializes reward functions based on environment code and task requirements with an Analysis and Initial Reward (AIR) module, and then iteratively optimizes them based on training feedback with an evolutionary module. The AIR module guides LLM to deepen their understanding of code and tasks through a chain of thought, effectively mitigating hallucination risks in code generation. The evolutionary module fine-tunes and reconstructs the reward function, achieving a balance between exploration diversity and convergence stability for training. To validate our approach, we establish six challenging coordination scenarios with varying complexity levels within the Yangtze River Delta transportation network simulation. Comparative experimental results demonstrate that RL agents utilizing PCRD-generated reward functions consistently outperform human-engineered reward functions, achieving an average of 10% higher performance metrics in all scenarios.

Designing effective incentive mechanisms in mobile crowdsensing (MCS) networks is crucial for engaging distributed mobile users (workers) to contribute heterogeneous data for various applications (tasks). In this paper, we propose a novel stagewise trading framework to achieve efficient and stable task-worker matching, explicitly accounting for task diversity (e.g., spatio-temporal limitations) and network dynamics inherent in MCS environments. This framework integrates both futures and spot trading stages. In the former, we introduce the futures trading-driven stable matching and pre-path-planning mechanism (FT-SMP3), which enables long-term taskworker assignment and pre-planning of workers' trajectories based on historical statistics and risk-aware analysis. In the latter, we develop the spot trading-driven DQN-based path planning and onsite worker recruitment mechanism (ST-DP2WR), which dynamically improves the practical utilities of tasks and workers by supporting real-time recruitment and path adjustment. We rigorously prove that the proposed mechanisms satisfy key economic and algorithmic properties, including stability, individual rationality, competitive equilibrium, and weak Pareto optimality. Extensive experiements further validate the effectiveness of our framework in realistic network settings, demonstrating superior performance in terms of service quality, computational efficiency, and decision-making overhead.

As an essential aerial platform in Internet of Things (IoT) applications, UAV swarms require high-precision attitude estimation in GPS-limited and dynamic environments, which supports higher-level IoT functions such as smart logistics, disaster response, and environmental monitoring. However, most odometry-based pose estimation methods in dynamic scenarios without GPS encounter issues with cumulative errors over time. In this paper, we propose a method to reduce these cumulative errors by constraining the absolute positioning of Visual-Inertial Odometry (VIO) using relative poses obtained from multi-view landmark and Resonant-Beam (RBeam) sensors between UAVs. For synchronous moments, we estimate the 6 Degree-of-Freedom (DoF) relative pose using the Angle of Arrival and Time of Flight data from the RBeam, combined with nonlinear optimization. For asynchronous moments, a two-stage visual estimation method is introduced, combining multi-camera epipolar geometry for rotation recovery and depth reconstruction optimization for translation recovery, enabling the estimation of asynchronous relative poses. Finally, we design a global objective function based on a sliding window and factor graph, integrating RBeam, multi-view landmark, and VIO for absolute 6-DoF pose optimization of the UAV swarm. Simulation results demonstrate that optimizing with the addition of RBeam synchronous relative poses improves overall positioning accuracy by 32.94% compared to pure VIO. Incorporating both RBeam synchronous and visual asynchronous relative poses further enhances overall positioning accuracy by 37.65%. Additionally, the UAV’s attitude benefits from the rotational constraints provided by the RBeam, achieving over 30% improvement in the pitch and yaw directions.

Cooperation is fundamental to human societies, and the interaction structure among individuals profoundly shapes its emergence and evolution. In real-world scenarios, cooperation prevails in multi-group (higher-order) populations, beyond just dyadic behaviors. Despite recent studies on group dilemmas in higher-order networks, the exploration of cooperation driven by higher-order strategy updates remains limited due to the intricacy and indivisibility of group-wise interactions. Here we investigate four categories of higher-order mechanisms for strategy updates in public goods games and establish their mathematical conditions for the emergence of cooperation. Such conditions uncover the impact of both higher-order strategy updates and network properties on evolutionary outcomes, notably highlighting the enhancement of cooperation by overlaps between groups. Interestingly, we discover that the group-mutual comparison update - selecting a high-fitness group and then imitating a random individual within this group - can prominently promote cooperation. Our analyses further unveil that, compared to pairwise interactions, higher-order strategy updates generally improve cooperation in most higher-order networks. These findings underscore the pivotal role of higher-order strategy updates in fostering collective cooperation in complex social systems.

This article considers the security control problem of a safety-critical system, described by a general nonlinear uncertain system with constraints for collision avoidance and internal dynamic limitations. We design an integrated security and safety-critical control law to prevent the system from operating in the unsafe mode under denial-of-service (DoS) attacks in the signal transmission channels. By combining the internal model principle and the time- and event-triggered sampling mechanism for DoS detection, an improved dynamic compensator is first proposed and converts the safety tracking problem into the attractivity problem of the constrained error system. Then a security control is constructed for the error system by integrating the safety-critical controller in the barrier function-based framework. Finally, we prove that the integrated control design can guarantee the security, safety, and stability of the closed-loop system.

Hierarchical Federated Learning (HFL) extends conventional Federated Learning (FL) by introducing intermedi ate aggregation layers, enabling distributed learning in geograph ically dispersed environments, particularly relevant for smart IoT systems, such as remote monitoring and battlefield operations, where cellular connectivity is limited. In these scenarios, UAVs serve as mobile aggregators, dynamically connecting terrestrial IoT devices. This paper investigates an HFL architecture with energy-constrained, dynamically deployed UAVs prone to communication disruptions. We propose a novel approach to minimize global training costs by formulating a joint optimization problem that integrates learning configuration, bandwidth allocation, and device-to-UAV association, ensuring timely global aggregation before UAV disconnections and redeployments. The problem accounts for dynamic IoT devices and intermittent UAV con nectivity and is NP-hard. To tackle this, we decompose it into three subproblems: (i) optimizing learning configuration and bandwidth allocation via an augmented Lagrangian to reduce training costs; (ii) introducing a device fitness score based on data heterogeneity (via Kullback-Leibler divergence), device-to UAV proximity, and computational resources, using a TD3-based algorithm for adaptive device-to-UAV assignment; (iii) developing a low-complexity two-stage greedy strategy for UAV redeployment and global aggregator selection, ensuring efficient aggregation despite UAV disconnections. Experiments on diverse real-world datasets validate the approach, demonstrating cost reduction and robust performance under communication disruptions.

This study explores linear encryption techniques to protect against information-based stealthy attacks on re-mote state estimation. Utilizing smart sensors equipped with local Kalman filters, the system transmits innovations rather than raw measurements via wireless networks. However, this transmission is susceptible to malicious data interception and manipulation by attackers. To safeguard against these stealthy threats, encryption and decryption modules are integrated into the system. This research aims to assess the effectiveness of the encryption strategy when faced with information-based stealthy attacks. A key contribution of this paper is the adoption of the most comprehensive attack models, moving away from the conventional reliance on innovation-based linear attack models. Our results demonstrate that the proposed linear encryption approach effectively mitigates stealthy attacks under certain mild conditions. The efficacy of the encryption is further validated through numerical examples, corroborating the theoretical advancements presented in this paper.

Motion planning at intersections is a challenging problem in autonomous driving due to the complicated interactions. The existing pipeline of "planning after predicting" is too conservative, can reduce traffic efficiency. Using game theory to model the non-cooperative coupling relationships between multiple vehicles can resolve the above problems, but such methods cannot guarantee safety without collision. This paper presents motion planning for autonomous driving with safe differential games based on Control Barrier Function (CBF), and also provides a safety-critical generalized Nash equilibrium seeking algorithm. We handle the hard CBF constraints through augmented Lagrangian multiplier method. Motivated by iterative Linear-Quadratic Game (iLQG) algorithm, we use the Taylor expansion method to approximate the model into an Linear-Quadratic (LQ) structure, and then incrementally solve this problem with an iterative feedback LQ game algorithm. Through Carla simulation and hardware testing, our results indicate that the algorithm can find a balance between safety and efficiency while maintaining real-time implementation performance.

Many machine learning problems can be formulated as non-convex multi-player games. Due to non-convexity, it is challenging to obtain the existence condition of the global Nash equilibrium (NE) and design theoretically guaranteed algorithms. This paper studies a class of non-convex multi-player games, where players' payoff functions consist of canonical functions and quadratic operators. We leverage conjugate properties to transform the complementary problem into a variational inequality (VI) problem using a continuous pseudo-gradient mapping. We prove the existence condition of the global NE as the solution to the VI problem satisfies a duality relation. We then design an ordinary differential equation to approach the global NE with an exponential convergence rate. For practical implementation, we derive a discretized algorithm and apply it to two scenarios: multi-player games with generalized monotonicity and multi-player potential games. In the two settings, step sizes are required to be O(1/k) and O(1/√k) to yield the convergence rates of O(1/ k) and O(1/√k), respectively. Extensive experiments on robust neural network training and sensor network localization validate our theory. Our code is available at https://github.com/GuanpuChen/Global-NE.

We consider solving distributed constrained optimization in this article. To avoid projection operations due to constraints in the scenario with large-scale variable dimensions, we propose distributed projection-free dynamics by employing the Frank-Wolfe method, also known as the conditional gradient. Technically, we find a feasible descent direction by solving an alternative linear suboptimization. To make the approach available over multiagent networks with weight-balanced digraphs, we design dynamics to simultaneously achieve both the consensus of local decision variables and the global gradient tracking of auxiliary variables. Then, we present the rigorous convergence analysis of the continuous-time dynamical systems. Also, we derive its discrete-time scheme with an accordingly proved convergence rate of O(1/k) . Furthermore, to clarify the advantage of our proposed distributed projection-free dynamics, we make detailed discussions and comparisons with both existing distributed projection-based dynamics and other distributed Frank-Wolfe algorithms.

There has been significant recent interest in a class of three-player leader-follower game models in many important cybersecurity scenarios. In such a tri-level hierarchical structure, a defender usually serves as a leader, dominating the decision process by the Stackelberg equilibrium (SE) strategy. However, such a leader-follower scheme may not always work, and the Nash equilibrium (NE) strategy may provide an alternative choice. Thus, we need to reveal the consistency between SE and NE in the three-player model to help the leader evaluate its strategy impact and avoid a choice dilemma. To this end, we first provide a necessary and sufficient condition such that each SE is an NE, which not only provides access to seek a satisfactory SE but also makes a criterion for an obtained SE. Then, we apply the results for case studies with a unique SE or with at least one SE being an NE. Moreover, when the consistency condition falls short, we give an upper bound of the deviation between SE and NE to help the leader tolerably adopt an SE strategy. Finally, we apply our consistency analysis to practical scenarios, including secure wireless transmission and advanced persistent threat defense.

Communication has been seen as a significant bottleneck in industrial applications over large-scale networks. To alleviate the communication burden, sign-based optimization algorithms have gained popularity recently in both industrial and academic communities, which is shown to be closely related to adaptive gradient methods, such as Adam. Along this line, this article investigates faster convergence for a variant of sign-based gradient descent, called scaledsignGD, in three cases: First, the objective function is strongly convex; second, the objective function is nonconvex but satisfies the Polyak–Łojasiewicz inequality; third the gradient is stochastic, called scaledsignSGD in this case. For the first two cases, it can be shown that the scaledsignGD converges at a linear rate. For case third, the algorithm is shown to converge linearly to a neighborhood of the optimal value when a constant learning rate is employed, and the algorithm converges at a rate of$O(1/k+1/k^{2}+1/k^{3})$when using a diminishing learning rate, where$k$is the iteration number. The results are also extended to the distributed setting by majority vote in a parameter-server framework. Finally, numerical experiments are performed to corroborate the theoretical findings.

Within the realm of distributed optimization, each node in the network possesses computational capabilities. Nodes perform local calculations on their own data, and by communicating local information (e.g., local gradients) with neighboring nodes, agents collectively achieve a globally optimal solution. In recent years, distributed optimization has garnered interest across diverse disciplines, particularly in situations where communication abilities are limited or data are private. In this paper, a distributed second-order algorithm, based on an augmented Lagrangian function, is proposed with an enhanced diffusion communication strategy. An R-linear convergence rate is established under a relaxed locally restricted strong convexity assumption, along with a widely employed L-smoothness assumption. Finally, the superiority of the algorithm is showcased by a distributed logistic regression example utilizing a synthetic dataset with various parameter settings.

Online optimization has witnessed a massive surge of research attention in recent years. In this paper, we propose online gradient descent and online bandit algorithms over Riemannian manifolds in full information and bandit feedback settings respectively, for both geodesically convex and strongly geodesically convex functions. We establish a series of upper bounds on the regrets for the proposed algorithms over Hadamard manifolds. We also find a universal lower bound for achievable regret on Hadamard manifolds. Our analysis shows how time horizon, dimension, and sectional curvature bounds have impact on the regret bounds. When the manifold permits positive sectional curvature, we prove similar regret bound can be established by handling non-constrictive project maps. In addition, numerical studies on problems defined on symmetric positive definite matrix manifold, hyperbolic spaces, and Grassmann manifolds are provided to validate our theoretical findings, using synthetic and real-world data.

In this work, we design a distributed algorithm for time-varying convex optimization over networks with quantized communications. Each agent has its local time-varying objective function, while the agents need to cooperatively track the optimal solution trajectories of global time-varying functions. The distributed algorithm is motivated by the alternating direction method of multipliers, but the agents can only share quantization information through an undirected graph. To reduce the tracking error due to information loss in quantization, we apply the dynamic quantization scheme with a decaying scaling function. The tracking error is explicitly characterized with respect to the limit of the decaying scaling function in quantization. Furthermore, we are able to show that the algorithm could asymptotically track the optimal solution when time-varying functions converge, even with quantization information loss. Finally, the theoretical results are validated via numerical simulation.

In this article, we aim to design a distributed approximate algorithm for seeking Nash equilibria (NE) of an aggregative game. Due to the local set constraints of each player, projection-based algorithms have been widely employed for solving such problems actually. Since it may be quite hard to get the exact projection in practice, we utilize inscribed polyhedrons to approximate local set constraints, which yields a related approximate game model. We first prove that the NE of the approximate game is the ϵ -NE of the original game and then propose a distributed algorithm to seek the ϵ -NE, where the projection is then of a standard form in quadratic optimization with linear constraints. With the help of the existing developed methods for solving quadratic optimization, we show the convergence of the proposed algorithm and also discuss the computational cost issue related to the approximation. Furthermore, based on the exponential convergence of the algorithm, we estimate the approximation accuracy related to ϵ . In addition, we investigate the computational cost saved by approximation in numerical simulation.

We consider a distributed online convex optimization problem when streaming data are distributed among computing agents over a connected communication network. Since the data are high-dimensional or the network is large-scale, communication load can be a bottleneck for the efficiency of distributed algorithms. To tackle this bottleneck, we apply the state-of-art data compression scheme to the fundamental GD-based distributed online algorithms. Three algorithms with difference-compressed communication are proposed for full information feedback (DC-DOGD), one-point bandit feedback (DC-DOBD), and two-point bandit feedback (DC-DO2BD), respectively. We obtain regret bounds explicitly in terms of time horizon, compression ratio, decision dimension, agent number, and network parameters. Our algorithms are proved to be no-regret and match the same regret bounds, w.r.t. time horizon, with their uncompressed versions for both convex and strongly convex losses. Numerical experiments are given to validate the theoretical findings and illustrate that the proposed algorithms can effectively reduce the total transmitted bits for distributed online training compared with the uncompressed baseline.

This article proposes a distributed optimization design to compute continuous-time algebraic Riccati inequalities (ARIs), where the information of matrices is distributed among agents. We propose a design procedure to tackle the nonlinearity, the inequality, and the coupled information structure of ARI; then, we design a distributed algorithm based on an optimization approach and analyze its convergence properties. The proposed algorithm is able to verify whether ARI is feasible in a distributed way and converges to a solution if ARI is feasible for any initial condition.

In this paper, we employ a hypergame framework to analyze the single-leader-multiple-followers (SLMF) Stackelberg security game with two typical misinformed situations: misperception and deception. We provide a stability criterion with the help of hyper Nash equilibrium (HNE) to investigate both strategic stability and cognitive stability of equilibria in SLMF games with misinformation. In fact, we find mild stable conditions such that the equilibria with misperception and deception can become HNE. Moreover, we discuss the robustness of the equilibria to reveal whether players have the ability to keep their profits under the influence of some misinformation.

Due to the growing awareness of driving safety and the development of sophisticated technologies, advanced driving/driver assistance system (ADAS) has been equipped in more and more vehicles with higher accuracy and lower price. The latest progress in this field has called for a review to sum up the conventional knowledge of ADAS, the state-of-the-art researches, novel applications and standards in real world. With the help of this kind of review, newcomers in this field can get basic knowledge easier and other researchers may be inspired with potential future development possibility. This paper makes a general introduction about ADAS by analyzing its hardware support, computation algorithms and current development state. Different types of perception sensors are introduced from their interior feature classifications, installation positions, supporting ADAS functions, and pros and cons. The comparisons between different sensors are concluded and illustrated from their inherent characters and specific usages serving for each ADAS function. The current algorithms for ADAS functions are also collected and briefly presented in this paper from both traditional methods and novel ideas. Additionally, discussions about current regulations and market state of ADAS in China are reviewed in this paper, and other open issues related to ADAS are also introduced in particular.

This article establishes an approach, based on distributed optimization, for solving continuous-time Lyapunov equations (CTLE) over multiagent networks. Each agent in the network knows partial information of the CTLE and has a dynamical system to estimate exact or least-squares solutions. The aim of agents is to find a solution to CTLE by sharing information with connected agents over a network. This article develops distributed algorithms with an exponential rate of convergence for CTLE via the convex optimization design. Finally, this article presents numerical simulations to show the efficacy of the main results.

This article focuses on the problem of a distributed computation of continuous-time algebraic Riccati equations (CARE), where information of matrices is split and known by multiple agents. This article proposes a distributed optimization design of the iterative refinement technique (IRM), a well-established centralized method for CARE. By assuming that each agent only knows partial information of CARE, we reformulate IRM for CARE as three classes of distributed optimization subproblems with different formulations and constraints. Then, we propose distributed algorithms for obtained distributed optimization subproblems and prove convergence properties of proposed algorithms. Numerical results show the efficacy of the proposed distributed IRM.

We consider online optimization over Riemannian manifolds, where a learner attempts to minimize a sequence of time-varying loss functions defined on Riemannian manifolds. Though many Euclidean online convex optimization algorithms have been proven useful in a wide range of areas, less attention has been paid to their Riemannian counterparts. In this paper, we study Riemannian online gradient descent (R-OGD) on Hadamard manifolds for both geodesically convex and strongly geodesically convex loss functions, and Riemannian bandit algorithm (R-BAN) on Hadamard homogeneous manifolds for geodesically convex functions. We establish upper bounds on the regrets of the problem with respect to time horizon, manifold curvature, and manifold dimension. We also find a universal lower bound for the achievable regret by constructing an online convex optimization problem on Hadamard manifolds. All the obtained regret bounds match the corresponding results are provided in Euclidean spaces. Finally, some numerical experiments validate our theoretical results.

MOTIVATION: Single-cell multi-omics sequencing data can provide a comprehensive molecular view of cells. However, effective approaches for the integrative analysis of such data are challenging. Existing manifold alignment methods demonstrated the state-of-the-art performance on single-cell multi-omics data integration, but they are often limited by requiring that single-cell datasets be derived from the same underlying cellular structure. RESULTS: In this study, we present Pamona, a partial Gromov-Wasserstein distance-based manifold alignment framework that integrates heterogeneous single-cell multi-omics datasets with the aim of delineating and representing the shared and dataset-specific cellular structures across modalities. We formulate this task as a partial manifold alignment problem and develop a partial Gromov-Wasserstein optimal transport framework to solve it. Pamona identifies both shared and dataset-specific cells based on the computed probabilistic couplings of cells across datasets, and it aligns cellular modalities in a common low-dimensional space, while simultaneously preserving both shared and dataset-specific structures. Our framework can easily incorporate prior information, such as cell type annotations or cell-cell correspondence, to further improve alignment quality. We evaluated Pamona on a comprehensive set of publicly available benchmark datasets. We demonstrated that Pamona can accurately identify shared and dataset-specific cells, as well as faithfully recover and align cellular structures of heterogeneous single-cell modalities in a common space, outperforming the comparable existing methods. AVAILABILITYAND IMPLEMENTATION: Pamona software is available at https://github.com/caokai1073/Pamona. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The work studies how node-to-node communications over an Erd\H{o}s-R\'enyi random network influence distributed online convex optimization, which is vital in solving large-scale machine learning in antagonistic or changing environments. At per step, each node (computing unit) makes a local decision, experiences a loss evaluated with a convex function, and communicates the decision with other nodes over a network. The node-to-node communications are described by the Erd\H{o}s-R\'enyi rule, where independently each link takes place with a probability $p$ over a prescribed connected graph. The objective is to minimize the system-wide loss accumulated over a finite time horizon. We consider standard distributed gradient descents with full gradients, one-point bandits and two-points bandits for convex and strongly convex losses, respectively. We establish how the regret bounds scale with respect to time horizon $T$, network size $N$, decision dimension $d$, and an algebraic network connectivity. The regret bounds scaling with respect to $T$ match those obtained by state-of-the-art algorithms and fundamental limits in the corresponding centralized online optimization problems, e.g., $\mathcal{O}(\sqrt{T}) $ and $\mathcal{O}(\ln(T)) $ regrets are established for convex and strongly convex losses with full gradient feedback and two-points information, respectively. For classical Erd\H{o}s-R\'enyi networks over all-to-all possible node communications, the regret scalings with respect to the probability $p$ are analytically established, based on which the tradeoff between the communication overhead and computation accuracy is clearly demonstrated. Numerical studies have validated the theoretical findings.

MOTIVATION: Single-cell multi-omics data provide a comprehensive molecular view of cells. However, single-cell multi-omics datasets consist of unpaired cells measured with distinct unmatched features across modalities, making data integration challenging. RESULTS: In this study, we present a novel algorithm, termed UnionCom, for the unsupervised topological alignment of single-cell multi-omics integration. UnionCom does not require any correspondence information, either among cells or among features. It first embeds the intrinsic low-dimensional structure of each single-cell dataset into a distance matrix of cells within the same dataset and then aligns the cells across single-cell multi-omics datasets by matching the distance matrices via a matrix optimization method. Finally, it projects the distinct unmatched features across single-cell datasets into a common embedding space for feature comparability of the aligned cells. To match the complex non-linear geometrical distorted low-dimensional structures across datasets, UnionCom proposes and adjusts a global scaling parameter on distance matrices for aligning similar topological structures. It does not require one-to-one correspondence among cells across datasets, and it can accommodate samples with dataset-specific cell types. UnionCom outperforms state-of-the-art methods on both simulated and real single-cell multi-omics datasets. UnionCom is robust to parameter choices, as well as subsampling of features. AVAILABILITY AND IMPLEMENTATION: UnionCom software is available at https://github.com/caokai1073/UnionCom. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, we study an optimal output consensus problem for a multiagent network with agents in the form of multi-input multioutput minimum-phase dynamics. Optimal output consensus can be taken as an extended version of the existing output consensus problem for higher-order agents with an optimization requirement, where the output variables of agents are driven to achieve a consensus on the optimal solution of a global cost function. To solve this problem, we first construct an optimal signal generator, and then propose an embedded control scheme by embedding the generator in the feedback loop. We give two kinds of algorithms based on different available information along with both state feedback and output feedback, and prove that these algorithms with the embedded technique can guarantee the solvability of the problem for high-order multiagent systems under standard assumptions.

In this paper, a distributed resource allocation problem with nonsmooth local cost functions is considered, where the interaction among agents is depicted by strongly connected and weight-balanced digraphs. Here the decision variable of each agent is within a local feasibility constraint described as a convex set, and all the decision variables have to satisfy a network resource constraint, which is the sum of available resources. To solve the problem, a distributed continuous-time algorithm is developed by virtue of differentiated projection operations and differential inclusions, and its convergence to the optimal solution is proved via the set-valued LaSalle invariance principle. Furthermore, the exponential convergence of the proposed algorithm can be achieved when the local cost functions are differentiable with Lipschitz gradients and there are no local feasibility constraints. Finally, numerical examples are given to verify the effectiveness of the proposed algorithms.

Automata networks are a class of fully discrete dynamical systems, which have received considerable interest in various different areas. This brief addresses the observability of automata networks and switched automata networks in a unified framework, and proposes simple necessary and sufficient conditions for observability. The results are achieved by employing methods from symbolic computation, and are suited for implementation using computer algebra systems. Several examples are presented to demonstrate the application of the results.

In this paper, a distributed optimization problem is studied for continuous-time multiagent systems with unknown-frequency disturbances. A distributed gradient-based control is proposed for the agents to achieve the optimal consensus with estimating unknown frequencies and rejecting the bounded disturbance in the semi-global sense. Based on convex optimization analysis and adaptive internal model approach, the exact optimization solution can be obtained for the multiagent system disturbed by exogenous disturbances with uncertain parameters.

In the era of Internet of Things, WiFi fingerprinting based indoor positioning system (IPS) has been recognized as the most promising IPS for indoor location-based service. Fingerprinting-based algorithms critically rely on a fingerprint database built from machine learning methods, and extreme machine learning (ELM) is preferred for its fast training speed. However, traditional WiFi based IPS usually requires a central server to collect and process data, which is tremendously vulnerable to server breakdown and communication link failure. To address this issue, we propose Consensus-based Parallel ELM (CPELM) to enhance the robustness by distributing the data on different computation nodes. Specifically, each node keeps updating the corresponding terms in the ELM regression equation as a weighted average of those from neighboring nodes based on the distributed consensus iterative scheme. Upon the agreement of the regression equation within the network, the output weight of ELM can be calculated on some nodes and propagated to other nodes. Extensive simulation with real data has demonstrated that CPELM is able to produce same level of localization accuracy as centralized ELM without incurring additional computational cost, and in the meanwhile provides more robustness to the entire IPS in case of server breakdown and link failures.

The paper studies the distributed optimization problem for a class of nonlinear multiagent systems in the presence of external disturbances. To solve the problem, we need to achieve the optimal multiagent consensus based on local cost function information and neighboring information and meanwhile to reject local disturbance signals modeled by an exogenous system. With convex analysis and the internal model approach, we propose a distributed optimization controller for heterogeneous and nonlinear agents in the form of continuous-time minimum-phase systems with unity relative degree. We prove that the proposed design can solve the exact optimization problem with rejecting disturbances.

In this paper, we investigate source localization for wireless sensor networks based on received signal strength. We first formulate the localization problem as the intersection computation of a group of sensing rings, and then convert this non-convex problem into two weighted convex optimization problems. We next propose a unified distributed alternating projection algorithm to solve the resulting weighted optimization problems, where sensor nodes can communicate only locally with their neighbors over a time-varying jointly-connected topology. We also show that sensor nodes' estimates can achieve consensus on a possible minimizer. Both theoretical analysis and some comparative simulations reveal that the proposed approach has good estimation performance in both the consistent and inconsistent cases.

This paper addresses the leader escort control problem of multi-agent systems and discusses how the agents can be effectively separated into two groups to move symmetrically around their leader. A distributed design is given to escort the moving leader under an undirected switching graph, based on a neighbor-based state-estimation rule to estimate the leader's state and a neighbor-based escort controller. With the help of graph theory, the problem can be solved, and related simulations are proposed for illustration.

Hierarchical control for a class of nonlinear systems is discussed using approximate simulation relation in this paper. An error bound is obtained under a modified approximate simulation function at first. The interface construction problem is solved for a class of nonlinear systems. Moreover, with a small-gain type condition, the abstraction result is extended to interconnected systems with a sufficient condition for approximate simulation when a part of the considered system can be removed by abstraction.

A distributed output regulation problem is studied for a class of nonlinearly coupled multi-agent systems, taking a decentralized output-feedback form. By a networked internal model with a preassigned topology and a decentralized stabilization design, a distributed regulator is constructed and solves the problem. As an illustration, for a three-mass-three-spring system, a problem of synchronizing every agent motion to a harmonic oscillator can be solved.

This paper studies the distributed estimation problem for a moving discrete-time target under switching topologies and stochastic noises. For this problem, we propose a recursive distributed estimation algorithm based on state-consensus strategy. Under well-known observability and connectivity assumptions, an upper bound and lower bound for the total mean square estimation error (TMSEE) are established, respectively, by using common Lyapunov method and Kalman filtering theory.

In this paper, global finite-time stabilization problem for a large class of nonlinear control systems in the p normal form is considered. An iterative design approach is given based on Lyapunov function and homogeneity. The finite-time stabilizing control laws are constructed in the form of continuous but non-smooth time-invariant feedback.

We analyze two software rejuvenation policies of cluster server systems under varying workload, called fixed rejuvenation and delayed rejuvenation. In order to achieve a higher average throughput, we propose the delayed rejuvenation policy, which postpones the rejuvenation of individual nodes until off-peak hours. Analytic models using the well known paradigm of Markov chains are used. Since the size of the Markov model is nontrivial, automated specification generation, and the solution via stochastic Petri nets is utilized. Deterministic time to trigger rejuvenation is approximated by a 20-stage Erlangian distribution. Based on the numerical solutions of the models, we find that under the given context, although the fixed rejuvenation occasionally yields a higher throughput, the delayed rejuvenation policy seems to outperform fixed rejuvenation policy by up to 11%. We also compare the steady-state system availabilities of these two rejuvenation policies.

In this paper, an optimal training equalization for wireless communication is proposed and analyzed. By our scheme, the training of the equalizer is carried out periodically, with the training interval optimized for a maximal channel utilization. A closed-form expression for the optimal training interval is derived via a semi-Markov process which requires the knowledge of the channel equalization failure time distribution. A statistical estimation algorithm with complexity O(n/sup 2/) is presented and applied to adaptively estimate and track the optimal interval when the failure time distribution is not available. Numerical results show that by choosing the optimal training interval, the channel utilization can be improved and the statistical estimation algorithm can effectively approach the optimal solution with a reasonable number of failure time data points. By comparing this scheme with nonperiodic training scheme and the mean time to failure (MTTF)-based heuristic scheme, we find that our scheme outperforms the nonperiodic training scheme and provides an upper bound for MTTF-heuristic scheme.

An optimal training equalization for wireless communication is proposed and analyzed. By our scheme, the training of the equalizer is carried out periodically, with the training interval optimized for a maximal channel utilization. A closed-form expression for the optimal training interval is derived via a semi-Markov process (SMP) which requires knowledge of the channel equalization failure time distribution. A statistical estimation algorithm is presented and applied to adaptively estimate and track the optimal interval when the failure time distribution is not available. Numerical results show that by choosing the optimal training interval, the channel utilization can be improved, and the statistical estimation algorithm can effectively approach the optimal solution with a reasonable number of failure time data points.