This article addresses the distributed aggregative optimization (DAO) problem for high-order nonlinear systems with unknown pure-feedback dynamics over directed unbalanced networks, with the key contribution being the extension of DAO methods to high-order nonlinear systems. To achieve this, we first introduce auxiliary aggregative variables that integrate agent output and sampled neighbor information, progressively updated through a smoothing function. Using these variables and drawing inspiration from the dynamic average consensus-based approach, a pivotal theorem is introduced to facilitate the transformation of the DAO problem into a regulation problem, enabling the application of classical control methods to manage complex high-order dynamics. Furthermore, we present a control law based on prescribed performance functions and aggregative variables to solve the approximate aggregative optimization problem for high-order nonlinear agents with bounded disturbances. Finally, numerical examples are provided to validate the effectiveness of the proposed control scheme.

Standard domain adaptation methods require access to both source and target data. However, sharing source data is often impractical in real-world scenarios due to data privacy and memory limitation issues. In this work, we focus on the source-free domain adaptation (SFDA). Existing SFDA methods mainly learn independent information within individual samples and lack the utilization of topological information between samples. For this reason, we explicitly constrain the sample relational knowledge in the mean teacher framework for solving SFDA. Specifically, three relational graphs are first constructed based on the similarity between sample feature pairs: teacher, student, and teacher-student. Then, model adaptation is achieved via two consistency regularizations: 1) Inter-graph consistency constrains the consistency between graph structures. 2) Intra-graph consistency enhances the compactness of the samples within classes. In addition, to mitigate the effect of noisy pseudo labels, local prototypes during the iterations are continuously utilized to calibrate the global prototype to generate high-quality pseudo labels for the target samples. Further, the classification loss is reweighted according to the uncertainty of the pseudo labels, which allows the model to not only highlight the role of high-reliability samples but also to fully exploit the entire target domain. Extensive experimental results on Office-31, Office-Home, VisDA-2017, DomainNet and Digit dataset demonstrate the effectiveness of our method.

In this article, the finite-time distributed aggregative optimal consensus (DAOC) problems for multivehicle systems (MVSs) with multiple time-varying constraints are investigated. First, we formulate a new distributed optimization model, called finite-time DAOC (FT-DAOC), where each cost function contains an extra aggregative variable. Then, a class of new finite-time distributed algorithms is designed for MVSs with time-varying cost functions under time-varying digraphs. Moreover, as vehicles may work in settings with time-varying unknown control gains and unknown disturbances, we extend the newly presented distributed algorithms to solve finite-time aggregative optimal consensus issues for MVSs with multiple time-varying constraints. Finally, the validity of the newly illustrated algorithms is analyzed theoretically, and two simulation examples are provided.

In this article, the adaptive output consensus problem of high-order nonlinear heterogeneous agents is addressed using only delayed, sampled neighbor output measurements. A class of auxiliary variables is introduced which are n -times differentiable functions and include the agent's output along with delayed, sampled output neighbor measurements. It is proven that if these variables are bounded and regulated to zero then asymptotic consensus among all agent outputs is ensured. In view of this property, an adaptive distributed backstepping design procedure is presented that guarantees boundedness and regulation of the proposed variables. This design procedure ensures not only the desired asymptotic output consensus but also the uniform boundedness of all closed-loop variables. The main feature of our approach is that, in the proposed control law for each agent, the entire state vector of the neighbors is not needed and only delayed sampled measurements of the neighbors' outputs are utilized. The simulation results are also presented that verify our theoretical analysis.

This article studies the prescribed performance fault-tolerant control problem for a class of uncertain nonlinear multi-input and multioutput systems. A learning-based fault-tolerant controller is proposed to achieve the asymptotic stability, without requiring a priori knowledge of the system dynamics. To deal with the prescribed performance, a new error transformation function is introduced to convert the constrained error dynamics into an equivalent unconstrained one. Under the actor-critic learning structure, a continuous-time long-term performance index is presented to evaluate the current control behavior. Then, a critic network is used to approximate the designed performance index and provide a reinforcement signal to the action network. Based on the robust integral of the sign of error feedback control method, an action network-based controller is developed. It is shown by the Lyapunov approach that the tracking error can converge to zero asymptotically with the prescribed performance guaranteed. Simulation results are provided to validate the feasibility and effectiveness of the proposed control scheme.

This study investigates the consensus problem of a nonlinear discrete-time multi-agent system (MAS) under bounded additive disturbances. We propose a self-triggered robust distributed model predictive control consensus algorithm. A new cost function is constructed and MAS is coupled through this function. Based on the proposed cost function, a self-triggered mechanism is adopted to reduce the communication load. Furthermore, to overcome additive disturbances, a local minimum-maximum optimization problem under the worst-case scenario is solved iteratively by the model predictive controller of each agent. Sufficient conditions are provided to guarantee the iterative feasibility of the algorithm and the consensus of the closed-loop MAS. For each agent, we provide a concrete form of compatibility constraint and a consensus error terminal region. Numerical examples are provided to illustrate the effectiveness and correctness of the proposed algorithm.

In this study, we present the convergence of time-varying networks. Then, we apply the convergence property to cooperative control of nonlinear multiagent systems (MASs) with unknown control directions (UCDs), and illustrate a new kind of Nussbaum-type function based control algorithms. It is proven that if the time-varying networks are cut-balance, the convergence of nonlinear MASs with nonidentical UCDs is achieved using the presented algorithms. A critical feature of this application is that the designed algorithms can deal with nonidentical UCDs by employing conventional Nussbaum-type functions. Finally, one simulation example is given to illustrate the effectiveness of the presented algorithms.

In this paper, an observer-based adaptive prescribed performance tracking control scheme is developed for a class of uncertain multi-input multi-output nonlinear systems with or without input saturation. A novel finite-time neural network disturbance observer is constructed to estimate the system uncertainties and external disturbances. To guarantee the prescribed performance, an error transformation is applied to transfer the time-varying constraints into a constant constraint. Then, by employing a barrier Lyapunov function and the backstepping technique, an observer-based tracking control strategy is presented. It is proven that using the proposed algorithm, all the closed-loop signals are bounded, and the tracking errors satisfy the predefined time-varying performance requirements. Finally, simulation results on a quadrotor system are given to illustrate the effectiveness of the proposed control scheme.

This article proposes an adaptive neural network (NN) distributed control algorithm for a group of high-order nonlinear agents with nonidentical unknown control directions (UCDs) under signed time-varying topologies. An important lemma on the convergence property is first established for agents with antagonistic time-varying interactions, and then by using Nussbaum-type functions, a new class of NN distributed control algorithms is proposed. If the signed time-varying topologies are cut-balanced and uniformly in time structurally balanced, then convergence is achieved for a group of nonlinear agents. Moreover, the proposed algorithms are adopted to achieve the bipartite consensus of high-order nonlinear agents with nonidentical UCDs under signed graphs, which are uniformly quasi-strongly δ -connected. Finally, simulation examples are given to illustrate the effectiveness of the NN distributed control algorithms.

Existing results for cooperative control of high-order agents mainly employ the Nussbaum-type function to cope with unknown control directions and mostly require an assumption that the control directions are identical and unknown. This paper proposes a class of algorithms with nonlinear PI functions to relax such an assumption and make them suitable for nonidentical unknown control directions. It is proven that if the distributed nonlinear PI functions are suitably selected, the proposed algorithms can achieve consensus for high-order agents under strongly connected topologies and switching topologies with a jointly strongly connected basis (JSCB). Furthermore, we extend the consensus results to the case of time-varying topologies described by δ-connected, continuous graphs. As a special case, the consensus of high-order agents under the directed graph having a spanning tree is also investigated. Finally, illustrative simulations are presented to indicate the efficiency of the proposed algorithms.

We use the advanced proximal policy optimization (PPO) reinforcement learning algorithm to optimize the stochastic control strategy to achieve speed control of the “model-free” quadrotor. The model is controlled by four learned neural networks, which directly map the system states to control commands in an end-to-end style. By introducing an integral compensator into the actor-critic framework, the speed tracking accuracy and robustness have been greatly enhanced. In addition, a two-phase learning scheme which includes both offline- and online-learning is developed for practical use. A model with strong generalization ability is learned in the offline phase. Then, the flight policy of the model is continuously optimized in the online learning phase. Finally, the performances of our proposed algorithm are compared with those of the traditional PID algorithm.

In this paper, we investigate the adaptive cooperative control problem with guaranteed convergence for a class of nonlinear multiagent systems with unknown control directions and time-varying topologies. A key lemma is first derived which involves dynamically changing interaction topologies, and then a new kind of distributed control algorithms with Nussbaum-type functions are proposed based on this lemma. It is proven that if the topologies are time varying with integral weight uniform upper bound and reciprocity, then convergence is guaranteed with the proposed algorithms for nonlinear multiagent systems with nonidentical unknown control directions. An important feature of this paper is that, under time-varying topologies, the designed algorithms can deal with nonidentical unknown control directions by using classical Nussbaum-type functions. Moreover, with the proposed algorithms, we extend the adaptive cooperative control results to the case of δ -connected graphs. In particular, the adaptive leaderless consensus of high-order nonlinear agents with nonidentical unknown control directions and a directed graph having a spanning tree is also tackled as a special case. Finally, theoretical results are illustrated by a group of Genesio-Tesi systems with distributed control algorithms under time-varying topologies and some special network topologies.

This paper solves the adaptive consensus problem for first-order linearly parameterized agents with completely nonidentical unknown high-frequency gain signs under directed graphs. A new class of Nussbaum-type function-based algorithms are proposed to handle the unknown high-frequency gain signs adaptively and cooperatively. It is shown that if the underlying topology is a fixed graph with strongly connected or switching topologies having a jointly strongly connected basis, the first-order linearly parameterized agents with nonidentical unknown high-frequency gain signs can achieve asymptotic consensus. Finally, the effectiveness of proposed algorithms are verified by one simulation example.

This paper addresses the robust cooperative control for lateral formation tracking a set of circles on the given sphere in an absolutely unknown spatial flowfield. Different from the adaptive estimation method for the unknown flow speed with knowledge of the velocity direction in the literatures, a novel adaptive neural estimate is constructed based on the neighbors' information to approximate the unknown flow velocity. It is noted that such neighbor-based adaptive neural estimation develops the traditional adaptive neural approach by consensus. Then, a robust spherical formation tracking control law is established according to flow estimation. The uniform ultimate boundedness is proven when the communication topology associated with networked first-order agents. The effectiveness of the analytical results is verified by numerical simulations.

In the copper electrolytic refining process, the short-circuit failure among the electrodes usually exists. When it occurs, the temperature distribution is often abnormal, accompanied by local or overall overheating. This paper proposes a novel processing method for infrared temperature images to effectively detect the short-circuit fault and locate the accurate malfunctioning spots. The design procedure contains contrast enhancement, filter denoising, edge detection, image segmentation, high temperature electrode positioning. Finally, experimental results show that the proposed method can guarantee real-time processing of the infrared image of the electrolytic cell and accurately mark the malfunctioning position of electrodes with high temperature fault.

Service capability, which represents the actions performed or the information delivered by a service, has become an important issue for service-oriented architecture. But most of the current semantic representation methods for service capabilities are usually based on top-down methodology and there is a gap between the semantic web services approach and the real features of web services. We aim to develop services characterization methods with statistical study on existing web services and to improve services capability representation with bottom-up software services comprehension. In this paper, the main issues for statistical study on existing web services are summarized. Two types of services characterization methods are proposed in our work: quantitative statistical study which used for probing the distribution of main objects in web services and relational statistical study which used for clustering actions or contents and measuring the similarity of web services. We conducted a statistical study on more than four hundred WSDL documents collected from XMethods.net, Amazon and Google and main quantitive statistical results are presented in this paper. A statistical relational model is proposed for mining and recognizing the patterns of services.

Most of the SOA (Service Oriented Architecture) applications are not brand new and usually evolved from legacy systems. Legacy systems carry out the enterprisepsilas most crucial business information together with business processes and many organizations have leveraged the value of their legacy systems by exposing parts of it as services. Most of current Web services are SOAP-RPC style services. The evolution of the Web 2.0 phenomenon has led to the increased adoption of the RESTful services paradigm and reengineering legacy system to SOA with RESTful Web services is not only for reusing but will bring other benefits due to its special features. In this paper, the key issues for reengineering legacy systems with RESTful Web services are discussed. A common process for reengineering legacy systems to REST-style is proposed. The candidate Web services are identified by legacy systems comprehension at first. Then blend services are generated based on relationships between entities and constraint rules specified. The generated URIs are refined and split carefully to represent the RESTful Web services.

Web services (WS) are becoming more and more popular nowadays and service-oriented architecture (SOA) have been widely used in the construction of information systems. But most of the SOA applications are not brand new and usually evolved from legacy systems. Our research group is building a service identification framework used for the SOA reengineering of existing large-scale information applications. Service capabilities have become an important issue we care about which are the actions performed or the information delivered by a service. But the semantic description for service capabilities has not been fully addressed in the current approach. In this paper, we analyze the contributions and limitations of current approach. In order to find the practical features of existing web services, we conducted a statistical study on more than four hundred WSDL documents collected from XMethods.net, Amazon and Google. Then we propose a new approach for Semantic Web Services description. Service capabilities are represented by informative entities and standard actions are specified for each informative entity.

Publishing and delivering traffic information generally depend on message board and public radio service in China, which usually sends out static information without dynamic interaction between the services and the users. With the development of WebGIS and its successful application, it is possible to introduce the Web-based multiple architecture into the traffic information system. Currently, most WebGIS portals only provide static information in words and figures without any significant improvement in dynamic aspect, which is a very important requirement for traffic information system. We develop an urban traffic information portal for dynamic information updating and responding, which is based on the fast growing rich Internet application and WebGIS technology. The portal can not only provide the real-time information but also can interact with users for more specific. This paper introduces the multi-layer architecture used in our project. Reusable services related with GIS services are designed and encapsulated as portlet. We briefly introduce some implements and optimization at the end of this paper.