Large-scale Complex Systems Research Articles

Probabilistic message passing control and FPD based decentralised control for stochastic complex systems

This paper offers a novel decentralised control strategy for a class of linear stochastic largescale complex systems. The proposed control strategy is developed to address the main challenges in controlling complex systems such as high dimensionality, stochasticity, uncertainties, and unknown system parameters. To overcome a wide range of domain of complex systems, the proposed strategy decomposes the complex system into several subsystems and controls the system in a decentralised manner. The global control objective is achieved by individually controlling all the local subsystems and then exchanging information between subsystems about their state values. This paper mainly focuses on the probabilistic communication between subsystems, therefore the detailed process of message-passing probabilistic framework is provided. For each subsystem, the regulation problem is considered, and fully probabilistic design (FPD) is applied to take the stochastic nature of complex systems into consideration. Also, since the governing equations of the system dynamics are assumed to be unknown, linear optimisation methods are employed to estimate the parameters of the subsystems. To demonstrate the effectiveness of the proposed control framework, a numerical example is given.

Learning Markov Jump Affine Systems via Regression Trees for MPC

Model Predictive Control is a well consolidated technique to design optimal control strategies, leveraging the capability of a mathematical model to predict a system’s behavior over a time horizon. However, building physics-based models for complex large-scale systems can be cost and time prohibitive. To overcome this problem we propose a methodology to exploit Regression Trees technique in order to build a Markov Jump System model of a large-scale system using historical data, and apply Model Predictive Control. A comparison with an optimal benchmark and related techniques is provided on an energy management system to validate the performance of the proposed methodology.

Distributed Model Predictive Safety Certification for Learning-based Control

While distributed algorithms provide advantages for the control of complex large-scale systems by requiring a lower local computational load and less local memory, it is a challenging task to design high-performance distributed control policies. Learning-based control algorithms offer promising opportunities to address this challenge, but generally cannot guarantee safety in terms of state and input constraint satisfaction. A recently proposed safety framework for centralized linear systems ensures safety by matching the learning-based input online with the initial input of a model predictive control law capable of driving the system to a terminal set known to be safe. We extend this idea to derive a distributed model predictive safety certification (DMPSC) scheme, which is able to ensure state and input constraint satisfaction when applying any learning-based control algorithm to an uncertain distributed linear system with dynamic couplings. The scheme is based on a distributed tube-based model predictive control (MPC) concept, where subsystems negotiate local tube sizes among neighbors in order to mitigate restrictiveness of the safety approach. In addition, we present a technique for generating a structured ellipsoidal robust positive invariant tube. In numerical simulations, we show that the safety framework ensures constraint satisfaction for an initially unsafe control policy and allows to improve overall control performance compared to robust distributed MPC.

Application of Image Processing Technology in Substation Patrol Inspection

Electricity plays an important role in the development of national economy, and substation is the core of the whole power grid, which plays an important role in dispatching power supply. In the past, substation management mainly relied on human resources to monitor and inspect the substation on duty, which caused a lot of waste of electric power technicians. With the development of automation technology and network technology, a new substation management mode emerged as the times require, that is, the application of image processing technology in substation inspection. This kind of substation inspection involves image and voice compression technology, computer technology, multimedia network technology, so it is a relatively complex large-scale system. Image processing technology makes it possible to patrol unattended substations. In view of space, this paper mainly introduces the application of image processing technology in substation patrol.

System Portfolio Selection for Large-Scale Complex Systems Construction

With the number of alternative systems increasing, the system portfolio selection problem for large-scale complex systems is an non-deterministic polynomial (NP)-hard problem. The time cost of the classification selection algorithm used for the portfolio selection is intolerable; thus, improving the algorithm is necessary. In this paper, first, the weapon system portfolio selection (WSPS) model is categorized into two types: single objective and multiobjective; the optimization difficulties are analyzed; and the feasible solution space reduction strategy is given. Second, a portfolio selection optimization algorithm based on the difference evolution technique for order preference by similarity to ideal solution (DE-TOPSIS) is proposed where the weapon system weighting method TOPSIS is integrated with the DE algorithm. Finally, considering different weapon system scales, the advantages of the proposed algorithm are illustrated by comparing it with two other algorithms in a single-target case and two other algorithms in a multiobjective case. The results indicate that the DE algorithm always has better performance with regard to optimal solution quality, convergence speed, and algorithm stability.

Performance design of a cryogenic air separation unit for variable working conditions using the lumped parameter model

Large-scale cryogenic air separation units (ASUs), which are widely used in global petrochemical and semiconductor industries, are being developed with high operating elasticity under variable working conditions. Different from discrete processes in traditional machinery manufacturing, the ASU process is continuous and involves the compression, adsorption, cooling, condensation, liquefaction, evaporation, and distillation of multiple streams. This feature indicates that thousands of technical parameters in adsorption, heat transfer, and distillation processes are correlated and merged into a large-scale complex system. A lumped parameter model (LPM) of ASU is proposed by lumping the main factors together and simplifying the secondary ones to achieve accurate and fast performance design. On the basis of material and energy conservation laws, the piecewise-lumped parameters are extracted under variable working conditions by using LPM. Takagi-Sugeno (T-S) fuzzy interval detection is recursively utilized to determine whether the critical point is detected or not by using different thresholds. Compared with the traditional method, LPM is particularly suitable for “rough first then precise” modeling by expanding the feasible domain using fuzzy intervals. With LPM, the performance of the air compressor, molecular sieve adsorber, turbo expander, main plate-fin heat exchangers, and packing column of a 100000 Nm3 O2/h large-scale ASU is enhanced to adapt to variable working conditions. The designed value of net power consumption per unit of oxygen production (kW/(Nm3 O2)) is reduced by 6.45%.

An Augmented Lagrangian Approach for Distributed Robust Estimation in Large-Scale Systems

Nonlinear estimation using maximum-likelihood estimation and maximum a posterior probability approaches is frequently employed for large-scale cyber-physical and complex systems in the big data era. Efficient distributed processing and robust estimation algorithms resilient to outliers are of great importance. This paper proposes a novel method for distributed solution of these robust estimation problems with equality constraints based on the augmented Lagrangian method (ALM). Specifically, a novel covariance normalization method and an automatic method for selecting regularization parameter with improved performance are proposed. Under the ALM framework, nonlinear equality constraints and nonsmooth L 1 regularization can be incorporated. The proposed method is illustrated with two emerging applications respectively in robust distributed power system state estimation (DSSE) with nonlinear zero injection constraints and gene regulatory network (GRN) identification. Experimental results in DSSE show that the covariance normalization method improves considerably the convergence speed over the alternating direction method of multipliers algorithm and the robust statistics employed effectively mitigates the adverse effects of extreme outliers. Zero-injection constraints can be effectively incorporated. For GRN identification, putative genes and connectivity for a yeast dataset with 1.57 million variables can be identified via sparsity and piecewise temporal continuity penalties and they generally align well with literature.

A nonintrusive reduced order modelling approach using Proper Orthogonal Decomposition and locally adaptive sparse grids

Large-scale complex systems require high fidelity models to capture the dynamics of the system accurately. The complexity of these models, however, renders their use to be expensive for applications relying on repeated evaluations, such as control, optimization, and uncertainty quantification. Proper Orthogonal Decomposition (POD) is a powerful Reduced Order Modelling (ROM) technique developed to reduce the computational burden of high fidelity models. In cases where the model is inaccessible, POD can be used in a nonintrusive manner. The accuracy and efficiency of the nonintrusive reduced model are highly dependent on the sampling scheme, especially for high dimensional problems.To that end, we study integrating the locally adaptive sparse grids with the POD method to develop a novel nonintrusive POD-based reduced order model. In our proposed approach, the locally adaptive sparse grid is used to adaptively control the sampling scheme for the POD snapshots, and the hierarchical interpolant is used as a surrogate model for the POD coefficients. An approach to efficiently update the surpluses of the sparse grids with each POD snapshots update is also introduced. The robustness and efficiency of the locally adaptive algorithm are increased by introducing a greediness parameter, and a strategy to validate the reduced model after convergence. The proposed validation algorithm can also enrich the reduced model around regions of detected discrepancies. Three numerical test cases are presented to demonstrate the potential of the proposed POD-Adaptive algorithm. The first is a nuclear reactor point kinetics, the second is a general diffusion problem, and the last is a variation of the analytical Morris function. The results show that the developed algorithm reduced the number of model evaluations compared to the classical sparse grid approach. The built reduced models captured the dynamics of the reference systems with the desired tolerances. The non-intrusiveness and simplicity of the method provide great potential for a wide range of practical large scale applications.

Characteristic Analysis and Modeling of Network Traffic for the Electromagnetic Launch System

The electromagnetic launch system (EMLS) is a kind of large-scale complex system with strong mechanical, electrical, and hydraulic coupling. The effective operation of the system requires the support of advanced and reliable health management system, and the function of the health management system relies on the network traffic to complete data transmission and interaction. Through protocol filtering and time-frequency characteristic analysis of network traffic, it is found that the system mainly includes CIP and TCP; traffic of CIP shows continuity and stability, while traffic of TCP shows sparsity and impact. Based on the analysis of traffic characteristics, an SVR-ARMA network traffic model is established, which improves the prediction accuracy and performance of network traffic and provides a reliable technical guarantee for the fault diagnosis and health management of the EMLS.

A Passivity-Based Approach for Kinematic Control of Manipulators With Constraints

Most traditional methods for solving the kinematic control problem of redundant manipulators are designed from a signal processing perspective. However, such a perspective may make the resultant design difficult for practitioners to understand. If the problem is addressed from an energy perspective, the resultant design may be more comprehensive, because energy is a universal concept and can be used to describe complex large-scale industrial systems. Passivity is a property of engineering systems, which is characterized through energy transformation. In this paper, a passivity-based approach is proposed for the kinematic control of redundant manipulators, where the joint velocity limit of manipulators is also considered. The performance of the approach is theoretically guaranteed. In addition, simulative examples are presented to validate the efficacy of the approach and the theoretical results.

The Wireless Access for Future Smart Cities as a Large Scale Complex Cyber Physical System

In future smart cities (SCs) highly developed and smart wireless communication access infrastructures will be needed for the connection of a huge number of different types of objects, sensors and user terminals. Such access networks must have the necessary autonomic and intelligent mechanisms to respond to the needs of an increasing variety of users (human and non-human), to cope with the high user density in SCs, their mobility, new and increasing service requirements, traffic dynamics, SC complex wireless channel conditions, etc. The wireless AN of a future SC must be a type of network which is able to offer revolutionary services, capabilities, and facilities that are hard to be provided via the heterogeneous network (HetNet) infrastructures that are implemented today. This paper introduces the concept of the unified wireless access (UWA) for SCs and considers some of the challenges related to its functional requirements and design. The structure of a sample UWA network illustrating the functional relations between its components is given. It is envisaged that such UWA architecture will perform and could be considered from the aspect of a large scale complex and intelligent cyber physical system (CPS) with control feedbacks and different types of users introducing stochasticity in the loop of the system. For the goal of analyzing the performance and functional relationships between the elements of such UWA a general modeling approach is introduced taking into consideration some of the basic approaches applied for CPS analysis.

Bipolar Fuzzy Nominal Classification (BFNC) framework: Application to risk analysis

Nominal classification (NC) is a subfield of multi-criteria decision making where an object (in a broad sense) characterized by some attributes (with their valuation belonging to an ordered set, numeric in general) must be assigned to one of pre-defined classes or categories; these classes are char acterized by some numerical valued features. This is also known as supervised classification as opposed to unsupervised classification in machine learning literature. In many applications such as that of risk analysis, characterization of classes by features may not be precisely defined; they will be rather fuzzily expressed using linguistic appreciation such as high is better, low is more appreciated, medium range is better, etc. leading to what is referred to as fuzzy nominal classification (FNC). On other hand bipolar reasoning is pervasive in classification in the sense that given a couple (feature, class), there will be some values of the feature that lead to automatically assigning (respect. automatically excluding to assign) the considered object into that class leading to what we name bipolar fuzzy nominal classification or BFNC for short; the main purpose of this paper is to develop this BFNC framework with risk analysis as an illustrative application domain. The stepping stones of this framework are two indexes for each couple (class, object) known as classifiability index (that measures the extent to which the considered object can be included into that class) and the rejectability index measuring the extent to which one should avoid including this object into that class. By using two indexes for classification, many classes can be qualified for inclusion of a given object rendering this framework flexible. Analyzing risks for large-scale complex systems requires identifying, assessing, and prioritizing different risk scenarios for their appropriate treatment such as resources allocation for risk mitigation, risk prevention, risk sharing, etc. To this end and given scarcity of resources in general, one must consider first prioritizing, filtering, or scoring risks that return to assigning them to pre-defined classes or categories; that is nominally classifying them. The developed BFNC framework applied to a real world application in the domain of countries’ risk classification shows its practical potentialities.

Multi-time scale dynamic analysis of integrated energy systems: An individual-based model

This paper proposes an individual-based model (IBM) for the modeling of large-scale heterogenous complex systems. The IBM decouples the complex systems into independent individuals based on physical characteristics. In order to formulate the actions of individuals and interactions between individuals, the IBM is formalized with a quintuple parameter set comprised of input, knowledge, state, function, and output sets. Accordingly, individuals can make decisions independently applying accurate evolutionary mechanisms described in the function set. Additionally, the individuals can interact with others by input and output sets in a uniform manner. Consequently, a complex system can be modelled by independent individuals whose internal characteristics are fully specified and hidden from the external environment. The IBM is applied to model heterogenous integrated energy systems (IES) and simulate the multi-time scale dynamics of the IES under conditions of disturbances and faults. The results show that the IBM are capable of simulating quantified system states and unquantified rules of the IES dynamics simultaneously. Furthermore, the IBM can significantly improve the computation efficiency in terms of computation time and interaction, compared with the traditional equal-step method. Therefore, it is verified that the proposed IBM is an efficient method for complex systems.

Automatic Implementation of Fuzzy Reasoning Spiking Neural P Systems for Diagnosing Faults in Complex Power Systems

As an important variant of membrane computing models, fuzzy reasoning spiking neural P systems (FRSN P systems) were introduced to build a link between P systems and fault diagnosis applications. An FRSN P system offers an intuitive illustration based on a strictly mathematical expression, a good fault‐tolerant capacity, a good description for the relationships between protective devices and faults, and an understandable diagnosis model‐building process. However, the implementation of FRSN P systems is still at a manual process, which is a time‐consuming and hard labor work, especially impossible to perform on large‐scale complex power systems. This manual process seriously limits the use of FRSN P systems to diagnose faults in large‐scale complex power systems and has always been a challenging and ongoing task for many years. In this work we develop an automatic implementation method for automatically fulfilling the hard task, named membrane computing fault diagnosis (MCFD) method. This is a very significant attempt in the development of FRSN P systems and even of the membrane computing applications. MCFD is realized by automating input and output, and diagnosis processes consists of network topology analysis, suspicious fault component analysis, construction of FRSN P systems for suspicious fault components, and fuzzy inference. Also, the feasibility of the FRSN P system is verified on the IEEE14, IEEE 39, and IEEE 118 node systems.

A multi-level generic multi-agent architecture for supervision of collective cyber-physical systems

Cyber-physical systems like networked embedded systems or ambient sensors networks are called large-scale artificial complex systems. They are difficult to supervise because of their numerous components in interaction relying upon physical devices, their extension and their openness. A multi-scale organisation can be a solution to make them more accessible. Multi-agent systems (MAS) are well suited for modeling large complex systems, as multi-agent organisational capabilities allow introducing multi-level observation. This paper proposes a multi-level multi-agent mechanism based on recursion for supervision and observation of large-scale artificial complex systems. It is developed as a free framework that is a decentralised application allowing truly physically decentralised MAS to communicate with abstract multi-agent layers. As an illustration, the framework is applied to a wireless sensor network supervision system.

Research on massive information query and intelligent analysis method in a complex large-scale system.

With the rapid growth of big data and network information, it is particularly important to perform information query and intelligent analysis on unstructured massive data in large-scale complex systems. The existing methods of directly collating, sorting, summarizing, and storing retrieval of documents cannot meet the needs of information management and rapid retrieval of massive data. This paper takes the standardized storage, effective extraction and standardized database construction of massive resume information in social large-scale complex systems as an example, and proposes a massive information query and intelligent analysis method. The method utilizes the semi-structured features of the resume document, constructs the extraction rule model of various resume data to extract the massive resume information. On the basis of HBase distributed storage, with the help of parallel computing technology to optimize the storage and query efficiency, which ensures the intelligent analysis and retrieval of massive resume information. The experimental results show that this method not only greatly improves the extraction accuracy and recall rate of resume information data, but also compared with the traditional methods, there are obvious improvements in the three aspects of massive information retrieval methods, query usage efficiency, and the intelligent analysis of complex systems.

Self-Adaptation Techniques in Cyber-Physical Systems (CPSs)

Cyber-Physical Systems (CPSs) are large-scale complex systems that monitor and control physical processes by using computer algorithms tightly integrated with networking and their users. Monitoring and controlling the physical environment is a hot topic for today's researchers and engineers in academia and industry. Within this realm, an important feature of current and future Information and Communications Technology (ICT) systems is self-adaptation-yet there is a shortage of information focusing on this characteristic in the literature, particularly as it relates to CPSs. Here, we investigate current state-of-the-art research on CPSs from this perspective, and evaluate the main self-adaptive approaches proposed in the literature, along with their results, strengths, and weaknesses. We also discuss appropriate techniques for enabling self-adaptation capabilities within CPSs at different architectural layers. Overcoming the challenges associated with designing and implementing self-adaptive mechanisms in CPSs will provide a path for bolstering a new generation of CPSs with greater robustness and reliability.

Transit network design with meta-heuristic algorithms and agent based simulation

Abstract In this work, a transit network design problem is presented. The problem is identified as a typical large-scale complex system. Subsequently, it is decomposed into its sub-components. The first two sub-components, which encompass the network design and frequency setting problems, are then tackled by means of an innovative solution framework that combines a genetic algorithm with agent-based travel demand modelling. An analysis of results obtained from applying the proposed method to different testing scenarios shows that it is capable of designing transit networks that address the individual and collective perspectives of different stakeholders. Hence it can be used as a viable decision support tool for policy makers in the transportation network sector.

Collaborative Creation of Engineering Artifacts by Geographically-Distributed Teams

It is common practice today for engineering development projects to be partitioned across multiple, geographically-distributed work locations, often in multiple times zones, and at times, in different countries. Underlying this practice is an implied assumption that the tools and services provided by such the electronic collaboration infrastructure (e.g., electronic mail, audio and video teleconferencing, group on-line editing, sharable file repositories, etc.) used to interconnect the people at these various sites is adequate for supporting such geographically-distributed teams, facilitating their inter-personal communications and collaboration. The goal is for collaborative work to be accomplished with little or no physical travel between these geographically-distributed work sites. In this paper, we describe research conducted to test the validity of this hypothesis, within the domain of projects intended to develop complex, software-intensive systems, of the sort used by the aerospace and energy industries.We discovered important limitations in such electronic collaboration infrastructures, which limit their ability to support such geographically-distributed collaborative development. Specifically, we found that (for large-scale complex systems development) there is evidence that such mechanisms are effective for geographically-distributed review of engineering artifacts, but that they are not effective for geographically-distributed creation of such artifacts. Understanding this distinction is therefore crucial for effective project management. Such an understanding also identifies topics for future research and development: the need to create more effective mechanisms to support the geographically-distributed creation of complex engineering artifacts. To this end, we present findings from our research on how collaborative generation of engineering artifacts occurs, what are the specific challenges that arise from various modes of geographically-distributed project implementation, and why (within the considered problem domain) current electronic collaboration infrastructure tools are not effective to support this geographically-distributed engineering creation. We conclude by postulating mechanisms and methods that are beginning to contribute to the development of effective tools for geographically-distributed creation of complex engineering artifacts.

Evolution of clusters in large-scale dynamical networks

Recent tremendous progress in electronics, complexity theory and network science provides new opportunities for intellectual control of complex large-scale systems operating in turbulent environment via networks of interconnected miniature devices, serving as actuators, sensors and data processors. Actual dynamics of the resulting control systems are too sophisticated to be examined controlled by traditional methods, which primarily deal with ordinary differential equations. However, their complexity can be dramatically reduced by fast processes, organizing the elementary units of the system (called agents) into relatively small number of clusters. The clusters emerge and deteriorate in response to changes in the environment, and the processes of their formation and destruction are very short in time. During the periods of the clusters’ existence, the system’s dynamics is essentially low-dimensional due to synchronization between the agents in each cluster. An enormously complicated system is thus reduced to a finite-dimensional model with time-varying structure of the state vector. The low-dimensionality of the reduced model allows to control it by using classical methods, e.g. model-predictive or adaptive control. This philosophy of complex systems control is illustrated on an experimental setup, called the “airplane with feathers”. The wings of this airplane are equipped with arrays of microsensors, microcomputers, and microactuators (“feathers”). The feathers self-organize into clusters by using a multi-agent consensus protocol; the aim of this coordination is to reduce the perturbing forces, affecting the airplane in a turbulent flow.