EditorialAdvances in Modelling, Monitoring, and Control for ComplexIndustrial Systems

Zhiwei Gao ,1 Sing Kiong Nguang ,2 and De-Xing Kong 3

1Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne, NE1 8ST, UK2Department of Electrical and Computer Engineering, University of Auckland, Auckland 1010, New Zealand3Department of Mathematics, Zhejiang University, Hangzhou, China

Correspondence should be addressed to Zhiwei Gao; zhiwei.gao@northumbria.ac.uk

Received 23 December 2018; Accepted 24 December 2018; Published 27 February 2019

Copyright © 2019 Zhiwei Gao et al. is is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

Nowadays, industrial systems, such as chemical process,manufacturing process, power network, sustainable energysystems, transportation systems, wireless communicationnetwork, robotic systems, and biomedical systems, arebecoming more complex, which are generally composed ofa couple of interconnected systems, possess high-nonlinearand stochastic dynamics, are equipped with multiple controlloops, and operate under noisy environments and varyingloads. On the other hand, industrial systems are becomingmore expensive, which have higher requirement for oper-ation performance, productiveness, availability, reliability,and safety. Moreover, industry 4.0 has become the currenttrend of automation industries, which has great impactson improving the reliability and operation performance ofcomplex industrial systems. erefore, it is paramount butchallenging to develop effective techniques in modelling,monitoring, and control for complex industrial systems. Asrapid developments of industrial automation, cloud and cog-nitive computation, internet of things, artificial intelligence,cyber-physical systems, and sensor technologies, modelling,monitoring, and control for complex industrial systems havebeen greatly stimulated and new techniques and applicationshave emerged in recent years.

is special issue aims to provide a platform forresearchers and engineers to report their recent results,exchange research ideas, and overlook emerging researchand application directions in modelling, monitoring, andadvanced control for complex industrial systems. A�er a

strict peer-review process, thirteen papers are selected out offorty submissions.e selected papers can be categorized intomodelling and optimization for complex industrial systems,monitoring and diagnosis for complex industrial systems,and advanced control for complex industrial systems, respec-tively.epapers selected in this special issuewill be reviewedfollowing the categories mentioned above.

2. Modelling and Optimization for ComplexIndustrial Systems

Solid oxide fuel cells (SOFCs) are electrochemical devicesconverting chemical energy stored in hydrocarbon fuelsinto electrical energy, which are alternatives to conventionalpower generation schemes. It is interesting to describedynamic behavior of a solid oxide fuel cell, which is multi-input and multioutput process in essence. In the paperentitled “TowardsReduced-OrderModels of SolidOxide FuelCell” authored by M. Lawrynczuk, modelling issue of a solidoxide fuel cell is investigated. By using the first-principletheory, full-order model is established firstly, and reduced-models are then derived by using state-truncation methodand residualisation model-reduction method, respectively.e effectiveness of the established models is demonstratedby using simulation studies.

Recently, fractional-order differentiation equationshave received attention in modelling of complex systems.e paper entitled “Fractional-Derivative Approximationof Relaxation in Complex Systems,” authored by K. M.Li et al., presents a system identification procedure to

HindawiComplexityVolume 2019, Article ID 2975083, 3 pageshttps://doi.org/10.1155/2019/2975083

2 Complexity

build a factional-order differential equation model toaccurately describe time-dependent behavior of a classof complex systems. Caputo definition of the fractionalderivative, Mattag-Leffler function, and simulated annealingoptimization algorithm are used to identify the modelparameters. e proposed system identification modellingalgorithm is validated by using experimental data of a shell-and-tube heat exchanger and a multiroom building test bed.

Four-stage helicopter main gearbox has a complex struc-ture, and its dynamic response has a significant influenceon the fatigue life of the system. As a result, it is significantto carry out modelling and dynamic analysis of the four-stage helicopter gearbox. In the paper entitled “A NewMath-ematical Modeling Method for Four-Stage Helicopter MainGearbox and Dynamic Response Optimisation” authoredby Y. Chen et al., finite element method and lumped massmethod are synthesized to model the overall multinodedynamic model of a four-stage helicopter main gearbox.

Logistics and supply chain networks consist in a largeamount of interconnected actors with significant nonlinearand stochastics features; therefore, it is challenging to modeland analyse this kind of complex industrial systems. Inthe paper entitled “Design of a Logistics Nonlinear Systemfor a Complex, Multiechelon, Supply Chain Network withUncertain Demands” authored by A. G. Campanur et al., thedesign problem of a four-echelon supply chain is investigated,which is represented as an inventory-location model withuncertain demand and continuous review inventory policy.A mixed integer nonlinear programming model is used toseek the optimal design of the distribution network, and alinearized integer programming model is also used to savecomputational cost.

In order to respond to unpredictable market changes,manufacturing industries are required to reconstruct rapidly.Discrete manufacturing has thus become the mainstreammodel in manufacturing industries owing to its capabilitiesof handling noncontinuity and configurability. In the paperentitled “Optimal Allocation Method of Discrete Manufac-turing Resources for Demand Coordination between Sup-pliers and Customers in a Fuzzy Environment” authoredby W. Xu and Y. Yu, a resource optimization configura-tion approach is presented on the basis of the customers’expectation bias and the suppliers’ profitmaximization underfuzzy environment. A bi-level planning model for an optimalconfiguration of manufacturing resources is established, anda fast nondominated sorting genetic algorithm is used tosolve the model. e validity and feasibility of the model areillustrated by an example and simulations.

Automatic motion planning for industrial robots playsa crucial role in the next-generation manufacturing systemssuch as industrial 4.0. e paper entitled “Maximizing theCoverage of Roadmap Graph for Optimal Motion Planning”authored by J.-H. Park and T.-W. Yoon addresses an optimalalgorithm to optimize a roadmap graph which can coverarbitrary morphologies of the free configuration space withmaximal coverage. e proposed approach is applied to anindustrial robot model, and the obtained roadmap graphis demonstrated to be effective in path-finding tests viasimulation studies.

3. Monitoring and Diagnosis for ComplexIndustrial Systems

Nowadays, industrial systems are becoming more complexand expensive; thus they have higher demand and require-ment on system reliability and safety. As a result, monitoringand diagnosis for complex industrial systems have been apopular research topic during the last decades. Industry 4.0has further stimulated this research and application field.

In system of systems engineering, system integratorsplay key roles in forming compatible and reliable interfacesbetween subsystems. In the paper entitled “Interface DataModeling to Detect and Diagnose Intersystem Faults forDesigning and Integrating System of Systems” authored byK.-M. Seo and K.-P. Park, a systematic solution is proposedto identify and diagnose interface faults during the phase ofthe system design and integration. e presented approachis applied to an underwater shipbuilding system with 10interfaces, and 14 fault scenarios are identified and diagnosed,leading to a reliable submarine construction.

4. Advanced Control for ComplexIndustrial Systems

Singular systems are also called DAE (differential-algebraicequation) systems, descriptor systems, generalized dynamicsystems, and implicit systems, which arise in convenientand natural modelling process. Many engineering systemssuch as electrical engineering systems, mechanical systems,chemical systems, economic systems, and environmental sys-tems can bemodeled as singular systems. Stochastic behaviorin some engineering systems such as Markovian jumpingwould further add the complexity of system dynamics. Inthe paper entitled “Estimation and Synthesis of ReachableSet for Singular Markovian Jump Systems” authored by Y.Ding and H. Lui, reachable set estimation and state-feedbackcontrol are investigated. Sufficient conditions on state/outputreachable sets are developed, and state-feedback controllersare designed correspondingly.

Hysteresis, widely existing in mechanical systems, woulddegrade system performance and even cause system insta-bility. Moreover, stochastic uncertainties usually occur inengineering systems, which have brought more challenges incontrol synthesis. In the paper entitled “Adaptive Fuzzy Con-trol for Stochastic Pure-Feedback Nonlinear Systems withUnknown Hysteresis and External Disturbance” authored byX. Liu et al., the tracking control problem for a class ofstochastic nonlinear systems subjected to unknownhysteresisis investigated. e mean-value theorem, Nussbaum func-tion, Lyapunov function, and back-stepping techniques areused in the analysis and design. An adaptive fuzzy controlleris designed to ensure that the closed-loop system is semi-globally uniformly bounded and the tracking errors convergeto a small neighborhood of the origin. e proposed controlalgorithms are demonstrated by simulation studies.

Vision-based control has been a mainstreaming tech-nique in industrial robotic systems; however, visual feedbackdelays and uncalibrated visual parameterswould limit controlsystem performance, or even damage the system stability.e

Complexity 3

paper entitled “Delay-Dependent Stability in UncalibratedImage-Based Dynamic Visual Servoing Robotic System”authored by T. Li et al. addresses an adaptive control tech-nique to stabilize a robotic system subjected to visual delaysand uncalibrated visual parameters.edelay-dependent sta-bility conditions are also provided to reveal the relationshipbetween the system stability and the delayed time.

Consensus is a fundament issue of the coordinatedcontrol for multiagent systems, which has a wide applicationin mobile robot systems, distributed target tracking, groupdecision making, and so forth. Group consensus is anextension of the conventional consensus issue, which hasbeen paid attention recently. In the paper entitled “Couple-Group Consensus: A Class of Delayed Heterogeneous Mul-tiagent Systems in Competitive Networks” authored by L.Ji et al., couple-group consensus is dealt with for a class ofheterogeneous multiagent systems subjected to both inputand communication delays. A novel distributed coordinationcontrol protocol is addressed to ensure the heterogeneoussystem to achieve couple-group consensus asymptotically.

Distributed engine control would be the next revolutionin turbine engine control systems. Networked engine controlis an intermediate distributed engine control approach, whichhas been paid many attentions. It is evident that transmissiondelays and packet dropout are inevitable during data trans-mission. Moreover, system performance may degrade due tocomponent aging. As a result, how to make the system robustagainst transmission delays, packet dropout, and componentaging is an important issue in network control systems. In thepaper entitled “Networked Control System Design for Tur-bofan Aeroengines with Aging and Deterioration” authoredby R. Li et al., a network control system design approachis proposed for a mixed two-spool turbofan aeroenginewith aging and deterioration so that the system can achieverobustness against dropouts and delays as well as satisfactorydynamic performance against aging and deterioration.

With an increasing demand of top quality and variety ofproducts, modern manufacturing industries are stimulatedto automate their production processes. However, mod-elling and control for automated manufacturing systems arechallenging due to rapid changes in global markets, unex-pected events, and discrete characteristics of both cellularautomata and manufacturing systems. In the paper entitled“Distributed Control of a Manufacturing System with One-Dimensional Cellular Automata” authored by I. Barragan-Vite et al., a distributed controlmodellingmethod is proposedfor an automated manufacturing process. A procedure isprovided to achieve local evolution rule of the automationon the basis of the relationships among the resources andmaterial flow in manufacturing process. e effectiveness ofthe proposed distributed control strategy is illustrated anddemonstrated by case studies.

5. Conclusion

An overview of the 13 selected papers for this special issuehas been presented, reflecting the most recent progress in theresearch field. We hope this special issue can further stim-ulate interest in advanced modelling, monitoring, control,

and optimization for complex industrial systems from bothacademic societies and automation industries. More researchresults and practical applications on complex industrialsystems are expected in the future.

Conflicts of Interest

e guest editor team does not have any conflicts of interestor private agreements with companies.

Acknowledgments

e guest editors would like to express great appreciation toall authors who supported this special issue by contributingpapers. e guest editor team is deeply indebted to allreviewers for their insightful and constructive commentsthat helped shape this special issue. Finally, the guest editorswould like to thank the research supports from the Alexandervon Humboldt Foundation, the NNSFC grant (61673074),and their institutes.

Zhiwei GaoSing Kiong Nguang

De-Xing Kong

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