Special Issue "Optimization, Health Monitoring and Control Methods for Modern Complex Systems"

A special issue of Designs (ISSN 2411-9660).

Deadline for manuscript submissions: 10 November 2018

Special Issue Editors

Guest Editor
Prof. Dr. Hamid Reza Karimi

Department of Mechanical Engineering, Politecnico di Milano, via La Masa 1, 20156 Milan, Italy
Website | E-Mail
Interests: control systems; mechatronics; applied mechanics; wind energy; robotics; manufacturing automation; intelligent computation
Guest Editor
Prof. Dr. Shen Yin

School of Astronautics, Harbin Institute of Technology, Harbin, China
Website | E-Mail
Interests: fault diagnosis and performance monitoring; data analysis and data mining; intelligent systems and fuzzy control; big data and its industrial applications
Guest Editor
Prof. Dr. Peng Shi

FIEEE, FIET, School of Electrical and Electronic Engineering, The University of Adelaide, SA 5005, Australia
Website | E-Mail
Interests: systems and Control; intelligent Systems; hybrid systems

Special Issue Information

Dear Colleagues,

In the last few decades, there has been increased attention to the problem of high level of automation for modern complex industrial systems with more focus on reliability, safety as well as economic performance of these systems. To this aim, literature of the work has witnessed the rapid growth of research developments on modeling, control, filtering, diagnostics and prognostics in modern industrial processes, thus become more important for both academic and industrial domains.

The primary objective of this Special Issue is to provide a forum for researchers and practitioners to exchange their latest achievements and to identify critical issues and challenges for future investigation on mathematical or data-driven modeling, optimization techniques, health monitoring, diagnostics and control design of modern complex systems. The papers to be published in the issue are expected to provide the latest results in advanced techniques especially for large-scale process industry and advanced complex electric/mechatronic/aerospace systems.

Prof. Dr. Hamid Reza Karimi
Prof. Dr. Shen Yin
Prof. Dr. Peng Shi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Designs is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Mathematical modeling and identification for modern complex systems
  • Robust control and filtering issues for modern complex systems
  • Optimization theory for controller and observer design for modern complex systems
  • Data-driven modeling techniques for modern complex systems
  • Fault diagnosis, prognosis and health monitoring system design
  • Deep learning techniques and big data solutions with complex system applications
  • Intelligence techniques, such as fuzzy logic, neural network approaches
  • Simulation technology for complex systems
  • Industrial applications

Published Papers (6 papers)

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Research

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Open AccessFeature PaperArticle Extraction of Structural System Designs from Topologies via Morphological Analysis and Artificial Intelligence
Received: 25 January 2018 / Revised: 8 February 2018 / Accepted: 9 February 2018 / Published: 13 February 2018
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Abstract
Structural system design is the process of giving form to a set of interconnected components subjected to loads and design constraints while navigating a complex design space. While safe designs are relatively easy to develop, optimal designs are not. Modern computational optimization approaches
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Structural system design is the process of giving form to a set of interconnected components subjected to loads and design constraints while navigating a complex design space. While safe designs are relatively easy to develop, optimal designs are not. Modern computational optimization approaches employ population based metaheuristic algorithms to overcome challenges with the system design optimization landscape. However, the choice of the initial population, or ground structure, can have an outsized impact on the resulting optimization. This paper presents a new method of generating such ground structures, using a combination of topology optimization (TO) and a novel system extraction algorithm. Since TO generates monolithic structures, rather than systems, its use for structural system design and optimization has been limited. In this paper, truss systems are extracted from topologies through morphological analysis and artificial intelligence techniques. This algorithm, and its assessment, constitutes the key contribution of this paper. The structural systems obtained are compared with ground truth solutions to evaluate the performance of the algorithms. The generated structures are also compared against benchmark designs from the literature. The results indicate that the presented truss generation algorithm produces structures comparable to those generated through metaheuristic optimization, while mitigating the need for assumptions about initial ground structures. Full article
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Open AccessArticle A Controller Design Based on Takagi-Sugeno Fuzzy Model Employing Trajectory of Partial Uncertainty
Received: 22 December 2017 / Revised: 6 February 2018 / Accepted: 9 February 2018 / Published: 13 February 2018
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Abstract
When the Takagi–Sugeno (T-S) fuzzy model is used to design controllers for a concerned system, the discrepancy between the system and its T-S fuzzy model becomes crucial sometimes in terms of control performance, particularly in cases when the magnitude of the discrepancy is
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When the Takagi–Sugeno (T-S) fuzzy model is used to design controllers for a concerned system, the discrepancy between the system and its T-S fuzzy model becomes crucial sometimes in terms of control performance, particularly in cases when the magnitude of the discrepancy is relatively large. While most existing works have focused on approaches to restrain the influence of the discrepancy, the idea used in this paper is to extract as much information from the discrepancy as possible at first and then use it in the controller design before restraining its influence. By doing so, the magnitude of the discrepancy is reduced accordingly, and thus, better control performance can be expected. Including the discrepancy and other uncertain elements like the inner parameters’ perturbation, a term called uncertainty is considered in this paper. Assuming that the uncertainty influences the system behavior through the state and control input, an observer able to catch the trajectory of the partial uncertainty related to the control input is proposed. Then, a controller employing the trajectory is suggested. All design parameters are obtained by solving certain linear matrix inequalities, which guarantees the system stability. Finally, simulations are provided to illustrate the effectiveness of the proposed approach. Full article
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Open AccessArticle Design of Distributed Multi-Actuator Systems with Incomplete State Information for Vibration Control of Large Structures
Received: 18 December 2017 / Revised: 19 January 2018 / Accepted: 8 February 2018 / Published: 11 February 2018
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Abstract
In this paper, we investigate the design and performance of static feedback controllers with partial-state information for the seismic protection of tall buildings equipped with incomplete multi-actuation systems. The proposed approach considers a partially instrumented multi-story building with an incomplete system of interstory
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In this paper, we investigate the design and performance of static feedback controllers with partial-state information for the seismic protection of tall buildings equipped with incomplete multi-actuation systems. The proposed approach considers a partially instrumented multi-story building with an incomplete system of interstory force–actuation devices implemented on selected levels of the building, and an associated set of collocated sensors that measure the corresponding interstory drifts and interstory velocities. The main elements of the proposed controller design methodology are presented by means of a twenty-story building equipped with a system of ten interstory actuators arranged in three different layouts: concentrated, semi-distributed and fully-distributed. For these control configurations, partial-state controllers are designed following a static output-feedback H-infinity controller design approach, and the corresponding frequency and time responses are investigated. The obtained results clearly indicate that the proposed partial-state controllers are effective in mitigating the building seismic response. They also show that a suitable distribution of the instrumented stories is a relevant factor in the control system performance. Full article
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Open AccessFeature PaperArticle Adaptive Sliding Mode Control for High-Frequency Sampled-Data Systems with Actuator Faults
Received: 14 December 2017 / Revised: 11 January 2018 / Accepted: 11 January 2018 / Published: 17 January 2018
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Abstract
This paper investigates the sliding mode control for high-frequency sampled-data systems with actuator faults. Besides matched nonlinearity, this paper also considers unmeasurable states and unknown actuator degradation ratio as important factors of the overall system. The estimates of system state vector are obtained
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This paper investigates the sliding mode control for high-frequency sampled-data systems with actuator faults. Besides matched nonlinearity, this paper also considers unmeasurable states and unknown actuator degradation ratio as important factors of the overall system. The estimates of system state vector are obtained by an adaptive sliding mode observer method firstly. Then, a novel integral-type sliding surface, corresponding to the unified closed-loop delta operator system, is provided based on aforementioned estimation values, and the fault closed-loop system is proven to be stable by the proposed sliding mode control law. Finally, the fault-tolerant control theory is verified to be valid via a practical simulation example. Full article
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Open AccessArticle A New Observer Design for Fuzzy Bilinear Systems with Unknown Inputs
Received: 3 September 2017 / Revised: 16 November 2017 / Accepted: 16 November 2017 / Published: 21 November 2017
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Abstract
An observer design for a class of nonlinear systems with unknown inputs is considered. Takagi–Sugeno fuzzy bilinear systems represent a wide class of nonlinear systems, and these systems with unknown inputs are an ideal model for many physical systems. For such systems, a
[...] Read more.
An observer design for a class of nonlinear systems with unknown inputs is considered. Takagi–Sugeno fuzzy bilinear systems represent a wide class of nonlinear systems, and these systems with unknown inputs are an ideal model for many physical systems. For such systems, a design method for obtaining an observer that estimates the state of the system is proposed. A parallel distributed observer (PDO), which is constructed with local linear observers and the appropriate grade of the membership functions, is a conventional observer for Takagi–Sugeno fuzzy bilinear systems. However, it is known that its design conditions have conservativeness. In this paper, to reduce the conservatism in the design conditions, non-PDO with new design conditions is proposed. Our design conditions are derived from a multiple Lyapunov function, which depends on the membership function with time-delay in the premise variables. This method eventually reduces the conservatism and enables us to construct an observer for a wide class of nonlinear systems. When the premise variables are the state variables that are not measurable, Takagi–Sugeno fuzzy bilinear systems can represent a wider class of nonlinear systems. Hence, an observer design for fuzzy bilinear systems with unmeasurable premise variables is also proposed. Finally, numerical examples are given to illustrate our design methods. Full article
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Review

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Open AccessReview Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis
Received: 2 February 2018 / Revised: 22 April 2018 / Accepted: 23 April 2018 / Published: 9 May 2018
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Abstract
Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society. The early diagnosis of BC can
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Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown. Full article
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