Hong Wang

Bio: Hong Wang is an academic researcher from Northeastern University (China). The author has contributed to research in topics: Nonlinear system & Probability density function. The author has an hindex of 47, co-authored 510 publications receiving 8952 citations. Previous affiliations of Hong Wang include Zhejiang University & Shenyang Institute of Automation.

Actuator fault diagnosis: an adaptive observer-based technique

Abstract: This paper presents a novel approach for the fault diagnosis of actuators in known deterministic dynamic systems by using an adaptive observer technique. Systems without model uncertainty are initially considered, followed by a discussion of a general situation where the system is subjected to either model uncertainty or external disturbance. Under the assumption that the system state observer can be designed such that the observation error is strictly positive real (SPR), an adaptive diagnostic algorithm is developed to diagnose the fault, and a modified version is proposed for the general system to improve robustness. The method is demonstrated through its application to a simulated second-order system.

A direct adaptive neural-network control for unknown nonlinear systems and its application

Abstract: In this paper a direct adaptive neural-network control strategy for unknown nonlinear systems is presented. The system considered is described by an unknown NARMA model, and a feedforward neural network is used to learn the system. Taking the neural network as a neural model of the system, control signals are directly obtained by minimizing either the instant difference or the cumulative differences between a set point and the output of the neural model. Since the training algorithm guarantees that the output of the neural model approaches that of the actual system, it is shown that the control signals obtained can also make the real system output close to the set point. An application to a flow-rate control system is included to demonstrate the applicability of the proposed method and desired results are obtained.

Bounded Dynamic Stochastic Systems: Modelling and Control

Abstract: 1 Preliminaries- 11 Introduction- 12 An example: flocculation model- 13 The aim of the new development- 14 The structure of the book- 15 Random variables and stochastic processes- 151 Random variables and their distribution functions- 152 Mean and variance- 153 Random vector- 154 Conditional mean- 16 Stochastic processes- 17 Some typical distributions- 171 Gaussian distribution- 172 Uniform distribution- 173 ? distribution- 18 Conclusions- 2 Control of SISO Stochastic Systems: A Fundamental Control Law- 21 Introduction- 22 Preliminaries on B-splines artificial neural networks- 23 Model representation- 231 Static models- 232 Dynamic models- 24 System modelling and parameter estimation- 241 Modelling of static systems- 242 Modelling of linear dynamic systems- 25 Control algorithm design- 251 Control algorithm for static systems- 252 Control algorithm for linear dynamic systems- 253 Constraints on input energy for dynamic systems- 26 Discussions- 261 Adaptive control- 262 Modelling and control of time delay systems- 263 On-line measurement of Vk- 264 Controllability, observability and stability- 27 Examples- 271 Static system modelling- 272 A design example for dynamic systems- 28 Conclusions- 3 Control of MIMO Stochastic Systems: Robustness and Stability- 31 Introductionx- 32 Model representation- 321 State space form- 322 The input-output form- 33 The controller using V(k)- 331 Measurement of V(k)- 332 Feedback control using V(k)- 333 Stability issues- 34 The controller using f(y, U(k))- 341 The formulation of control algorithm- 342 Stability issues- 35 An illustrative example- 351 Control algorithm design- 352 Simulation results- 36 Conclusions and discussions- 4 Realization of Perfect Tracking- 41 Introduction- 42 Preliminaries and model representation- 43 Main result- 44 Simulation results- 441 Controller design- 442 Simulation results- 45 An LQR based algorithm- 46 Conclusions- 5 Stable Adaptive Control of Stochastic Distributions- 51 Introduction- 52 Model representation- 53 On-line estimation and its convergence- 54 Adaptive control algorithm design- 55 Stability analysis- 56 A simulated example- 57 Conclusions- 6 Model Reference Adaptive Control- 61 Introduction- 62 Model representation- 63 An adaptive controller design- 631 Construction of the reference model- 632 Construction of error dynamics- 64 Adaptive tuning rules for K(t) and Q(t)- 65 Robust adaptive control scheme- 651 Control scheme when ?(t) ? 0- 652 Control scheme when both e0 and ? are present- 66 A case study- 67 Conclusions and discussions- 7 Control of Nonlinear Stochastic Systems- 71 Introduction- 72 Model representation- 73 Control algorithm design- 74 Stability issues- 75 A neural network approach- 751 Training of the neural networks- 752 A linearised control algorithm- 76 Two examples- 77 Calculation of ?- 78 Conclusions- 8 Application to Fault Detection- 81 Introduction- 82 Model representation- 83 Fault detection- 831 Fault detection for static systems- 832 Dynamic systems- 833 Fault detection signal- 84 An adaptive diagnostic observer- 85 Discussions- 86 An identification based FDD- 87 Fault diagnosis- 871 The algorithm- 872 An applicability study- 88 Discussions and conclusions- 9 Advanced Topics- 91 Introduction- 92 Square root models- 93 Control algorithm design- 931 Finding weights from ?(y, u(k))- 932 The control algorithm- 94 Simulations- 95 Continuous-time models- 96 The control algorithm- 97 Control of the mean and variance- 971 The control of output mean value- 972 The control of output variance- 98 Singular stochastic systems- 981 Model representation- 982 Control algorithm design- 99 Pseudo ARMAX systems- 910 Filtering issues- 911 Conclusions- References

Differential Evolution: A Survey of the State-of-the-Art

Abstract: Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

Robust Model-Based Fault Diagnosis for Dynamic Systems

Abstract: There is an increasing demand for dynamic systems to become safer and more reliable This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital It is clear that fault diagnosis is becoming an important subject in modern control theory and practice Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework It contains many important topics and methods; however, total coverage and completeness is not the primary concern The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications The first two chapters are of tutorial value and provide a starting point for newcomers to this field The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications This will certainly appeal to experts in this field Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed Although this is a research monograph, it will be an important text for postgraduate research students world-wide The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world

European Position Paper on Rhinosinusitis and Nasal Polyps 2020

Abstract: The European Position Paper on Rhinosinusitis and Nasal Polyps 2020 is the update of similar evidence based position papers published in 2005 and 2007 and 2012. The core objective of the EPOS2020 guideline is to provide revised, up-to-date and clear evidence-based recommendations and integrated care pathways in ARS and CRS. EPOS2020 provides an update on the literature published and studies undertaken in the eight years since the EPOS2012 position paper was published and addresses areas not extensively covered in EPOS2012 such as paediatric CRS and sinus surgery. EPOS2020 also involves new stakeholders, including pharmacists and patients, and addresses new target users who have become more involved in the management and treatment of rhinosinusitis since the publication of the last EPOS document, including pharmacists, nurses, specialised care givers and indeed patients themselves, who employ increasing self-management of their condition using over the counter treatments. The document provides suggestions for future research in this area and offers updated guidance for definitions and outcome measurements in research in different settings. EPOS2020 contains chapters on definitions and classification where we have defined a large number of terms and indicated preferred terms. A new classification of CRS into primary and secondary CRS and further division into localized and diffuse disease, based on anatomic distribution is proposed. There are extensive chapters on epidemiology and predisposing factors, inflammatory mechanisms, (differential) diagnosis of facial pain, allergic rhinitis, genetics, cystic fibrosis, aspirin exacerbated respiratory disease, immunodeficiencies, allergic fungal rhinosinusitis and the relationship between upper and lower airways. The chapters on paediatric acute and chronic rhinosinusitis are totally rewritten. All available evidence for the management of acute rhinosinusitis and chronic rhinosinusitis with or without nasal polyps in adults and children is systematically reviewed and integrated care pathways based on the evidence are proposed. Despite considerable increases in the amount of quality publications in recent years, a large number of practical clinical questions remain. It was agreed that the best way to address these was to conduct a Delphi exercise . The results have been integrated into the respective sections. Last but not least, advice for patients and pharmacists and a new list of research needs are included. The full document can be downloaded for free on the website of this journal: http://www.rhinologyjournal.com.