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In conditions of above rated wind speed, the power output of a wind turbine should be maintained at rated power to prevent overheating of generators and power electronics systems. Furthermore, extreme wind conditions, such as wind gusts, can even lead to shut-downs. In this paper, a novel L 1 adaptive controller is designed for blade pitch control of wind energy conversion systems (WECS) to achieve stable output power and generator speed, in the presence of turbulent wind conditions. Firstly, the pitch regulated variable-speed wind turbine is modeled as a non-affine nonlinear system with uncertainties. Subsequently, the L 1 adaptive controller is introduced, which consists of three elements: state predictor, adaptive law, and control law. Without the requirement of exact system dynamics and wind speed measurement, uniformly bounded transient power response can be achieved. Compared with traditional methods adopted in industries, L 1 adaptive controller is more robust and can achieve better transient control performance. The feasibility of the proposed scheme is verified via extensive simulation studies on a professional GH Bladed software package. • A novel L 1 adaptive pitch angle controller is proposed for WECS. • The proposed controller does not need system dynamics and wind speed information. • It achieves better stability and robustness in both generator speed and power output. • Both transient and steady-state performance of the closed-loop system is guaranteed. • Extensive simulations on professional tools are done to testify the effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, a miniature methanol fuel processor and its controller design is introduced for onboard hydrogen production. The hydrogen is generated via autothermal reforming of methanol. The control scheme consists of a hydrogen flow rate controller and a reforming temperature controller. To deal with uncertain system dynamics and external disturbance, an adaptive sliding mode control algorithm is adopted as the hydrogen flow rate controller for regulating hydrogen flow rate by manipulating methanol flow rate. Additionally, a high-gain observer is implemented to estimate the unmeasurable system state. The stability of closed-loop system is guaranteed by standard Lyapunov analysis. Furthermore, a variable ratio control law is employed as the reforming temperature controller to achieve steady reforming temperature by adjusting the reforming air flow rate. Finally, the effectiveness of the entire system is testified by experimental means. [ABSTRACT FROM AUTHOR]

Highlights: [•] A relative principal component of fault reconstruction (RPCFR) modeling algorithm is proposed in the present work for online fault diagnosis. [•] The algorithm gives the original fault space a further decomposition according to their relationships with the fault-free process information. [•] Fault reconstruction model is developed only using those fault directions that can best characterize the effect of the fault deviations relative to normal data. [•] The proposed method provides comprehensive analysis of fault information from the reconstruction perspective, which can improve the diagnosis efficiency. [Copyright &y& Elsevier]

Abstract: In the present work, a new subspace decomposition approach of fault deviations is developed in the context of principal component analysis (PCA) based monitoring system for fault diagnosis via reconstruction. The fault effects are decomposed in different monitoring subspaces, principal subspace (PCS) and residual subspace (RS), and the significant fault deviations that are responsible for the concerned alarming monitoring statistic are calculated. This is achieved by designing a two-step feature decomposition procedure in each monitoring subspace. In the first step, the relative fault deviations are sorted by comparing the fault variations with the normal variations. All possible fault deviations that may contribute to the out-of-control monitoring statistics are collected. In the second step, PCA is performed on the chosen fault information where the largest fault deviation directions are decomposed in order. By the two-step decomposition, in each monitoring subspace, two different parts are separated for the purpose of fault reconstruction. One is composed of the concerned fault deviations that contribute to alarming monitoring statistics which are thus significant to remove the out-of-control signals. The other is composed of general variations that are deemed to follow normal rules and thus insignificant to remove alarming monitoring statistics. Theoretical support is framed and the related statistical characteristics are analyzed. Its feasibility and performance are illustrated with data from the three-tank system and the Tennessee Eastman (TE) benchmark process. [Copyright &y& Elsevier]

Abstract: Wireless Sensor Network (WSN) is increasingly popular in the field of micro-environmental monitoring due to its promising capability. However, most systems using WSN for environmental monitoring reported in the literature are developed for specific applications without functions for exploiting user’s data processing methods. In this paper, a new system is designed in detail to perform micro-environmental monitoring taking the advantages of the WSN. The application-oriented hardware working style is designed, and the system platform for data acquisition, validation, processing and visualization is systematically presented. Several strategies are proposed to guarantee the system capability in terms of extracting useful information, visualizing events to their authentic time are also described. Moreover, a web-based surveillance subsystem is presented for remote control and monitoring. In addition, the system is extensible for engineers to carry their own data analysis algorithms. Experimental results are to show the path reliability and real-time characteristics, and to display the feasibility and applicability of the developed system into practical deployment. [Copyright &y& Elsevier]

Due to the characteristics of non-linearity, large time delay, time-variety, high-dimension and distributed parameters of blast furnace smelting process, it brought great difficulties to the detection and diagnosis of abnormal furnace conditions. Although there have been some researches on the fault detection of blast furnace smelting process, works concerning on the fault classification are still limited. And there is no exploration to deal with it based on hidden Markov model (HMM). Taking into account the inherent uncertainty and dynamics of the blast furnace smelting process, this article proposes a novel iron-making process monitoring approach based on effective variable selection and moving window HMM (VS-MVHMM). First, considering that not all variables are beneficial to fault identification, a variable selection method based on the coefficient of variation is used. Second, instead of just considering the posterior probability of one single sample, moving window is introduced to utilize the dependency of samples for improving the accuracy of on-line fault identification. Besides, a MVHMM-based threshold statistic is defined to identify the unknown fault in iron-making process. And various known modes are separated based on Viterbi algorithm. The effectiveness of the proposed approach is first validated by a numerical simulation example and Tennessee Eastman (TE) simulation platform. Then it is tested by the real data, which is generated in the No. 2 blast furnace of Liuzhou Iron and Steel Group Corporation. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive fault tolerant controller design is proposed for a class of nonlinear unknown systems with multiple actuators. The controller consists of an adaptive learning-based control law, a Nussbaum gain, and a switching function scheme. The adaptive control law is implemented by a two-layer neural network to accommodate the unknown system dynamics. Without the requirement of additional fault detection mechanism, the switching function is designed to automatically locate and turn off the unknown faulty actuators by observing a control performance index. The asymptotic stability of the system output in the presence of actuator failures is rigidly proved through standard Lyapunov approach, while the other signals of the closed-loop system are guaranteed to be bounded. The theoretical result is substantiated by simulation on a two-tank system. [ABSTRACT FROM AUTHOR]

Abstract: This paper is concerned with the delay-dependent stability and robust stability criteria for linear systems with time-varying delay and norm-bounded uncertainties. Through constructing a general form of Lyapunov–Krasovskii functional, and using integral inequalities, some slack matrices and newly established convex combination condition in the calculation, the delay-dependent stability criteria are derived in terms of linear matrix inequalities. Numerical examples are given to illustrate the improvement on the conservatism of the delay bound over some reported results in the literature. [Copyright &y& Elsevier]

Abstract: This paper deals with the strong – stabilization problem. A new bounded real lemma is provided by adding two slack variables, which provides extra freedom to include more control constraints in handling robust control problems. With the extended bounded real lemma, a new sufficient condition is proposed for th order strong – stabilization. It is also proved that the design of an th order () strong – controller can be transformed into that of an th order controller for an associated plant, which tackles the possible order problem. Furthermore, the stable stabilization and strong stabilization problems can also be solved by relaxing certain constraints accordingly. Despite the BMI constraints, the path-following and alternative iteration methods are adopted to propose procedures for both th and th order controller designs. The effectiveness is indicated by several numerical examples. [Copyright &y& Elsevier]

Abstract: Identifying and validating novel phenotypes from images inputting online is a major challenge against high-content RNA interference (RNAi) screening. Newly discovered phenotypes should be visually distinct from existing ones and make biological sense. An online phenotype discovery method featuring adaptive phenotype modeling and iterative cluster merging using improved gap statistics is proposed. Clustering results based on compactness criteria and Gaussian mixture models (GMM) for existing phenotypes iteratively modify each other by multiple hypothesis test and model optimization based on minimum classification error (MCE). The method works well on discovering new phenotypes adaptively when applied to both of synthetic datasets and RNAi high content screen (HCS) images with ground truth labels. [Copyright &y& Elsevier]

Fault diagnosis is always a crucial and challenging technology in industry, which contains huge amount of variables need to be measured and analyzed. A high-efficiency fault diagnosis method can reduce the economic loss drastically. This paper presents a new fault diagnosis method based on relevance vector machine (RVM) to deal with the small sample data. Particle swam optimization (PSO) algorithm and differential evolution (DE) algorithm are employed together to optimize the parameters of RVM, which strengthen the classification ability and provide strong potential in prediction fault. Then, a novel fault diagnosis method DEPSO–RVM is obtained, which has the advantages of high accuracy and low false positive rate. Furthermore, the proposed method DEPSO–RVM is accomplished with two representative test data TE process and ethylene cracking furnace process. Besides, in order to compare the performance of DEPSO–RVM, SVM, DEPSO–SVM and PSO–RVM are applied to Tennessee Eastman process and ethylene cracking furnace process as well as the basis of comparison. The results show the validity of the proposed method on TE process and ethylene cracking furnace process and give a reference on industry process. [ABSTRACT FROM AUTHOR]

This study presents a model-based optimization strategy for an actual chiller driven dehumidifier of liquid desiccant dehumidification system operating with lithium chloride solution. By analyzing the characteristics of the components, energy predictive models for the components in the dehumidifier are developed. To minimize the energy usage while maintaining the outlet air conditions at the pre-specified set-points, an optimization problem is formulated with an objective function, the constraints of mechanical limitations and components interactions. Model-based optimization strategy using genetic algorithm is proposed to obtain the optimal set-points for desiccant solution temperature and flow rate, to minimize the energy usage in the dehumidifier. Experimental studies on an actual system are carried out to compare energy consumption between the proposed optimization and the conventional strategies. The results demonstrate that energy consumption using the proposed optimization strategy can be reduced by 12.2% in the dehumidifier operation. [ABSTRACT FROM AUTHOR]

Abstract: This brief investigates the adaptive neural network (NN) control of a class of high-order nonaffine nonlinear systems with completely unknown dynamics. Since the control terms appear within the unknown nonlinearity, traditional control schemes and stability analysis are usually rendered extremely complicated. Our main contribution includes a novel system transformation that converts the nonaffine system into an affine system through a combination of a low-pass filter and state transformation. As a result, the state-feedback control of the nonaffine system can be viewed as the output-feedback control of an affine system in normal form. The transformed system becomes linear with respect to the new input while the traditional backstepping approach is not needed thus allowing the synthesis to be extremely simplified. It is theoretically proven that all the signals in the closed-loop system are uniformly ultimately bounded (UUB). Simulation results are provided to demonstrate the performance of the developed controller. [Copyright &y& Elsevier]

Highlights: [•] Study the heat and mass transfer process in dehumidifier for LDDS. [•] A simplified yet accurate model for real-time performance optimization is developed. [•] The model requires no iterative computations and is easy for engineering application. [Copyright &y& Elsevier]

Abstract: Over the past few years, fault detection for computer networks has attracted extensive attentions for its importance in network management. Most existing fault detection methods are based on active probing techniques which can detect the occurrence of faults fast and precisely. But these methods suffer from the limitation of traffic overhead, especially in large scale networks. To relieve traffic overhead induced by active probing based methods, a new fault detection method, whose key is to divide the detection process into multiple stages, is proposed in this paper. During each stage, only a small region of the network is detected by using a small set of probes. Meanwhile, it also ensures that the entire network can be covered after multiple detection stages. This method can guarantee that the traffic used by probes during each detection stage is small sufficiently so that the network can operate without severe disturbance from probes. Several simulation results verify the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: The wireless sensor/actuator networks (WSANs) can be used for spatially distributed control systems. With smart sensors and actuators, the WSANs are able to not only sense the control system states and report measurements, but also perform control and actuation. This paper investigates WSANs on their ability of control. A centralized controller is introduced into WSANs to make up closed-loop control systems, in which control decisions are made based on global network-wide information. A model of the control and communication over WSANs is made theoretically, based on which we achieved an optimal control method. It is demonstrated by simulations that the control method proposed could stabilize the control system quickly. [Copyright &y& Elsevier]

• Adaptive generic model control based on extended least square estimation is employed to control ITCASC. • State estimation is realized through the MRELS to derive the controller adaptation. • The AGMC can effectively compensate for a structural mismatch. • The tracking of the product concentration trajectory is fast in the AGMC. • The effect of the decrease and increase change in the process control setpoint is analyzed. A gasification process is key in clean electricity production and decarbonization in an integrated gasification combined cycle (IGCC) plant. The quality of the gasification process depends on the stability of the product-concentration streamed from the internal thermally coupled air separation column (ITCASC) unit. There is a need for a stable product-concentration supply to sustain clean electricity production. A proportional-integral-derivative control (PID) control and a nonlinear internal model control (NIMC) scheme are first explored to control the IGCC-ITCASC. Moreover, a robust generic model control (RGMC) is employed to deal with the IGCC-ITCASC inherent nonlinearity and strong interaction that make it very difficult to control. To obtain a satisfactory control performance, an adaptive generic model control (AGMC) with a multivariable recursive estimation is developed. The compared control systems show that AGMC can better handle both the system nonlinearity and strong interaction while improving the control performance. [ABSTRACT FROM AUTHOR]