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This paper presents a disturbance observer-based robust backstepping load-following control (DO-RBLFC) scheme for modular high-temperature gas-cooled reactors (MHTGRs) in the presence of actuator saturation and disturbances. Based on reactor kinetics and temperature reactivity feedback, the mathematical model of the MHTGR is first established. After that, a DO is constructed to estimate the unknown compound disturbances including model uncertainties, external disturbances, and unmeasured states. Besides, the actuator saturation is compensated by employing an auxiliary function in this paper. With the help of the DO, a robust load-following controller is developed via the backstepping technique to improve the load-following performance of the MHTGR subject to disturbances. At last, simulation and comparison results verify that the proposed DO-RBLFC scheme offers higher load-following accuracy, better disturbances rejection capability, and lower control rod speed than a PID controller, a conventional backstepping controller, and a disturbance observer-based adaptive sliding mode controller.

Waterjet propulsion is increasingly used in both combat and civil ships. In this study, a mechanism analysis-based modelling approach of a dual-waterjets propulsion steering system is presented. Experiments carried out on an experimental platform are used to validate the model system. Then a model-based synchronous control strategy for the dual-waterjets propulsion steering system is developed. The experiment results show that the model-based proportional–integral synchronous control strategy may reduce the synchronization error significantly compared with the reciprocal of the synchronization error driving synchronous control strategy practised on some small ships.

It is well known that the model of nuclear reactors features natural nonlinearity, and variable parameters during power tracking operation. In this paper, a disturbance observer-based adaptive sliding mode control (DOB-ASMC) strategy is proposed for power tracking of the pressurized-water reactor (PWR) in the presence of lumped disturbances. The nuclear reactor model is firstly established based on point-reactor kinetics equations with six delayed neutron groups. Then, a new sliding mode disturbance observer is designed to estimate the lumped disturbance, and its stability is discussed. On the basis of the developed DOB, an adaptive sliding mode control scheme is proposed, which is a combination of backstepping technique and integral sliding mode control approach. In addition, an adaptive law is introduced to enhance the robustness of a PWR with disturbances. The asymptotic stability of the overall control system is verified by Lyapunov stability theory. Simulation results are provided to demonstrate that the proposed DOB-ASMC strategy has better power tracking performance than conventional sliding mode controller and PID control method as well as conventional backstepping controller.

The uncertainty and disturbance estimator (UDE)-based robust control is extensively investigated for its merits of strong robustness and straightforward principle. However, the structural constrain...

Grid voltage parameters, including the amplitude, frequency and phase angle, are the essential information for the control and protection of power systems. The sinusoid-locked loop (SLL) is a novel approach to estimate the grid voltage parameters, which achieves accurate estimation and strong robustness in the presence of harmonic disturbances. However, the conventional SLL is sensitive to large voltage variations or computation errors, which may fail to achieve accurate estimation when the controller is trapped in the undesired equilibrium. In this paper, a modified sinusoid-locked loop (MSLL) is designed to improve the estimation performance with respect to large voltage variations. A virtual stator impedance is introduced to eliminate the undesired equilibrium, which mimics the armature reaction of a virtual synchronous machine (VSM). In addition, the closed-loop stability is investigated based on the small-signal model of the proposed MSLL. Following the idea of the VSM, the output voltage of the VSM is locked with the input grid voltage when the real power and reactive power are both driven to zero. Simulation and experimental results are presented to illustrate the effectiveness of the proposed MSLL, which achieves excellent estimation performance in the presence of large voltage variations and harmonic disturbances.

The boiler-turbine system (BTS) is usually subject to the tight input constraint, the strong nonlinearity and the complex disturbance, which makes the control a challenging task To this end, a disturbance observer based fuzzy model predictive control (DOBFMPC) scheme is proposed for the BTS in this paper. The generalized discrete-time nonlinear disturbance observer (GDNDO) is first developed to estimate the higher-order disturbance by systematically extending the conventional nonlinear disturbance observer. The GDNDO exhibits a series structure of the internal states, and can precisely estimate the disturbance if its order is equal to or greater than that of the disturbance In addition, a baseline fuzzy model predictive control (FMPC) law is synthesized on the fuzzy model. With FMPC, the asymptotic stability is guaranteed, and meanwhile the input constraints are satisfied by both the free control variables and the future control inputs in the form of the state feedback law. At last, the disturbance estimate and the FMPC are applied to constitute the DOBFMPC law. With the proper design of the disturbance compensation gain, the disturbance influence is removed from the output channels by the composite DOBFMPC law at the steady state. Simulations for a 300 MW subcritical BTS well demonstrate the effectiveness of the proposed control scheme.

This paper proposes a disturbance-observer-based fuzzy model predictive control (DOBFMPC) scheme for the nonlinear process subject to disturbances and input constraints. The proposed control scheme is composed of the baseline fuzzy model predictive control (FMPC) law designed on the Takagi–Sugeno fuzzy model and the disturbance compensation law. To build a fuzzy model of appropriate complexity and accuracy for the nonlinear process model, a systematic approach is developed via the gap metric to determine the linearization points. With FMPC, the asymptotic stability is theoretically proved, and the input constraints are satisfied by both the free control variables and the future control inputs in the form of the state feedback law. The disturbance compensation gain is designed such that the influence of the disturbance is removed from the output channels by the composite DOBFMPC law at the steady state. The application to a subcritical boiler–turbine system demonstrate the effectiveness of the proposed control scheme.

The uncertainty and disturbance estimator (UDE)-based robust control approach is widely investigated and applied due to its excellent performance and simple implementation. However, its stabilisability of unstable systems has not been documented. In this study, the sufficient and necessary stabilisability conditions of the UDE-based robust control are investigated. According to the stabilisability conditions, a systematical design method is presented for the reference model based on the controllable canonical transformation and pole placement. This is then applied to a magnetic levitation system (MagLev) subject to model uncertainties and external disturbance as an example. Simulation results are presented to illustrate the effectiveness of the proposed control approach with respect to matched uncertainties.

Grid voltage parameters are essential for the real-time control and protection in power systems. Current approaches mainly focus on the phase-locked loop methods, but which are slow and sensitive to voltage variations and disturbances. In this paper, the estimation of grid voltage parameters is recast as an observer design problem. A second-order sliding mode observer (SMO) is designed to estimate the grid voltage parameters based on the exosystem model. Moreover, the asymptotic stability of the closed-loop SMO is proved. Simulation and experimental results are presented to illustrate the effectiveness of the proposed approach, which achieves fast response and accurate estimation

The DC–DC power converter plays an important role in solar systems to provide the stable DC bus voltage, but which is easily subject to various uncertainties and disturbances in practical operation. In this paper, an uncertainty and disturbance estimator (UDE) based sliding mode control approach is applied to improve the performance of power converters. The UDE is designed for the estimation of both the matched and mismatched uncertainties. To address the mismatched uncertainties, an adaptive sliding mode function is constructed with the compensation of the estimated uncertainties, which renders a chattering-free robust control law. Simulation and experimental results illustrate that the proposed control scheme achieves good dynamic performance, strong robustness and chattering reduction in the presence of uncertainties and disturbances

Maneuverability is one of the most important sailing performances of vessels. In this article, a motion control model with three degrees of freedom (DoFs) for twin-waterjet propulsion vessel is proposed. The model is developed on the basis of maneuvering model group (MMG) maneuverability equations. A simulation environment is constructed on the Matlab Simulink platform. Standard turning tests and zig-zag tests are simulated by solving the motion control model, and the corresponding maneuverability parameters are calculated. Simulation results demonstrate that the maneuverability parameters are in accordance with the vessel maneuverability standard. The approach may be applied to the rapid prediction of vessel maneuverability.

As higher requirements are proposed for the load regulation and efficiency enhancement, the control performance of boiler-turbine systems has become much more important. In this paper, a novel robust control approach is proposed to improve the coordinated control performance for subcritical boiler-turbine units. To capture the key features of the boiler-turbine system, a nonlinear control-oriented model is established and validated with the history operation data of a 300 MW unit. To achieve system linearization and decoupling, an adaptive feedback linearization strategy is proposed, which could asymptotically eliminate the linearization error caused by the model uncertainties. Based on the linearized boiler-turbine system, a second-order sliding mode controller is designed with the super-twisting algorithm. Moreover, the closed-loop system is proved robustly stable with respect to uncertainties and disturbances. Simulation results are presented to illustrate the effectiveness of the proposed control scheme, which achieves excellent tracking performance, strong robustness and chattering reduction.

The load following of nuclear power plants (NPPs) has been a contentious issue in the control field. In this paper, we propose a chattering-free higher order sliding mode control scheme with a high-gain observer for the load following of a pressurized water reactor (PWR) in the presence of lumped disturbances owing to model uncertainties and external disturbances. The mathematical model of the PWR system is first set up in the form of an affine nonlinear equation. Subsequently, a high-gain observer, which achieves accurate estimations of the unmeasured state and lumped disturbances, is designed. Based on the outputs of the high-gain observer, we develop a chattering-free higher order sliding mode controller to improve the load-following performance while dealing with lumped disturbances and estimation errors of the high-gain observer. In contrast to some previous sliding mode controllers for the load following of NPPs, the proposed controller is completely free from chattering effects because the control input is obtained after integration. The asymptotic stability of the overall control scheme is demonstrated by combining the Lyapunov stability theory with backstepping technology. Finally, the simulation results reveal that the maximum absolute power error is less than 1 × 10 4 W with the proposed control scheme, 5 × 10 5 W with a PID controller, and 4 × 10 5 W with a conventional sliding mode controller. In addition, in contrast to the conventional sliding mode controller, the proposed control scheme produces smooth control input without the chattering phenomenon. Thus, the proposed chattering-free higher order sliding mode control scheme with a high-gain observer provides smoother control input, higher load-following accuracy, and stronger robustness against lumped disturbances than the PID controller and the conventional sliding mode controller.

Efficient and stable operation of marine diesel engines is crucial both for navigation maneuverability and safety of waterjet propulsion vessels. In this paper, a piecewise linear model and an affine nonlinear model of marine diesel engines are established by applying mechanism analysis techniques. The models are validated with the experimental data collected from a 290 kW (maximum power) diesel engine equipped on a waterjet propulsion vessel. The relative error of engine speed between model simulation and the measurements is found less than 3%. To improve the performance of the speed regulation system of diesel engines, a sliding mode control strategy is developed on the basis of the affine nonlinear model utilizing the equivalent control method. Simulation results are presented to illustrate the effectiveness of the proposed control strategy regarding model mismatch, response speed, disturbance rejection, and engine speed tracking ability.

Load following is one of the basic control task for nuclear power plants. This paper proposes a RBF-based adaptive sliding mode control strategy with extended state observer (ESO) for load following of nuclear power plants in the presence of disturbances. The nonlinear mathematical model of the nuclear reactor system is firstly described. In order to recover unmeasured states including average fuel temperature and relative delayed neutron precursor density, an extended state observer is designed to reconstruct both unmeasured states and disturbances at the same time, and its asymptotically stability condition is analyzed. Based on the nuclear reactor model and estimation information from the ESO, a RBF-based adaptive sliding mode control strategy is proposed, which consists of a dual closed-loop integral sliding mode control scheme while taking into account disturbances simultaneously. In addition, the chattering phenomenon of sliding mode control is alleviated by replacing the sign function with a saturation function. Simulation results are provided to verify the effectiveness of the overall control scheme compared with conventional PID control and sliding mode control approaches in accordance to load following performance, states reconstruction accuracy, and disturbances rejection ability.

Grid voltage parameters, including the amplitude, frequency and phase angle, are crucial information for real-time control and protection in power systems. Currently, the phase-locked loop methods are widely used for voltage parameters estimation. However, they could hardly achieve accurate estimation in the presence of unbalanced grid voltage and harmonic disturbance. In this paper, a novel three-phase sinusoid-locked loop (SLL) approach is proposed to address the estimation issue of the unbalanced grid voltage parameters. Firstly, the unbalanced three-phase grid voltage is decomposed as the positive and negative sequences based on the instantaneous symmetrical components theory. Then, two SLLs based on the principles of the virtual synchronous machine (VSM) are developed for the positive and negative sequence grid voltages, respectively. Following the idea of SLL, the output voltage of the VSM is locked with the input grid voltage when the real power and reactive power are both driven to zero. Eventually, the unbalanced three-phase grid voltage is reconstructed with the estimated parameters of the positive and negative sequence voltages. Simulation results demonstrate that the proposed SLL approach achieves excellent estimation performance with respect to the unbalanced grid voltage.

T0620. The gap metric is applied to investigate the nonlinearity of the boiler-turbine system to determine the linearization points for nonlinear model such that a Takagi-Sugeno fuzzy model of appropriate accuracy and complexity is obtained. The FAMPC is then designed based on the fuzzy model that is online updated to well approximate the nonlinear dynamics. The fuzzy model is validated with the plant data of a 300 MW unit and the result shows good agreement. The simulation cases demonstrate the proposed controller has good tracking performance and strong robustness.

Grid voltage parameters, including the amplitude, frequency and phase angle of the grid voltage, are essential for real-time control and protection in power systems. Current approaches mainly focus on adopting a phase-locked loop or its derivatives for parameters estimation. However, they could hardly achieve fast response and are sensitive to voltage variations and disturbances. In this paper, the estimation of grid voltage parameters is recast as an observer design problem. An exosystem model of the sinusoidal voltage is established at first. Then, a framework for the observer-based methods is proposed for the robust estimation of single-phase grid voltage parameters. Moreover, a second-order sliding mode observer (SMO), which actually has the lowest order among all possible observers, is designed to estimate the parameters. Based on the Lyapunov theory, the asymptotic stability of the closed-loop SMO is proved. Compared with two other typical methods, simulations are carried out to illustrate the effectiveness and robustness of the proposed approach in the presence of voltage variations and external disturbances. In addition, the proposed estimation approach may also be valuable for online fault detection of voltage sag and low-frequency oscillation.

In this paper, the GTucker2 model is proposed for monitoring both even-length and uneven-length batch processes. The GTucker2 model has two prominent advantages. The first one is that it performs tensor decomposition on the three-way data array and thus avoids potential problems of information loss and “curse of dimensionality” induced by data unfolding. The second one is that it solves the uneven-length problem in a “natural” way without using batch trajectory synchronization, which prevents distorting data and fault patterns and guarantees higher modeling and monitoring precisions. An online batch process monitoring method is then developed by integrating GTucker2 with the moving data window technique. Three monitoring statistics named Q, R2, and T2 statistics are constructed for fault detection and diagnosis. The effectiveness and advantages of the GTucker2-based monitoring method are illustrated by two case studies in a benchmark fed-batch penicillin fermentation process.

A novel data projection algorithm named tensor global-local preserving projections (TGLPP) is proposed for three-way data arrays. TGLPP is able to preserve global and local structures of data simultaneously. TGLPP builds a unified framework for global structure preserving and local structure preserving. Tensor principal component analysis (TPCA) and tensor locality preserving projections (TLPP) are unified in the TGLPP framework. They are proved to be two limiting cases of TGLPP. A batch process monitoring method is then developed on the basis of TGLPP. The SPD statistic and R2 statistic are used for fault detection. The contribution plot is applied for fault identification. Two case studies are carried out to validate the efficiency of TGLPP-based monitoring method.

A novel method named tensor global–local structure analysis (TGLSA) is proposed for batch process monitoring. Different from principal component analysis (PCA) and locality preserving projections (LPP), TGLSA aims at preserving both global and local structures of data. Consequently, TGLSA has the ability to extract more meaningful information from data than PCA and LPP. Moreover, the tensor-based projection strategy makes TGLSA more applicable for the three-dimensional data than multiway-based methods, such as MPCA and MLPP. A TGLSA-based online monitoring approach is developed by combining TGLSA with a moving window technique. Two new statistics, i.e., SPD and R2 statistics, are constructed for fault detection and diagnosis. In particular, the R2 statistic is a novel monitoring statistic, which is proposed based on a support tensor domain description method. The effectiveness and advantages of the TGLSA-based monitoring approach are illustrated by a benchmark fed-batch penicillin fermentation process.

An approach for multivariate statistical process control based on multiway locality preserving projections (LPP) is presented. The recently developed LPP is a linear dimensionality reduction technique for preserving the neighborhood structure of the data set. It is characterized by capturing the intrinsic structure of the observed data and finding more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. In this study, LPP is used to extract the intrinsic geometrical structure of the process data. Hotelling’s T 2 ( D ) and the squared prediction error (SPE or Q ) statistic charts for on-line monitoring are then presented, and the contribution plots of these two statistical indices are used for fault diagnosis. Moreover, a moving window technique is used for the implementation of on-line monitoring. Case study was carried out with the data of industrial penicillin fed-batch cultivations. As a comparison, the results obtained with the MPCA are also presented. It is concluded that the Multiway LPP (MLPP) outperforms the conventional MPCA. Finally, the robustness of the MLPP monitoring is analyzed by adding noises to the original data.

A scheduling model for a multi-product, multistage batch plant with parallel units is presented. The objective is to maximize the weighted completion times of orders in every processing stage while imposing a penalty on the slower orders. The proposed model uses the continuous-time representation mode and describes the allocations of tasks, units and stages by a set of binary variables. In order to reduce the model size and provide a more effective solution to the model, a pre-ordering approach that sorts the processing sequence of orders is developed. The pre-ordering approach identifies the infeasible assignments through which the number of binary variables is significantly reduced. Illustrative examples are provided to show that the size of the proposed model is small, and therefore, needs much less computational effort in comparison with the existing models in the literature.

A multivariate statistical process control (MSPC) method using dynamic multiway neighborhood preserving embedding (DMNPE) is proposed for fed-batch process monitoring. Different from principal component analysis (PCA) which aims at preserving the global Euclidean structure of the data set, neighborhood preserving embedding aims to preserve the local neighborhood structure of the data set. The neighborhood preserving property enables NPE to find more meaningful intrinsic information hidden in the high-dimensional observations compared with PCA. Moreover, the robustness of NPE is better than that of PCA. On the other hand, a dynamic monitoring approach based on moving window technique is employed to deal with the time-variant property of the dynamic processes. An industrial cephalosporin fed-batch fermentation process is used to demonstrate the performance of the DMNPE. The results show the advantages of DMNPE over those methods such as dynamic multiway PCA (DMPCA), static multiway NPE (SMNPE) and static multiway PCA (SMPCA) in fed-batch process monitoring. Finally, the robustness of the DMNPE monitoring is tested by adding noises to the original data sets.

A new spike sorting method based on the support vector machine (SVM) is proposed to resolve the superposition problem. The spike superposition is generally resolved by the template matching. Previous template matching methods separate the spikes through linear classifiers. The classification performance is severely influenced by the background noise included in spike trains. The nonlinear classifiers with high generation ability are required to deal with the task. A multi-class SVM classifier is therefore applied to separate the spikes, which contains several binary SVM classifiers. Every binary SVM classifier corresponding to one spike class is used to identify the single and superposition spikes. The superposition spikes are decomposed through template extraction. The experimental results on the simulated and real data demonstrate the utility of the proposed method.

In this article, a nonlinear dynamic multiway partial least squares (MPLS) based on support vector machines (SVM) is developed for on-line fault detection in batch processes. The approach, referred to as SVM-based DMPLS, integrates the SVM with the MPLS model. Process data from normal historical batches are used to develop the MPLS model, and a series of single-input-single-output SVM networks are adopted to approximate nonlinear inner relationship between input and output variables. In addition, the application of a time-lagged window technique not only makes the complementarities of unmeasured data of the monitored batch unnecessary, but also significantly reduces the computation and storage requirements in comparison with the traditional MPLS. The proposed approach is validated by a simulation study of on-line fault detection for a fed-batch penicillin production.

A new method for spike sorting is proposed which partly solves the overlapping problem. Principal component analysis and subtractive clustering techniques are used to estimate the number of neurons contributing to multi-unit recording. Spike templates (i.e. waveforms) are reconstructed according to the clustering results. A template-matching procedure is then performed. Firstly all temporally displaced templates are compared with the spike event to find the best-fitting template that yields the minimum residue variance. If the residue passes the χ 2 -test, the matching procedure stops and the spike event is classified as the best-fitting template. Otherwise the spike event may be an overlapping waveform. The procedure is then repeated with all possible combinations of two templates, three templates, etc. Once one combination is found, which yields the minimum residue variance among the combinations of the same number of component templates and makes the residue pass the χ 2 -test, the matching procedure stops. It is unnecessary to check the remaining combinations of more templates. Consequently, the computational effort is reduced and the over-fitting problem can be partly avoided. A simulated spike train was used to assess the performance of the proposed method, which was also applied to a real recording of chicken retina ganglion cells.

A rolling learning-prediction approach based on neural networks is proposed with the aim of on-line prediction of the product formation. Commercial-scale penicillin cultivations were taken as an example to test the product predictor. Raw data are pretreated in such a way that each input vector of the neural network consists of a series of time-discretised values on a specified transient of process variables. The output vector is composed of the amount of product at the next one and two prediction steps. The process variables involved in the predictor include carbon dioxide and product formation as well as oxygen, precursor and substrate consumption. Accumulated rather than instant values of these variables were used. A simple three-layer feedforward backpropagation neural network with a tangent sigmoidal transfer function in the hidden nodes and a linear one in the output nodes was used as the main frame of the product predictor. The proposed prediction procedure is called rolling learning-prediction because the training database is updated after each sampling interval and the learning-prediction is repeated thereafter. The robustness of the predictor was illustrated by its adaptive ability to widely scattered data sets and extra added noises. The testing results indicated that a prediction accuracy of 2–5% could be generally expected in the later phase of cultivation and reliable prediction time spans may take more than 10% of the cultivation period for penicillin production. An intrinsic problem of using neural networks—occasional trap of the network in bad local minima—is automatically detected and remedied. In addition, it was illustrated by example that the prediction error signal may be potentially used to detect extraordinary charges caused, for example, by contamination. Problems associated with the industrial application of the predictor are discussed.

This article has been retracted consistent with Elsevier Policy on Article Withdrawal. The publisher apologises for any inconvenience this may cause.

A multiple modeling algorithm for antibiotic fermentation process based on fuzzy c-means (FCM) and support vector regression (SVR) is proposed. By analyzing the features of antibiotic fermentation, the mechanism of multiple modeling of the bioprocess is presented. Using FCM clustering method, the bioprocess is classified into several work states and sub-models. Then, taking advantage of the generalization properties of SVR, the multiple model of bioprocess is established and the proposed algorithm is described. Experimental data of industrial penicillin production is used to validate the model.

Spike sorting is a prerequisite for studying the multi-channel extracellular recordings. A new method is developed for spike sorting in multi-channel extracellular recordings of retinas. Based on the assumption that noise follows Gaussian distribution, spike templates are constructed based on principal component analysis and subtractive clustering, then a template matching procedure is applied to choose the best-fit template or templates combination for overlapping spikes so that after subtracting the template or templates combination from the spike event, the sum of squared residue follows Chi-square distribution. Artificial spike trains are used to assess the performance of the proposed method.