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HyperSpectral Imaging (HSI) plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. HyperSpectral (HS) image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper presents a HS image dataset encompassing left- and right-hand images captured from 100 subjects with varying skin tones. The dataset was annotated using anatomical data to represent vein and non-vein areas within the images. This dataset is utilised to explore the effectiveness of dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward's Linkage Strategy using Mutual Information (WaLuMI) for vein detection. To generate experimental results, the HS image dataset was divided into train and test datasets. Optimum performing parameters for each of the dimensionality reduction techniques in conjunction with the Support Vector Machine (SVM) binary classification were determined using the Training dataset. The performance of the three dimensionality reduction-based vein detection methods was then assessed and compared using the test image dataset. Results show that the FPCA-based method outperforms the other two methods in terms of accuracy. For visualization purposes, the classification prediction image for each technique is post-processed using morphological operators, and results show the significant potential of HS imaging in vein detection. [ABSTRACT FROM AUTHOR]

In sophisticated and complex system such as nuclear power plant, fault estimation and fault tolerant control always play an important role in maintaining the system stability and assuring satisfactory and safe operation. Thus, in this work a fault estimation and fault tolerant control scheme based on sliding mode theory is proposed for a pressurized water reactor type nuclear power plant considering simultaneous actuator and sensor faults. First, using descriptor sliding mode observer approach, an accurate estimation of the system states and sensor fault vector have been obtained simultaneously. Then, based on the estimated information, an integral type sliding mode control scheme is proposed to stabilize the resulting faulty system. With the help of Lyapunov stability theory, reachabilities of the proposed sliding mode surfaces are shown in both the state estimation space and the error estimation space, simultaneously. Finally, the efficacy of the proposed control scheme is shown by applying it to a nuclear power plant. • A fault estimation and fault tolerant control scheme based on sliding mode theory is designed for a pressurized water reactor type nuclear power plant. • The overall problem of active FTC is considered. • Effects of simultaneous actuator and sensor faults are studied. • A sufficient condition is derived using linear matrix inequalities to ensure overall stability of the closed-loop system. [ABSTRACT FROM AUTHOR]

Abstract The loss of coolant accident (LOCA) of a nuclear power plant (NPP) is a severe accident in the nuclear energy industry. Nowadays, neural networks have been trained on nuclear simulation transient datasets to detect LOCA. This paper proposes a constraint-based genetic algorithm (GA) to find optimised 2-hidden layer network architectures for detecting LOCA of a NPP. The GA uses a proposed constraint satisfaction algorithm called random walk heuristic to create an initial population of neural network architectures of high performance. At each generation, the GA population is split into a sub-population of feature subsets and a sub-population of 2-hidden layer architectures to breed offspring from each sub-population independently in order to generate a wide variety of network architectures. During breeding 2-hidden layer architectures, a constraint-based nearest neighbor search algorithm is proposed to find the nearest neighbors of the offspring population generated by mutation. The results showed that for LOCA detection, the GA-optimised network outperformed a random search, an exhaustive search and a RBF kernel support vector regression (SVR) in terms of generalization performance. For the skillcraft dataset of the UCI machine learning repository, the GA-optimised network has a similar performance to the RBF kernel SVR and outperformed the other approaches. [ABSTRACT FROM AUTHOR]

This paper presents an arbitrary order continuous-time sliding mode controller based on the super-twisting algorithm for a nonlinear pressurized water nuclear power plant. A proportional-derivative terminal sliding surface is designed to achieve the finite time convergence and to enhanced the tracking performance. The proposed controller is chattering free, which is always preferable in most of the practical applications and, it is robust against Lipschitz in time uncertainties. The implementation of the proposed controller requires only the information about the system output, and thus, it is most suitable for large scale complex systems, such as nuclear power plants. Superiority of the proposed controller over some conventional control techniques in the presence of uncertainties is shown with the help of simulation results in the MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

Abstract: Dynamic neural networks (DNNs) have important properties that make them convenient to be used together with nonlinear control approaches based on state space models and differential geometry, such as feedback linearisation. However the mapping capability of DNNs are quite limited due to their fixed structure, that is, the number of layers and the number of hidden units. An example shown in this paper has demonstrated this limitation of DNNs. The development of novel DNN structures, which has good mapping capability, is a relevant challenge being addressed in this paper. Although the structure is changed minorly only, the mapping capability of the new designed DNN in this paper has been improved dramatically. Previous work [J. Deng et al., 2005. The dynamic neural network of a hybrid structure for nonlinear system identification. In: 16th IFAC World Congress, Prague.] presents a new dynamic neural network structure which is suitable for the identification of highly nonlinear systems, which needs the outputs from the real system for training and operation. This paper presents a hybrid dynamic neural network structure which presents a similar idea of serial–parallel hybrid structure, but it uses an output from another neural network for training and operation classified as a serial–parallel model. This type of DNNs does not require the output of the plant to be used as an input to the model. This neural network has the advantages of good mapping capabilities and flexibilities in training complicated systems, compared to the existed DNNs. A theoretical proof showing how this hybrid dynamic neural network can approximate finite trajectories of general nonlinear dynamic systems is given. To illustrate the capabilities of the new structure, neural networks are trained to identify a real nonlinear 3D crane system. [Copyright &y& Elsevier]

In this paper, the preview tracking control problem for linear discrete-time periodic systems is considered. First, to overcome the difficulty arising from periodicity of the system, the linear discrete-time periodic system is transformed into an ordinary time-invariant system by lifting method. Secondly, the difference between a system state and its steady-state value is used to derive an augmented system instead of the usual difference between system states. Then, the preview controller for the augmented system is proposed by the preview control theory, which solves the preview tracking control problem for the periodic systems. Moreover, an integrator is introduced to ensure that the output can track the reference signal without static error. Finally, the obtained results are illustrated by the simulation examples. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of output tracking control with preview action for a class of Lipschitz nonlinear systems. By adopting a backward difference approach, an augmented error system is successfully constructed that fully utilizes the available future information in the reference signal as well as disturbances. The output tracking control problem with preview action is thereby reduced to a stabilization problem. Then, both the state feedback controller and the static output feedback controller are systematically developed. With the aid of the S-procedure and auxiliary matrix variable approach, sufficient design conditions for the asymptotic stability of the closed-loop system are presented through linear matrix inequality formulation. Based on these criteria, two novel output tracking control laws are derived. Finally, two numerical examples are provided to highlight the effectiveness and superiority of the proposed control methodologies in terms of enhancing the overall tracking performance of the system. [ABSTRACT FROM AUTHOR]

In this paper, a fault-tolerant preview controller is designed for a class of impulse controllable continuous time descriptor systems with sensor faults. Firstly, the impulse is eliminated by introducing state prefeedback; then an algebraic equation and a normal control system are obtained by restricted equivalent transformation for the descriptor system after impulse elimination. Next, the model following problem in fault-tolerant control is transformed into the optimal regulation problem of the augmented system which is constructed by a general method. And the final augmented system and its corresponding performance index function are obtained by state feedback for the augmented system constructed above. The controller with preview effect for the final augmented system is attained based on the existing conclusions of optimal preview control; then, the fault-tolerant preview controller for the original system is obtained through integral and backstepping. The relationships between the stabilisability and detectability of the final augmented system and the corresponding characteristics of the original descriptor system are also strictly discussed. The effectiveness of the proposed method is verified by numerical simulation. [ABSTRACT FROM AUTHOR]

This paper proposes and investigates a problem of preview tracking control for a class of continuous-time singular interconnected systems. Firstly, the related items are deleted to obtain several isolated subsystems with low dimensions. An error system is constructed for each isolated subsystem so that the tracking error is included in the state vector of the error system; then, the tracking problem is transformed into a regulation problem. Secondly, the preview tracking controller is designed for each error system and obtained controllers are combined as the controller of the error system of the singular interconnected system. Thirdly, the Lyapunov function method is utilized to determine the constraints of the related terms so that the closed-loop system of the error system of the singular interconnected system is stable under the action of the controller obtained. Finally, the preview tracking controller of the singular interconnected system is obtained from the relationship between the error system and the original system. A numerical simulation algorithm for continuous-time singular systems is also proposed in this paper. The numerical simulation illustrates the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper considers the design of the robust preview controller for a class of uncertain discrete-time Lipschitz nonlinear systems. According to the preview control theory, an augmented error system including the tracking error and the known future information on the reference signal is constructed. To avoid static error, a discrete integrator is introduced. Using the linear matrix inequality (LMI) approach, a state feedback controller is developed to guarantee that the closed-loop system of the augmented error system is asymptotically stable with H∞ performance. Based on this, the robust preview tracking controller of the original system is obtained. Finally, two numerical examples are included to show the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of preview tracking control for a class of differentiable nonlinear systems. First, by constructing an augmented error system (AES), the tracking problem is transformed into a regulation problem. Thanks to the differentiable mean value theorem, the obtained AES is a linear parameter varying system. Then, a state feedback controller is developed and a sufficient condition for asymptotic stability of the closed-loop system is presented in the LMI form. Based on this criterion, the preview controller of the original system is designed to guarantee that the output asymptotically tracks a reference signal. Finally, the effectiveness of the proposed controller is shown by numerical simulations. [ABSTRACT FROM AUTHOR]

This paper investigates the cooperative global optimal preview tracking problem of linear multi-agent systems under the assumption that the output of a leader is a previewable periodic signal and the topology graph contains a directed spanning tree. First, a type of distributed internal model is introduced, and the cooperative preview tracking problem is converted to a global optimal regulation problem of an augmented system. Second, an optimal controller, which can guarantee the asymptotic stability of the augmented system, is obtained by means of the standard linear quadratic optimal preview control theory. Third, on the basis of proving the existence conditions of the controller, sufficient conditions are given for the original problem to be solvable, meanwhile a cooperative global optimal controller with error integral and preview compensation is derived. Finally, the validity of theoretical results is demonstrated by a numerical simulation. [ABSTRACT FROM PUBLISHER]

This paper investigates the absolute stability problem of time-varying delay Lurie indirect control systems with variable coefficients. A positive-definite Lyapunov-Krasovskii functional is constructed. Some novel sufficient conditions for absolute stability of Lurie systems with single nonlinearity are obtained by estimating the negative upper bound on its total time derivative. Furthermore, the results are generalised to multiple nonlinearities. The derived criteria are especially suitable for time-varying delay Lurie indirect control systems with unbounded coefficients. The effectiveness of the proposed results is illustrated using simulation examples. [ABSTRACT FROM AUTHOR]

This paper discusses the optimal preview control problem for a class of linear continuous stochastic control systems in the infinite horizon, based on the augmented error system method. Firstly, an assistant system is designed and the state equation is translated to the assistant system. Then, an integrator is introduced to construct a stochastic augmented error system. As a result, the tracking problem is converted to a regulation problem. Secondly, the optimal regulator is solved based on dynamic programming principle for the stochastic system, and the optimal preview controller of the original system is obtained. Compared with the finite horizon, we simplify the performance index. We also study the stability of the stochastic augmented error system and design the observer for the original stochastic system. Finally, the simulation example shows the effectiveness of the conclusion in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a method for designing a type one servomechanism for a discrete-time linear system with input delay subject to a previewable desired output and a nonmeasurable constant disturbance. The tracking problem of a delay system is transformed into a regulation problem of a delay-free system via constructing an augmented error system and a variable substitution. A controller is obtained with delay compensation and preview compensation based on preview control theory and the predictor method. When the state vector is not directly measurable, a full-dimensional observer is offered. The effectiveness of the design method is demonstrated by numerical simulations. [ABSTRACT FROM AUTHOR]

This work proposes adaptive control-based design strategies to control a pressurized water reactor (PWR) nuclear power plant (NPP). An L1-adaptive-based state-feedback control technique is proposed using the linear quadratic Gaussian control and projection-based adaptation laws. The control scheme possesses good robustness capabilities in handling disturbances and uncertainties. A robust L1-adaptive control technique is also proposed by combining the L1-adaptive control with the loop transfer recovery (LTR) technology. The framework hence gives the strengthened robust set-point tracking performance given the matched and unmatched uncertainties and disturbances. The NPP model employed in this article is defined by five inputs, five outputs, and 38 state variables. A linear model for controller design is obtained by linearizing the nonlinear NPP model at operating conditions. Various simulations are carried out on subsystems of the NPP to verify the effectiveness of the proposed scheme. Numerical and statistical measures are computed for quantitative analysis of the controllers’ performance. Several classical control design techniques are also implemented, and their performance is compared with the proposed adaptive control techniques. [ABSTRACT FROM AUTHOR]

This paper studies the preview tracking control problem for linear discrete-time periodic switched systems. Firstly, an augmented error system is constructed for each subsystem by stabilizing the augmented error systems through the method of optimal preview control, and the tracking problem of the switched system is transformed into the switched stability problem of closed-loop augmented error systems. Secondly, a switched Lyapunov function method is applied to search the minimal dwell time satisfying the switched stability of the closed-loop augmented error systems. Thirdly, the switched preview control input is solved from the controller of the individual augmented error system, and then the sufficient conditions and the preview controller can be obtained to guarantee the solvability of the original periodic switched preview tracking problem. Finally, numerical simulations show the effectiveness of the stabilization design method. [ABSTRACT FROM AUTHOR]

This article proposes and studies a problem of preview control for a type of discrete-time interconnected systems. First, adopting the technique of decentralized control, isolated subsystems are constructed by splitting the correlations between the systems. Utilizing the difference operator to the system equations and error vectors, error systems are built. Then, the preview controller is designed for the error system of each isolated subsystem. The controllers of error systems of isolated subsystems are aggregated as a controller of the interconnected system. Finally, by employing Lyapunov function method and the properties of non-singular M-matrix, the guarantee conditions for the existence of preview controllers for interconnected systems are given. The numerical simulation shows that the theoretical results are effective. [ABSTRACT FROM AUTHOR]

In the fault-tolerant control theory based on model following control, the desired signal of the control system is the output of a reference system. This paper is concerned with the design of the preview controller for a class of fault systems. A composite vector is introduced by including error vector, fault system state vector and reference system state vector. Then, we derived an augmented system from the known system equation, in which the reference input has equal status with the desired signal in the traditional preview control theory. Therefore, we can use the known theory to design the preview controller for the augmented system, then the preview controller of the original fault system can be obtained by the integration method. This paper strictly discusses the connection between stabilisation and detectability of the augmented system and the corresponding characteristics of the original system. Finally, by applying this theory to a real steam generator water level control system, it is found that the actions of the reference input preview and the fault signal preview can effectively eliminate the effect of the fault signal on the water level of the steam generator. The simulation shows the effectiveness of the controller designed. [ABSTRACT FROM AUTHOR]

In this paper, the problem of optimal preview control is studied for a class of linear continuous-time large-scale systems. We first construct an augmented system including the error signal and the reference signal to transform the tracking problem into the regulator problem. Then, the controllers are designed for isolated augmented subsystems, which also constitute the controller of large-scale systems. On the basis of proving the asymptotic stability of closed-loop large-scale systems and the existence of the controller, sufficient conditions for reaching optimal preview control are given. In particular, the limiting condition of the correlation matrices is determined by the fact that the total derivative of a positive definite Lyapunov function is negative definite. The numerical simulation indicates that the controller can drive the large-scale systems to track the reference signal without steady-state error, and the tracking effect is improved with the increasing preview length. [ABSTRACT FROM AUTHOR]

In this study we propose a simple, yet inherently fault-tolerant and robust controller utilizing a combination of both feedforward and feedback control schemes. Feedback controllers are known to be robust, but feedforward controllers can be reliable in the context of safety as they can be less dependent on measurements. Though, many combined feedforward and feedback controllers have been proposed in the literature, to the best of our knowledge a controller tolerant to the failure of power feedback signal is nowhere demonstrated. In this scheme, major corrections are effected by a model predictive forward control unit, while bounded uncertain part is corrected by a feedback control unit. The feedforward control is implemented using the inverse equations of the nuclear reactor point kinetics model. The bounded error control is effected by a simpler limited Proportional–Integral–Derivative (PID) feedback controller. Stability analysis of the controller is carried out numerically by Lyapunov's direct method with the help of Particle Swarm Optimization (PSO) technique. The proposed controller (referred to as Inverse Dynamics Corrected Control (IDCC)), is studied for reactor power control with model error plus uncertainty and a principal feedback signal failure case (i.e. power sensor failure). It is shown that the IDCC has excellent load tracking performance for a challenging demand profile with model error, in comparison to Sliding Mode Controller (SMC) and a manually tuned PID controller. For the loss of principal feedback signal case, there is a trade off between the tracking performance of IDCC and safety aspects. While both PID and SMC drive the reactor power to unsafe levels, IDCC maintains the reactor power within a safer bound. • A conceptually new controller tolerant to faults in feedback signal has been designed. • The controller's stability is analysed by Lyapunov's method with the particle swarm optimization. • The controller's fault tolerance, robustness and power tracking ability has been demonstrated. • The controller's performance is compared with PID and SMC controllers. [ABSTRACT FROM AUTHOR]

A finite-time bounded tracking control problem for a class of linear discrete-time systems subject to disturbances is investigated. Firstly, by applying a difference method to constructing the error system, the problem is transformed into a finite-time boundedness problem of the output vector of the error system. In fact, this is a finite-time boundedness problem with respect to the partial variables. Secondly, based on the partial stability theory and the research methods of finite-time boundedness problem, a state feedback controller formulated in form of linear matrix inequality is proposed. Based on this, a finite-time bounded tracking controller of the original system is obtained. Finally, a numerical example is presented to illustrate the effectiveness of the controller. [ABSTRACT FROM AUTHOR]

The turbocharged diesel engine is a typical multi-input multioutput system with strong couplings, actuator constraints, and fast dynamics. This paper addresses the exhaust emission regulation in turbocharged diesel engines using an explicit model predictive control (EMPC) approach, which allows tracking of the time-varying setpoint values generated by the supervisory level controller while satisfying the actuator constraints. The proposed EMPC framework consists of calibration, engine model identification, controller formulation, and state observer design. The proposed EMPC approach has a low computation requirement and is suitable for implementation in the engine control unit on board. The experimental results on a turbocharged Cat C6.6 diesel engine demonstrate that the EMPC controller significantly improves the tracking performance of the exhaust emission variables in comparison with the decoupled single-input single-output control methods. [ABSTRACT FROM PUBLISHER]

• A hybrid controller is proposed to improve the control performance of PWR-type NPP. • The LQG, LQI, and LTR are integrated to design state-feedback assisted output controller. • Robust control of core-power, SG pressure, turbine-speed, pressurizer pressure and level. • Guaranteed set-point tracking with zero steady-state error in an uncertain environment. This work proposes a new hybrid control strategy for a pressurized water type nuclear power plant by integrating linear quadratic integrator (LQI), linear quadratic Gaussian (LQG), and loop transfer recovery (LTR) approaches. The multi-input multi-output nuclear plant model adopted in this work is characterized by 38 state variables. The nonlinear plant model is linearized around steady-state operating conditions to obtain a linear model for the controller design. The proposed LQGI/LTR technique designs state-feedback assisted output control using the estimated states. The control architecture offers robust performance and tracks the reference set-point with zero steady-state error in the presence of uncertainties and disturbances. The effectiveness of the proposed technique is demonstrated by simulations on different subsections of a pressurized water nonlinear nuclear power plant model. The control performance of the proposed technique is further compared with other classical control design schemes. Statistical measures are employed to quantitatively analyse control performance. [ABSTRACT FROM AUTHOR]

• MRAC (Model Reference Adaptive Controller) integrated with the diagnosis information of UKF (Unscented-Kalman-Filter) is used to fault-tolerant control. • The validated SCR aging model is used to carry out the simulation. • The performance of aging SCR based on UKF and MRAC can maintain constant. • The UKF can be used in control fields and realizes a better control performance. The performance of the selective catalytic reduction (SCR) system has been confirmed to be distinctly affected by hydrothermal aging fault. In this paper, an observer based on the Unscented-Kalman-Filter (UKF) algorithm theory is designed to identify the states of hydrothermal aging so that the aging SCR can be more accurately and efficiently diagnosed and fault-tolerant controlled. Furthermore, a model reference adaptive controller (MRAC) based on the theory of Lyapunov is constructed to enhance the fault-tolerant control performance of SCR Simulation, on the basis of the diagnosis information of UKF observer. The simulations and comparisons are performed to verify the performance of aging diagnosis and fault-tolerant control. The corresponding results show that the UKF observer has better estimation accuracy and respond speed for SCR aging, simultaneously, the fault-tolerant control performance of SCR aging is also better. [ABSTRACT FROM AUTHOR]

A control design scheme is formulated for a pressurized water type nuclear power plant by integrating the optimal linear quadratic Gaussian (LQG) control with the robust integral sliding mode (ISM) technique. A novel robust-optimal hybrid control scheme is further proposed by integrating the LQG-ISM technique with the loop transfer recovery approach to enhance the effectiveness and robustness capability. The control architecture offers robust performance with minimum control efforts and tracks the reference set-point effectively in the presence of disturbances and parametric uncertainties. The multi-input-multi-output nuclear power plant model adopted in this work is characterized by 38 state variables. The nonlinear plant model is linearized around steady-state operating conditions to obtain a linear model for the controller design. The efficacy of the proposed controllers is demonstrated by nuclear power plant subsystem simulations. The control performance of the proposed technique is also compared with other classical control design schemes. Numerical measures are employed to quantitatively analyse and compare the performance of the different controllers that are studied in the work. Image 1 • Robust-optimal controllers are proposed to improve performance with fewer control efforts. • Design, validation, and testing of various control loops of a PWR-type NPP. • Control of power, temperature, SG pressure, turbine speed, pressurizer pressure and level. • Robust performance in the presence of parametric uncertainties and disturbances. [ABSTRACT FROM AUTHOR]

• Integrated modelling of a PWR-type nuclear power plant, actuators, and sensors. • Control of reactor power, SG pressure, turbine speed, pressurizer pressure, and level. • Carefully tuned PI and LQG control strategies across different control loops. • Comprehensive open and closed-loop analysis of the model. • Completeness of equations and parameters make the model readily implementable. This article presents an integrated non-linear dynamic model of a Pressurized Water-type Nuclear Reactor (PWR) and associated plant components for control design and evaluation purposes. The model uses the first-principles approach to represent various components of the plant. The model considers the dynamics of the reactor core, thermal hydraulics, piping and plenum, pressurizer, steam generator, condenser, and turbine-governor system, in addition to various actuators and sensors. The response of the proposed model is tested using perturbations in different input variables. Various control loops implementing low-level PI control strategies are designed and implemented in the model to simulate the closed-loop behaviour of the plant. These include control loops for reactor power, steam generator pressure, pressurizer pressure and level, and turbine speed. Linear quadratic Gaussian-based optimal control strategies are further developed and implemented. Unique contributions of the work include the set of plant sections that are considered, the implementation of carefully tuned control strategies, the completeness of the model equations, and the availability of parameter values so that the model is readily implementable and has the potential to become a benchmark for control design studies in PWR nuclear power plants. [ABSTRACT FROM AUTHOR]

The estimation of radioactivity release following an accident in a nuclear power plant is crucial due to its short and long-term impacts on the surrounding population and the environment. In the case of any accidental release, the activity needs to be estimated quickly and reliably to effectively plan a rapid emergency response and design an appropriate evacuation strategy. The accurate prediction of incurred dose rate during normal or accident scenario is another important aspect. In this article, three different non-linear estimation techniques, extended Kalman filter, unscented Kalman filter, and cubature Kalman filter are proposed in order to estimate release activity and to improve the prediction of dose rates. Radionuclide release rate, average wind speed, and height of release are estimated using the dose rate monitors data collected in proximity of the release point. Further, the estimates are employed to improve the prediction of dose rates. The atmospheric dispersion phenomenon of radioactivity release is modelled using the Gaussian plume model. The Gaussian plume model is then employed for the calculation of dose rates. A variety of atmospheric and accident related scenarios for single source and multiple sources are studied in order to assess the efficacy of the proposed filters. Statistical measures have been used in order to validate the performance of the proposed approaches. [ABSTRACT FROM AUTHOR]