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Effective model is a novel tool for decentralized controller design to handle the interconnected interactions in a multi-input-multi-output (MIMO) process. In this paper, Type-1 and Type-2 effective Takagi-Sugeno fuzzy models (ETSM) are investigated. By means of the loop pairing criterion, simple calculations are given to build Type-1/Type-2 ETSMs which are used to describe a group of non-interacting equivalent single-input-single-output (SISO) systems to represent an MIMO process, consequently the decentralized controller design can be converted to multiple independent single-loop controller designs, and enjoy the well-developed linear control algorithms. The main contributions of this paper are: i) Compared to the existing T-S fuzzy model based decentralized control methods using extra terms to characterize interactions, ETSM is a simple feasible alternative; ii) Compared to the existing effective model methods using linear transfer functions, ETSM can be carried out without requiring exact mathematical process functions, and lays a basis to develop robust controllers since fuzzy system is powerful to handle uncertainties; iii) Type-1 and Type-2 ETSMs are presented under a unified framework to provide objective comparisons. A nonlinear MIMO process is used to demonstrate the ETSMs’ superiority over the effective transfer function (ETF) counterparts as well as the evident advantage of Type-2 ETSMs in terms of robustness. A multi-evaporator refrigeration system is employed to validate the practicability of the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract: Recently, exploiting the centroids of stability regions (admissible regions) to be used in tuning two-parameter controllers has been considered as an approach to obtain non-fragile two-parameter controllers. Such an approach can be extended for three-dimensional stability spaces (admissible spaces) by considering the center of mass of these spaces in tuning three-parameter controllers. In this paper, the mentioned approach is used to tune PID controllers for controlling integrator plus dead-time (IPDT) and first order plus dead-time (FOPDT) processes. It is shown that the tuning method resulted from this approach is very similar to the Ziegler–Nichols step response tuning method. Consequently, this paper presents an analytic non-fragility justification for the mentioned tuning method. [Copyright &y& Elsevier]

Abstract: This paper describes a new method for the design of model predictive control (MPC) using non-minimal state space models, in which the state variables are chosen as the set of measured input and output variables and their past values. It shows that the proposed design approach avoids the use of an observer to access the state information and, as a result, the disturbance rejection, particularly the system input disturbance rejection, is significantly improved when constraints become activated. In addition, when there is no model/plant mismatch, the paper shows that the system output constraints can be realised in the proposed approach. Furthermore, closed-form transfer function representation of the model predictive control system enables the application of frequency response analysis tools to the nominal performance of the system. [Copyright &y& Elsevier]

The goethite process is a complicated process with multiple interactive chemical reactions in zinc hydrometallurgy. The use of a dynamic model plays an important role in predicting the key indicator on-line and in process control and optimization. However, because of the coupling influences among the chemical reactions, the conventional continuous stirred tank reactor (CCSTR) model is not adequate to describe this process. In this paper, we develop a weighted-coupling CSTR (WCCSTR) model for the goethite process by introducing weighted parameters. A parameter identification method is proposed to determine the unknown parameters. Then, a model predicted control (MPC) scheme is designed to achieve the process performance goals and minimize the process cost. To overcome the impact of frequent fluctuations in production conditions on the control performance, a novel parameter adaptive correction approach is proposed. The convergence of the adaptive correction approach is proved based on Lyapunov stability theory. Simulation results verify that the WCCSTR model has a higher prediction accuracy than the CCSTR model. The experimental results demonstrate that the MPC scheme performs better in controlling the process and reducing the process costs. [ABSTRACT FROM AUTHOR]

In this paper, sensor fault detection and isolation of time-varying nonlinear dynamical systems is studied by utilizing an adaptive kernel principal component analysis (KPCA) solution as a useful method to overcome the weaknesses of conventional KPCA approach in dealing with time-varying dynamical processes. Toward this goal, adaptive Hotelling's T 2 is used with KPCA to tackle the time-varying behavior of nonlinear systems. Moreover, for fault isolation, partial adaptive KPCA (AKPCA) is proposed where a set of residual signals is generated based on the structured residual set framework. The simulation studies demonstrate that using the proposed methodology, the occurrence of sensor faults in the nonlinear dynamic model of an aeroderivative gas turbine can be effectively detected and isolated in the presence of component degradation. [ABSTRACT FROM AUTHOR]

This paper considers the isolation problem of incipient sensor fault. Based on recursive transformed component statistical analysis (RTCSA), two different isolation methods are proposed. The first method is called subspace reconstruction, where elements in specific subspaces are eliminated, and then reconstructed by minimizing the reconstructed detection index. The faulty variable is determined by the least scaled reconstructed detection index. The second method is called subblock detection, which has less online computational complexity. The subblocks of the measurement matrix are sequentially selected in each sliding window to calculate the subblock detection indices, and the faulty variable is determined by the largest subblock detection margin. Compared with the existing isolation methods such as reconstruction-based contribution (RBC) and its variant termed as average residual-difference reconstruction contribution plot (ARdR-CP), the superior isolation performances of the proposed methods are illustrated by a numerical example as well as a simulation on a continuous stirred tank reactor. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the model predictive control algorithms that are tailored for uncertain systems. Robustness notions with respect to both deterministic (or set based) and stochastic uncertainties are discussed and contributions are reviewed in the model predictive control literature. We present, classify and compare different notions of the robustness properties of state of the art algorithms, while a substantial emphasis is given to the closed-loop performance and computational complexity properties. Furthermore, connections between (i) the theory of risk and (ii) robust optimization research areas and robust model predictive control are discussed. Lastly, we provide a comparison of current robust model predictive control algorithms via simulation examples illustrating closed loop performance and computational complexity features. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a nonlinear system identification using parallel linear-plus-neural network models that provide more accurate predictions on the process behavior even on extrapolated regions. For this purpose, a residuals-based identification algorithm using parallel integration of linear orthonormal basis filters (OBF) and neural networks model is developed and analyzed under range extrapolations. Results on the van de Vusse reactor case study show enhanced extrapolation capability when compared to the conventional neural network (NN) and the series Wiener-NN models. [Copyright &y& Elsevier]

Abstract: This paper proposes a clustering-analysis-based membership functions formation method for fuzzy controller of ball mill pulverizing system. An improved density-based clustering algorithm is presented to detect the clusters in the field database and the clusters are agglomerated or divided to form the new clusters according to the proximity or the dissimilarity between objects. Then, the new clusters are projected into the domains of the control variables to form the intervals, and the membership functions are established based on the intervals and the projections of the centroids of the clusters. The experiments results verify that the performance of our membership functions formation method is better and the fuzzy controller designed by our method has higher control quality. In addition, the fuzzy controller has been put into practice and the field data verify the effectiveness of the fuzzy controller. [Copyright &y& Elsevier]

Abstract: Decentralized controllers with integral action represent a large portion of multivariable controllers currently employed in the process industry. The integrity of these systems with respect to loop failures has been relatively well researched. In particular, important results have been found with respect to the relative gain array (RGA). However, less has been done on the treatment of the integrity of uncertain systems. This paper considers the integrity conditions of uncertain systems with norm-bounded uncertainties, and extends four existing integrity conditions to these uncertain systems. These are: integral stabilizable (IS), integral controllable (IC), integral controllable with integrity (ICI), and decentralized integral controllability (DIC). For the first three, necessary and sufficient conditions are derived. For the DIC condition, a sufficient condition has been derived. These conditions impose a limit on the norm of the allowed perturbations, below which the closed-loop system is guaranteed to maintain integrity. [Copyright &y& Elsevier]

Abstract: In this paper, an extension of the modified generalized predictive control (GPC) algorithm and a tuning strategy is presented. To take the plant dynamics such as under damped behavior and the effect of zeros into account, extension to the second order plus dead time (SOPDT) of the first order plus dead time (FOPDT) modified GPC method is proposed. It is shown that this method is computationally undemanding. Also, implementation is more straightforward than conventional GPC algorithms. Moreover, the proposed tuning strategy enables a fast implementation of the GPC with regard to nominal stability and desired performance. The simplicity of this strategy and its wide applicability makes it readily accessible to practitioners for utilization. Multiple simulation results are provided to show the effectiveness of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: In this paper, a new form of Repetitive Control (RC) is proposed which is applicable to an extended class of systems, including time-delay systems. A new convergence condition which is necessary and sufficient will be derived for this RC. Simulation examples will illustrate the practical applications of the results to process control problems. [Copyright &y& Elsevier]

Abstract: The interactor matrix plays a key role in establishing multivariate performance indices for systems that are described by the superposition of linear(ized) dynamics and additive stochastic or deterministic disturbances. In this paper, new predictive interpretations for the invariant dynamics are provided. These interpretations enable bounds for the mean-square error performance to be easily and intuitively established. From these results it is shown that the number of zeros at infinity can be obtained from the formulation of a simple model predictive control problem without the requirement to explicitly form the interactor matrix. [Copyright &y& Elsevier]

Abstract: This paper presents a procedure for robust two-degrees-of-freedom PID synthesis based on a non-convex optimization problem considering the and/or guaranteed costs of the closed-loop transfer matrix as objective functions and constraints together with robust regional pole placement. The uncertain system can be represented by multiple, polytopic or affine parameter-dependent models. The efficiency of the proposed synthesis procedure is illustrated by examples. [Copyright &y& Elsevier]

Abstract: The problem of optimizing a dynamic system in the presence of uncertainty is typically tackled using measurements. The method most widely documented in the literature is based on repetitive optimization of an updated process model. Recently, a computationally less expensive alternative that is based on the real-time adaptation of a solution model using measurements has been proposed. The solution model, which relates elements of the input profiles to the set of active constraints and to appropriate sensitivities, has typically been derived manually from physical insight and intuition. This paper presents a systematic and automated approach to generate a solution model based on recent results in numerical optimization of dynamic systems. This concept provides the first step toward an entirely automated procedure for constrained dynamic optimization of uncertain large-scale processes. [Copyright &y& Elsevier]

Abstract: Run-to-run variability is a common problem for modeling batch-wise and semi-continuous operated processes. Although observed reactor runs show the same trends in process behaviour, each specific reactor run also shows its own characteristics. Until now, available modeling methods were unable to describe the observed between run variance. In this paper, we present a hierarchical modeling method to solve this problem. A case study for a semi-continuous operated polymer process is analysed to illustrate the hierarchical modeling approach. It was shown that, using the applied modeling method, it is possible to obtain a model which is robust over several reactor runs and which provides a tool for process analysis. Also, the hierarchical model was shown to be superior to a model which lacked an appropriate description of the differences between reactor runs. [Copyright &y& Elsevier]

Abstract: In this paper, we evaluate multivariate pattern matching methods for the Tennessee Eastman (TE) challenge process. The pattern matching methodology includes principal component analysis based similarity factors and dissimilarity factor of Kano et al., that compare current and historical data. In our similarity factor approach, the start and end times of disturbances are not known a priori and the data are compared by moving a window though the historical data. Comparisons with methods used in earlier case studies of the TE challenge process show advantages of using the proposed similarity factor approach. [Copyright &y& Elsevier]

Abstract: In this paper we present a novel method for the numerical solution of dynamic optimization problems. After obtaining a first solution at a coarse resolution of the control profiles with a direct sequential approach, the structure of the control profiles is analyzed for possible switching times and arcs. Subsequently, the problem is reformulated automatically and solved as a multi-stage problem, with each stage corresponding to a potential arc. Order and resolution of the control parameterization are adapted to the type of the particular arc. By means of some case studies we show that accurate solutions with only few degrees of freedom can be obtained. [Copyright &y& Elsevier]

The goal of this paper is to develop a discrete-time multivariable globally linearized control (GLC) algorithm, which provides low computational requirements with constraint handling ability. The control strategy is constructed with four elements: a transformer that accounts for process nonlinearities; an estimator, which observes the required unmeasured states; a variable constraint mapping optimizer that transforms the input constraints of the nonlinear process into constraints on the manipulated inputs of the globally linearized system and a quadratic dynamic matrix controller (QDMC) that provides constraints handling ability. The effectiveness of the designed controller has been tested on a multi-input multi-output (MIMO) nonlinear distillation column through extensive numerical simulations. The control law showed a high quality performance for set point tracking and disturbance rejection in presence of parametric uncertainty. The effect of unmeasured disturbance also has been studied through the simulation experiment. In the comparative study, the proposed GLC-QDMC control technique outperformed the GLC-DMC control law. [Copyright &y& Elsevier]

In this paper, discrete-time control schemes based on feedback linearization of serial gray-box models are considered for partially known nonlinear processes. These techniques combine the benefits of feedback linearization, neural networks, and serial gray-box modeling, which result in larger dynamic operating ranges, better extrapolation properties, and fewer data acquisition efforts in comparison with the corresponding black-box-based schemes. First-principles-based serial gray-box models are classified into invertible and non-invertible structures for training purposes, and an improved approximate feedback linearization scheme based on Taylor series terms of a non-affine gray-box model is proposed. Moreover, an affine gray-box model is developed for applying the exact feedback linearization scheme. Simulation results on a fermentation process show that the proposed methods yield significant improvement in modeling and control performance in comparison with that of the black-box feedback linearization schemes. [Copyright &y& Elsevier]

In this paper the disturbance model, used by MPC algorithms to achieve offset-free control, is optimally designed to enhance the robustness of single-model predictive controllers. The proposed methodology requires the off-line solution of a min-max optimization problem in which the disturbance model is chosen to guarantee the best closed-loop performance in the worst case of plant in a given uncertainty region. Application to a well-known ill-conditioned distillation column is presented to show that, for ill-conditioned processes, the use of a disturbance model that adds the correction term to the process inputs guarantees a robust performance, while the disturbance model that adds the correction term to the process outputs (used by industrial MPC algorithms) does not. [Copyright &y& Elsevier]