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Robust MPC based on polyhedral invariant sets for LPV systems

Robust MPC based on polyhedral invariant sets for LPV system...

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10th IFAC symposium on dynamics and control of process systems, DYCOPS 2013

作者： Kheawhom, Soorathep Bumroongsri, Pornchai Department of Chemical Engineering Faculty of Engineering Chulalongkorn University Bangkok 10330 Thailand Department of Chemical Engineering Faculty of Engineering Mahidol University Nakhon Pathom 73170 Thailand

ISBN: (纸本)9783902823595

A robust model predictive control (RMPC) using polyhedral invariant sets for linear parameter varying (LPV) systems is presented in this work. A sequence of state feedback gains associated with a sequence of nested polyhedral invariant sets is constructed off-line in order to reduce the computational burdens. At each control iteration, when the measured state lies between any two adjacent polyhedral invariant sets constructed, a state feedback gain is determined by interpolation of two pre-computed state feedback gains incorporated with scheduling parameters. Three interpolation algorithms are proposed. In the first algorithm, the real-time state feedback gain is determined by maximizing the state feedback gain with subjected to a set of constraints associated with current invariant set. In the second algorithm, the real-time state feedback gain is calculated by minimizing the violation of the constraints of the adjacent inner invariant set with subjected to a set of constraints associated with current invariant set. In the last algorithm, the real-time state feedback gain is obtaned by minimizing the upper bound of infinite horizon worst case performance cost, which is estimated by Lyapunov function at current state, with subjected to a set of constraints associated with current invariant set. The controller design is illustrated with a case study of nonlinear two-tank system. The simulation results showed that the proposed RMPC with interpolation provides a better control performance while on-line computation is still tractable as compared to previously reported algorithms. © IFAC.

关键词： Model predictive control

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Linear machine: A novel approach to point location problem

Linear machine: A novel approach to point location problem

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10th IFAC symposium on dynamics and control of process systems, DYCOPS 2013

作者： Airan, Astha Bhartiya, Sharad Bhushan, Mani Department of Chemical Engineering Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9783902823595

In recent literature, explicit model predictive control (e-MPC) has been proposed to facilitate implementation of the popular model predictive control (MPC) approach to fast dynamical systems. e-MPC is based on multi-parametric programming. The key idea in e-MPC is to replace the online optimization problem in MPC by a point location problem. After locating the current point, the control law is simply computed as an appropriate linear function of the states. A variety of approaches have been proposed in literature for the point location problem. In this work, we present a novel approach based on linear machines for solving this problem. Linear machines are widely used in multi-category pattern classification literature for developing linear classifiers given representative data from various classes. The idea in linear machines is to associate a linear discriminant function with each class. A given point is then assigned to the class with the largest discriminant function value. In this work, we develop an approach for identifying such discriminant functions from the hyperplanes characterizing the given regions as in multi-parametric programming. Apart from being an elegant solution to the point location problem as required in e-MPC, the proposed approach also links two apparently diverse fields namely e-MPC and multi-category pattern classification. To illustrate the utility of the approach, it is implemented on a hypothetical example as well as on a quadruple tank benchmark system taken from literature. © IFAC.

关键词： Model predictive control

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Derivative-free estimator based non-linear model predictive control of a boiler-turbine unit

Derivative-free estimator based non-linear model predictive ...

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10th IFAC symposium on dynamics and control of process systems, DYCOPS 2013

作者： Arasu, S. Kapil Prakash, J. Prasad, Vinay Department of Instrumentation Engineering Madras Institute of Technology Campus Anna University Chennai-44 India Department of Chemical and Materials Engineering University of Alberta Canada

ISBN: (纸本)9783902823595

In this work, the authors have proposed a constrained nonlinear model predictive control (NMPC) scheme for a boiler-turbine system. The proposed control scheme relies on the non-linear state space model proposed by Bell and Astrom, 1987 for prediction. A derivative-free Kalman filter has been designed to estimate the state variables of the boiler-turbine unit. These estimated values have been used as an initial condition for predicting the future states and outputs in the NMPC formulation. In order to account for model-plant mismatch and to achieve offset-free control, innovation based correction of predicted states and outputs as suggested by Ricker, 1990 has been incorporated in the proposed NMPC formulation. The extensive simulation studies show that the proposed control scheme effectively handles the input constraints of the boiler-turbine unit and meets the required electrical demand without requiring careful selection of operating points. © IFAC.

关键词： Kalman filters

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Alarm allocation for event-based process alarm systems

Alarm allocation for event-based process alarm systems

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10th IFAC symposium on dynamics and control of process systems, DYCOPS 2013

作者： Dalapatu, Pradeep Ahmed, Salim Khan, Faisal Faculty of Engineering and Applied Science Memorial University St. John's NL A1B 3X5 Canada

ISBN: (纸本)9783902823595

The ability to monitor large numbers of variables and the flexibility to assign alarms to each variable led to a substantial increase in the numbers of alarms in industrial plants. This, in turn, increased the numbers of false and redundant alarms. In plant operations, the numbers of annunciated alarms regularly exceed the acceptable rates that operators can handle. To reduce the number of assigned alarms, a risk-based alarm system has been proposed in the literature (Ahmed et al. (2011);Chang et al. (2011)) where alarms are assigned to groups of variables instead of individual variables. This articles explores the options for grouping variables for alarm allocation. Several grouping methods are discussed and an event-based grouping procedure is detailed. Selection of the key variables for a group is performed using the information that the variables can have to distinguish between an abnormal and a normal condition. The concept of mutual information is used to quantify the information. Variables with high information gain are grouped together for each respective abnormal event. To identify the redundant variables within the groups to further reduce the number of variables to be monitored, the maximum cross-correlation between pairs of key variables are used. A case study using the example of a continuous stirred tank reactor is used to demonstrate the methodology. © IFAC.

关键词： Alarm systems

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Reformulating real-time optimal feedback based on model uncertainty

Reformulating real-time optimal feedback based on model unce...

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10th IFAC symposium on dynamics and control of process systems, DYCOPS 2013

作者： Kamaraju, V.K. Chiu, M.S. Srinivasan, B. Dept. of Chemical and Biomolecular Engineering National University of Singapore Singapore 117576 Singapore Département de Génie Chimique École Polytechnique Montréal C.P. 6079 Succ. Centre-Ville Montréal QC H3C 3A7 Canada

ISBN: (纸本)9783902823595

Model Predictive control (MPC) and its first order approximation, the Neighboring Extremals (NE) have been used for real-time optimal control in the presence of model uncertainties for several decades. Traditionally, both MPC and NE would only correct for deviations in states considering the underlying model to be nominal - a procedure that is valid for additive disturbances. However, in the presence of model uncertainties, a simple illustrative example in this paper shows that such a MPC scheme or a NE controller could cause corrections in the wrong direction, thereby deteriorating performance. The paper, thus, addresses reformulating NE feedback considering sensitivities with respect to the model parameters. The feedback then has two components - one based on state deviations and the other based on parameter deviations. Note that this formulation also requires some primitive form of parameter estimation. The illustrative example shows the efficacy of this approach and the importance of incorporating the knowledge of parameter variations in real-time optimal control. © IFAC.

关键词： Model predictive control

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processing history dependent control parameter estimation in multi-step batch processes

Processing history dependent control parameter estimation in...

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10th IFAC symposium on dynamics and control of process systems, DYCOPS 2013

作者： Sim, Ye Seul Shin, Joohyun Lee, Hana Lee, Jay H. Chemical and Biomolecular Engineering Department Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9783902823595

In this study, we propose a method to estimate the parameters of a control model for a batch process by using previous batch data. We focus on the case of multistep batch processing, where appropriate control input values often exhibit strong dependency on the prior processing history of the feed (called "feed characteristics" hereafter), e.g., the equipment or operating conditions used in the previous processing steps. In such cases, it is a common practice to use the data from those previous batches with identical feed characteristics as the new batch. As batch operations become more complicated, however, the variety of feed characteristics is increased and consequently the chance of finding recent batch data with identical feed characteristics is reduced. To combat the shortage of usable data in this context, it is important to enable the utilization of not only data from batches of identical feed characteristics but also those from batches of "similar" feed characteristics. This paper attempts to address this need in a practical manner. By using MANOVA (multivariate analysis of variance), a popular statistical inference method, statistical similarities among the estimated parameter values for different feed characteristics can be evaluated and substitutable sets of the feed characteristics can be identified. Results from the statistical analysis can increase the amount and/or recency of the data used in the batch control input calculation. We suggest some specific rules for selecting among available previous batch data by considering both the feed characteristic similarity and time-immediacy. The proposed method has been tested on real manufacturing industrial data and the results showed practical viability and significant potentials of the method. © IFAC.

关键词： Parameter estimation

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Selection of control structure of elevated pressure air separation unit in an IGCC power plant for an economical operation

Selection of control structure of elevated pressure air sepa...

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10th IFAC symposium on dynamics and control of process systems, DYCOPS 2013

作者： Roh, Kosan Lee, Jay H. Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9783902823595

IGCC (Integrated Gasification Combined Cycle) is an alternative power generation system that can utilize fossil fuels in a more eco-friendly way than the conventional pulverized coal-fired plant. An IGCC plant requires an (Elevated Pressure) Air Separation Unit (EP ASU) that separates the air into pure oxygen and nitrogen, to be sent to the gasifier and the gas turbine, respectively. The ASU consumes about 10% of the gross power output generated in IGCC, so an economical operation of the ASU is important for lowering the overall power generation cost. In this research, controlled variable selection for an EP ASU is studied from the viewpoint of economics, i.e., with the objective of maintaining an economically (near-)optimal operation in the presence of load changes. Instead of the full-scale real-time optimization (RTO), we adopt a simpler approach known as self-optimizing control (SOC), which attempts to achieve the objective through a systematic selection of controlled variables. For the purpose of designing and testing a self-optimizing control structure, equation-based modeling of EP ASU is carried out using the software platform of gPROMS. Then, the SOC approach is applied based on the model to select the best set of controlled variables, which will lead to the most economical operation in the presence of load changes. Finally, PI control loops are designed and their dynamic control performances are tested. In addition, the economic loss in the presence of load changes is analyzed and compared with that achievable from the use of RTO. © IFAC.

关键词： Gas turbines

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A survey on sensitivity-based nonlinear model predictive control

A survey on sensitivity-based nonlinear model predictive con...

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10th IFAC symposium on dynamics and control of process systems, DYCOPS 2013

作者： Biegler, Lorenz T. Chemical Engineering Department Carnegie Mellon University Pittsburgh PA 15213 United States

ISBN: (纸本)9783902823595

This survey explores recent results in the development of optimization algorithms and formulations for moving horizon estimation (MHE), nonlinear model predictive control (NMPC) and dynamic real-time optimization (D-RTO), with extrapolative nonlinear dynamic (e.g., first principle) models. We consider Nonlinear Programming (NLP) and NLP sensitivity as natural tools for formulation and efficient solution of optimization problems for these three tasks. For MHE, we develop a maximum likelihood formulation that directly incorporates nonlinear models, and is seamlessly adapted to updating the arrival costs. This approach is also extended easily to M-estimators, which essentially eliminate biased estimates due to gross errors. For NMPC, we develop formulations that incorporate well-understood stability and robustness properties, even for large, complex plant models. Finally, recent work has extended these capabilities from setpoint tracking objectives to more general stage costs that are economically based, thus leading to a robustly stable strategy for D-RTO. In concert with these problem formulations, the realization of MHE, NMPC and D-RTO requires the application of a fast NLP solver for time-critical, on-line optimization, as well as efficient NLP sensitivity tools that eliminate computational delay, and guarantee stability and robustness. Algorithms that meet these demands are explored and outlined for these tasks. Finally, a number of challenging distillation case studies are presented that demonstrate the effectiveness of these optimization-based strategies. © IFAC.

关键词： Nonlinear programming

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Why risk-based multivariate fault detection and diagnosis?

Why risk-based multivariate fault detection and diagnosis?

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10th IFAC symposium on dynamics and control of process systems, DYCOPS 2013

作者： Zadakbar, O. Imtiaz, S. Khan, F. Process Engineering Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's NL A1B 3X5 Canada

ISBN: (纸本)9783902823595

A novel risk-based fault detection method has been developed. The proposed method provides a dynamic process risk indication based on the probability of happening a fault and its consequence. In this method instead of generating an alarm based on residuals or signals an alarm is activated only when the calculated risk of operation exceeds the acceptable threshold. This is an important concept as it can funnel the attention and effort of operators to the faults which poses the most operational or safety risk. Application of this new risk-based approach provides early warning of the fault as well as the associated risk with the fault. Methodologies were developed to apply the concept with model based fault detection algorithm as well as multivariate history based fault detection techniques. In this paper we show the model based approach by combining Kalman filter with the risk based approach. The history-based approach was demonstrated using principal component analysis (PCA). This method has more power in discerning between operational changes and abnormal conditions which have potential to cause accidents. © IFAC.

关键词： Fault detection

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A real-time initial-condition injection technique for implementation of discrete/continuous control-algorithms

A real-time initial-condition injection technique for implem...

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Southeastern symposium on System Theory

作者： C. D. Johnson Ashley Hunt Distinguished Professor of ECE ECE Department University of Alabama in Huntsville Huntsville AL USA

ISBN: (纸本)9781479900374

A generic signal-processing technique is described that enables computer-simulation studies, and practical hardware-implementation, of “real-time-determined” changes/updates in the “real-time initial-conditions” that must be “injected” into various real-time integration-operations comprising a Discrete/Continuous (D/C) control-Algorithm. An illustrative example is presented. The “initial-condition injection” technique introduced here is also applicable-to a broad-variety of computational or simulation applications involving the real-time imposition of a sequence of strategic "jump"-changes/updates of the evolving real-time values of various state-variables x i (t) associated with a dynamical system or process. For instance, such as those encountered-in the simulation of Newtonian dynamics associated-with “abrupt” changes in mass and/or “momentum” that are natural consequences of various kinds of elastic and non-elastic impacts, collisions, and mass-separations/mergings.

关键词： Real-time systems control systems control theory Computational modeling MIMO Educational institutions Mathematical model

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