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  • 1.
    Annergren, Mariette
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Kauven, D.
    Larsson, Christian A.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Potters, M. G.
    Tran, Q.
    Özkan, L.
    On the way to autonomous model predictive control: A distillation column simulation study2013In: 10th IFAC Symposium on Dynamics and Control of Process Systems, DYCOPS 2013, IFAC Secretariat , 2013, no PART 1, p. 713-720Conference paper (Refereed)
    Abstract [en]

    Model Predictive Control (MPC) is a powerful tool in the control of large scale chemical processes and has become the standard method for constrained multivariable control problems. Hence, the number of MPC applications is increasing steadily and it is being used in application domains other than petrochemical industries. A common observation by the industrial practitioners is that success of any MPC application requires not only efficient initial deployment but also maintenance of initial effectiveness. To this end, we propose a novel high level automated support strategy for MPC systems. Such a strategy consists of components such as performance monitoring, performance diagnosis, least costly closed loop experiment design, re-identification and autotuning. This work presents the novel technological developments in each component and demonstrates them on a distillation column case study. We show that automated support strategy restores nominal performance after a performance drop is detected and takes the right course of action depending on its cause.

  • 2.
    Annergren, Mariette
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Larsson, Christian A.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    MOOSE: A model based optimal input design toolbox2012In: 16th IFAC Symposium on System Identification, IFAC , 2012, no PART 1, p. 1535-1540Conference paper (Refereed)
    Abstract [en]

    MOOSE is a model based optimal input design toolbox developed for Matlab. The objective of the toolbox is to simplify the implementation of some optimal input design problems encountered in system identification. MOOSE provides an extra layer between the user and a convex optimization environment.

  • 3.
    Ebadat, Afrooz
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Annergren, Mariette
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Larsson, Christian A.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Wahlberg, Bo
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Molander, Mats
    Sjöberg, Johan
    Application Set Approximation in Optimal Input Design for Model Predictive Control2014In: 2014 European Control Conference (ECC), 2014, p. 744-749Conference paper (Refereed)
    Abstract [en]

    This contribution considers one central aspect of experiment design in system identification, namely application set approximation. When a control design is based on an estimated model, the achievable performance is related to the quality of the estimate. The degradation in control performance due to plant-modeling missmatch is quantified by an application cost function. A convex approximation of the set of models that satisfy the control specification is typically required in optimal input design. The standard approach is to use a quadratic approximation of the application cost function, where the main computational effort is to find the corresponding Hessian matrix. Our main contribution is an alternative approach for this problem, which uses the structure of the underlying optimal control problem to considerably reduce the computations needed to find the application set. This technique allows the use of applications oriented input design for MPC on much more complex plants. The approach is numerically evaluated on a distillation control problem.

  • 4.
    Ebadat, Afrooz
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Wahlberg, Bo
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hägg, Per
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Larsson, Christian R.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Applications Oriented Input Design in Time-Domain Through Cyclic Methods2014Conference paper (Refereed)
    Abstract [en]

    In this paper we propose a method for applications oriented input design for linear systems in open-loop under time-domain constraints on the amplitude of input and output signals. The method guarantees a desired control performance for the estimated model in minimum time, by imposing some lower bound on the information matrix. The problem is formulated as a time-domain optimization problem, which is non-convex. This is addressed through an alternating method, where we separate the problem into two steps and at each step we optimize the cost function with respect to one of two variables. We alternate between these two steps until convergence. A time recursive input design algorithm is performed, which enables us to use the algorithm with control. Therefore, a receding horizon framework is used to solve each optimization problem. Finally, we illustrate the method with a numerical example which shows the good ability of the proposed approach in generating an optimal input signal.

  • 5. Guidi, H.
    et al.
    Larsson, Christian
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Tran, Q. N.
    Ozkan, L.
    Backx, A. C. P. M.
    Autonomous maintenance of advanced process control: Application to an industrial depropanizer2014In: Fuels and Petrochemicals Division 2014 - Core Programming Area at the 2014 AIChE Spring Meeting and 10th Global Congress on Process Safety, AIChE , 2014, Vol. 2, p. 923-932Conference paper (Refereed)
    Abstract [en]

    Although Model Predictive Control (MPC) has been widely accepted as a main technology for Advanced Process Control (APC) due to its ability of operating the system closely to the constraints, proper maintenance of MPC systems is still a challenge. Based on this observation, this research aims to develop an automated support strategy for the autonomous maintenance of MPC. In this work, re-tuning and re-identification components of the automated support strategy are considered as corrective action to retain the performance of the system after a change in the plant dynamics causes performance degradation. An industrial FT-depropanizer is used to test the implementation of these components. Results successfully show that an automated unified framework approach to MPC maintenance can successfully be used in further securing the economic leverage of MPC in industry.

  • 6.
    Hägg, Per
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Larsson, Christian
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Ebadat, Afrooz
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Wahlberg, Bo
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Input Signal Generation for Constrained Multiple-Input Multple-Output Systems2014In: Proceedings of 19th World Congress of the International Federation of Automatic Control 2014, IFAC , 2014Conference paper (Refereed)
    Abstract [en]

    In this paper we extend a recent method for generating an input signal with a desired auto-correlation function while satisfying both input and output constraints for the system it is to be applied to. This is an important problem in system identication, firstly, the properties of the identified model is highly dependent on the used excitation signal during the experiment and secondly, on real processes, due to actuator saturation and safety considerations, it is important to constraint the input and output from the process. The proposed method corresponds to a nonlinear model predictive control problem and here we extend it to consider general input horizons and also to the multiple-input multiple-output case. In general this corresponds to solving a multivariate polynomial of order four in each time step. Here two dierent methods for solving this problem are considered: one based on convex relaxation and one based on a cyclic algorithm. The performance of the algorithm is successfully veried by simulations and the implications of dierent lengths of the input horizon is discussed.

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  • 7.
    Hägg, Per
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Larsson, Christian
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Robust and Adaptive Excitation Signal Generation for Input and Output Constrained Systems2013In: 2013 European Control Conference, ECC 2013, IEEE , 2013, p. 1416-1421Conference paper (Refereed)
    Abstract [en]

    Generating signals with a prespecified autocorrelation, available from some input design, while satisfying constraints on input and output signals of the systems which the signal is to be applied on is an important problem in system identification. This paper extends a recently proposed method for such signal generation. The method is modified to be robust to uncertainties in the system model. An adaptive formulation is also given which allows for improving the input design online. The adaptive method is compared to the true optimal input design on a simulation example. The robustness properties of the method are illustrated on an experimental setup.

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  • 8.
    Larsson, Christian
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Toward applications oriented optimal input design with focus on model predictive control2011Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Modern control designs are, with few exceptions, in some way model based. In particular, predictive control has rapidly become a popular control strategy, implemented in a large number of industrial plants. Model predictive control (MPC) uses a model to predict the impact of future control inputs on the controlled plant. The quality of the model can have a large impact on the achievable control performance. It is widely reported that modeling is the single most time and cost consuming part of the commissioning of an industrial MPC and therefore an important research issue.

    This thesis addresses the need for good modeling for MPC by introducing an optimal input design and identification method tailored to the specifics of predictive control. Parametric models are used and the influence of the individual parameters on the control performance is measured through a cost function. This leads to a set of parameters that are deemed acceptable. Optimal input design is used to ensure, with high probability, that the estimated parameters are in the set of acceptable parameters while keeping experimental cost low. It is shown that optimal input design can lead to a significant reduction of the experimental cost while still guaranteeing acceptable control performance. A toolbox for optimal input design in Matlab is also presented.

    Real world systems tend to be nonlinear and sometimes it is necessary to model them as such. Input design for two types of nonlinear systems with finite memory is considered. Similarities and differences compared to the linear case are pointed out and exploited. Convex formulations of the optimal input design problem are presented. It is shown by example that the resulting optimal design can differ greatly compared to designs for linear models.

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  • 9.
    Larsson, Christian A.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Application-oriented experiment design for industrial model predictive control2014Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Advanced process control and its prevalent enabling technology, model predictive control (MPC), can today be regarded as the industry best practice for optimizing production. The strength of MPC comes from the ability to predict the impact of disturbances and counteract their effects with control actions, and from the ability to account for constraints. These capabilities come from the use of models of the controlled process. However, relying on a model is also a weakness of MPC.The model used by the controller needs to be kept up to date with changing process conditions for good MPC performance. In this thesis, the problem of closed-loop system identification of models intended to be used in MPC is considered.

    The design of the identification experiment influences the quality and properties of the estimated model. In the thesis, an application-oriented framework for designing the identification experiment is used. The specifics of experiment design for identification of models for MPC are discussed. In particular, including constraints in the controllerresults in a nonlinear control law, which complicates the experiment design.

    The application-oriented experiment design problem with time-domain constraints is formulated as an optimal control problem, which in general is diffcult to solve. Using Markov decision theory, the experiment design problem is formulated for finite state and action spaces and solved using an extension of existing linear programming techniques for constrained Markov decision processes. The method applies to general noise and disturbance structures but is computationally intensive. Two extensions of MPC with dual control properties which implement the application-oriented experiment design idea are developed. These controllers are limited to output error systems but require less computations. Furthermore, since the controllers are based on a common MPC technique, they can be used as extensions of already available MPC implementations. One of the developed controllers is tested in an extensive experimental validation campaign, which is the first time that MPC with dual propertiesis applied to a full scale industrial process during regular operation of the plant.

    Existing experiment design procedures are most often formulated in the frequency domain and the spectrum of the input is used as the design variable. Therefore, a realization of the signal with the right spectrum has to be generated. This is not straightforward for systems operating under constraints. In the thesis, a framework for generating signals, with prespecified spectral properties, that respect system constraints is developed. The framework uses ideas from stochastic MPC and scenario optimization. Convergence to the desired autocorrelation is proved for a special case and the merits of the algorithm are illustrated in a series of simulation examples.

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    LarssonThesis
  • 10.
    Larsson, Christian A.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Annergren, Mariette J.E.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    On Optimal Input Design for Model Predictive Control2011In: Proceedings of the 49th IEEE Conference on Decision and Control, IEEE conference proceedings, 2011, p. 805-810Conference paper (Refereed)
    Abstract [en]

    This paper considers a method for optimal inputdesign in system identification for control. The approachaddresses model predictive control (MPC). The objective ofthe framework is to provide the user with a model whichguarantees that a specified control performance is achieved,with a given probability. We see that, even though the systemis nonlinear, using linear theory in the input design can reducethe experimental effort. The method is illustrated in a minimumpower input signal design in system identification of a watertank system.

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  • 11.
    Larsson, Christian A.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Ebadat, Afrooz
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Bombois, Xavier
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control.
    An application-oriented approach to dual control with excitation for closed-loop identification2016In: European Journal of Control, ISSN 0947-3580, E-ISSN 1435-5671, Vol. 29, p. 1-16Article in journal (Refereed)
    Abstract [en]

    Identification of systems operating in closed loop is an important problem in industrial applications, where model-based control is used to an increasing extent. For model-based controllers, plant changes over time eventually result in a mismatch between the dynamics of any initial model in the controller and the actual plant dynamics. When the mismatch becomes too large, control performance suffers and it becomes necessary to re-identify the plant to restore performance. Often the available data are not informative enough when the identification is performed in closed loop and extra excitation needs to be injected. This paper considers the problem of generating such excitation with the least possible disruption to the normal operations of the plant. The methods explicitly take time domain constraints into account. The formulation leads to optimal control problems which are in general very difficult optimization problems. Computationally tractable solutions based on Markov decision processes and model predictive control are presented. The performance of the suggested algorithms is illustrated in two simulation examples comparing the novel methods and algorithms available in the literature.

  • 12.
    Larsson, Christian A.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Geerardyn, Egon
    Department ELEC, Vrije Universiteit Brussels.
    Schoukens, Johan
    Department ELEC, Vrije Universiteit Brussels.
    Robust Input Design for Resonant Systems Under Limited A Priori Information2012In: 16th IFAC Symposium on System Identification, IFAC , 2012, p. 1611-1616Conference paper (Refereed)
    Abstract [en]

    Optimal input design typically depends on the unknown system parameters that need to be identifed. In this paper we consider robust input design for resonant systems that may span over a large frequency band. The concept is to use classical D-optimal design combined with a robust excitation signal which guarantees the same estimate variance regardless of resonance frequency. Simulations show that the proposed signal has the desired properties.

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  • 13.
    Larsson, Christian A.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Identification of nonlinear systems using misspecified predictors2010In: 49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2010, p. 7214-7219Conference paper (Refereed)
    Abstract [en]

    Identification of nonlinear systems is an important albeit difficult task. This work considers parameter estimation, using the prediction error method, of the class of models that fit into a nonlinear state space formulation. Finding the optimal predictor for such nonlinear models, if at all possible, often requires significant effort. As an alternative, techniques from indirect inference are used to circumvent this problem. A misspecified predictor, parameterized by a new set of parameters, is used in lieu of the optimal predictor. These new parameters are found numerically by using simulations of the model to be identified. The proposed method is applied to simulation examples and real process data with encouraging results.

  • 14.
    Larsson, Christian A.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    On optimal input design for nonlinear FIR-type systems2010In: 49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), New York: IEEE , 2010, p. 7220-7225Conference paper (Refereed)
    Abstract [en]

    We consider optimal input design for system identification of nonlinear FIR-type systems in the prediction error (PEM) framework. The input sequences are designed in terms of their statistical properties and not directly in time domain. The starting point is the asymptotic properties of PEM estimates. The fact that the inverse covariance matrix of the estimated parameters is linear in the input probability density function is exploited to formulate convex optimization problems. The main issues considered are the parameterization of the input pdf, reduction of the number of free variables in the optimization and to some extent signal generation. Two special model classes where tractable problems are obtainable are studied in detail. Convex formulations of the input design problem are presented for the static nonlinear and nonlinear FIR cases. Numerical examples of the discussed ideas are also presented.

  • 15.
    Larsson, Christian A.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Bombois, Xavier
    Delft University of Technology.
    Mesbah, Ali
    Delft University of Technology.
    Modén, Per
    ABB.
    Model predictive control with integrated experiment design for output error systems2013In: 2013 European Control Conference, ECC 2013, IEEE , 2013, p. 3790-3795Conference paper (Refereed)
    Abstract [en]

    Model predictive control has become an increasingly popular control strategy thanks to the ability to handle constrained systems. Obtaining the required models through system identification is often a time consuming and costly process. Applications oriented experiment design is a means of reducing this effort but is often formulated in terms of the input's spectral properties. Therefore, time domain constraints are difficult to enforce. In this contribution we combine MPC with experiment design to formulate a control problem where excitation constraints are included. The benefits are that time domain constraints are respected while the experiment design criteria are fulfilled. The method is evaluated on a numerical example.

  • 16.
    Larsson, Christian A.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hägg, Per
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Generation of signals with specified second-order properties for constrained systems2016In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 30, no 3, p. 456-472Article in journal (Refereed)
    Abstract [en]

    This contribution considers the problem of realizing an input signal with a desired autocorrelation sequence satisfying both input and output constraints for the system it is to be applied to. This is an important problem in system identification, firstly, because the quality and accuracy of the identified model are highly dependent on the excitation signal used during the experiment and secondly, because on real processes, it is often important to constrain the input and output of the process because of actuator saturation and safety considerations. The signal generation is formulated as a model predictive controller with probabilistic constraints to make the algorithm robust to model uncertainties and process noise. The corresponding optimization problem is then solved with tools from scenario-based stochastic optimization. To reduce the model uncertainties, the method is made adaptive where a new model of the system and its uncertainties are reidentified. The algorithm is successfully applied to a simulation example and in a practical experiment for the identification of a quadruple tank lab process.

  • 17.
    Larsson, Christian A.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    MPC Oriented Experiment Design2011In: Proceedings of the 18th IFAC World Congress / [ed] Bittanti, Sergio, Cenedese, Angelo, Zampieri, Sandro, IFAC Papers Online, 2011, p. 9966-9971Conference paper (Refereed)
    Abstract [en]

    In this contribution we outline an experiment procedure tailored for Model Predictive Control (MPC). The design criterion takes the MPC criterion into account explicitly. The Scenario Approach is used to handle the fact that there is no explicit expression for the MPC criterion nor to the performance degradation due to the use of an estimated model (due to the constraints). The approach is illustrated on a railcar example.

  • 18.
    Larsson, Christian A.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Rojas, Cristián R.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Bombois, X.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Experimental evaluation of model predictive control with excitation (MPC-X) on an industrial depropanizer2015In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 31, p. 1-16Article in journal (Refereed)
    Abstract [en]

    It is commonly observed that over the lifetime of most model predictive controllers, the achieved performance degrades over time. This effect can often be attributed to the fact that the dynamics of the controlled plant change as the plant ages, due to wear and tear, refurbishment and design changes of the plant, to name a few factors. These changes mean that re-identification is necessary to restore the desired performance of the controller. An extension of existing predictive controllers, capable of producing signals suitable for closed loop re-identification, is presented in this article. The main contribution is an extensive experimental evaluation of the proposed controller for closed loop re-identification on an industrial depropanizer distillation column in simulations and in real experiments. The plant experiments are conducted on the depropanizer during normal plant operations. In the simulations, as well as in the experiments, the updated models from closed loop re-identification result in improvement of the performance. The algorithm used combines regular model predictive control with ideas from applications oriented input design and linear matrix inequality based convex relaxation techniques. Even though the experiments show promising result, some implementation problems arise and are discussed.

  • 19.
    Larsson, Christian
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hägg, Per
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Generation of excitation signals with prescribed autocorrelation for input and output constrained systems2013In: 2013 American Control Conference (ACC), American Automatic Control Council , 2013, p. 3918-3923Conference paper (Refereed)
    Abstract [en]

    This paper considers the problem of realizing an input signal with a desired autocorrelation sequence satisfying both input and output constraints for the system it is to be applied to. This is a important problem in system identification. Firstly, the properties of the identified model are highly dependent on the used excitation signal during the experiment and secondly, on real processes, due to actuator saturation and safety considerations, it is important to constrain the inputs and outputs of the process. The proposed method is formulated as a nonlinear model predictive control problem. In general this corresponds to solving a non-convex optimization problem. Here we show how this can be solved in one particular case. For this special case convergence is established for generation of pseudo-white noise. The performance of the algorithm is successfully verified by simulations for a few different autocorrelation sequences, with and without input and output constraints.

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