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| 2. |
- Ebadat, Afrooz, et al.
(författare)
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Application Set Approximation in Optimal Input Design for Model Predictive Control
- 2014
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Ingår i: 2014 European Control Conference (ECC). - 9783952426913 ; , s. 744-749
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 3. |
- Larsson, Christian A., et al.
(författare)
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An application-oriented approach to dual control with excitation for closed-loop identification
- 2016
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Ingår i: European Journal of Control. - : Elsevier. - 0947-3580 .- 1435-5671. ; 29, s. 1-16
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Larsson, Christian A., 1983-
(författare)
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Application-oriented experiment design for industrial model predictive control
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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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| 5. |
- Larsson, Christian A., et al.
(författare)
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Experimental evaluation of model predictive control with excitation (MPC-X) on an industrial depropanizer
- 2015
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Ingår i: Journal of Process Control. - : Elsevier BV. - 0959-1524 .- 1873-2771. ; 31, s. 1-16
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- Larsson, Christian A., et al.
(författare)
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Generation of signals with specified second-order properties for constrained systems
- 2016
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Ingår i: International journal of adaptive control and signal processing (Print). - : Wiley. - 0890-6327 .- 1099-1115. ; 30:3, s. 456-472
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 7. |
- Larsson, Christian A., et al.
(författare)
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Identification of nonlinear systems using misspecified predictors
- 2010
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Ingår i: 49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC). - 9781424477463 ; , s. 7214-7219
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 8. |
- Larsson, Christian A., et al.
(författare)
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Model predictive control with integrated experiment design for output error systems
- 2013
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Ingår i: 2013 European Control Conference, ECC 2013. - : IEEE. - 9783033039629 ; , s. 3790-3795
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 9. |
- Larsson, Christian A., et al.
(författare)
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MPC Oriented Experiment Design
- 2011
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Ingår i: Proceedings of the 18th IFAC World Congress. - : IFAC Papers Online. ; , s. 9966-9971
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 10. |
- Larsson, Christian A., et al.
(författare)
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On Optimal Input Design for Model Predictive Control
- 2011
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Ingår i: Proceedings of the 49th IEEE Conference on Decision and Control. - : IEEE conference proceedings. ; , s. 805-810
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Konferensbidrag (refereegranskat)abstract
- 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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