| 1. |
- 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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| 2. |
- Ebadat, Afrooz, et al.
(författare)
-
Applications Oriented Input Design for Closed-Loop System Identification : a Graph-Theory Approach
- 2014
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Ingår i: 2014 IEEE 53RD ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC). - : IEEE. - 9781467360906 ; , s. 4125-4130
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Konferensbidrag (refereegranskat)abstract
- A new approach to experimental design for identification of closed-loop models is presented. The method considers the design of an experiment by minimizing experimental cost, subject to probabilistic bounds on the input and output signals, and quality constraints on the identified model. The input and output bounds are common in many industrial processes due to physical limitations of actuators. The aforementioned constraints make the problem non-convex. By assuming that the experiment is a realization of a stationary process with finite memory and finite alphabet, we use results from graph-theory to relax the problem. The key feature of this approach is that the problem becomes convex even for non-linear feedback systems. A numerical example shows that the proposed technique is an attractive alternative for closed-loop system identification.
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| 3. |
- Ebadat, Afrooz, et al.
(författare)
-
Applications Oriented Input Design in Time-Domain Through Cyclic Methods
- 2014
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 4. |
- Ebadat, Afrooz, et al.
(författare)
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Blind identification strategies for room occupancy estimation
- 2015
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Ingår i: 2015 European Control Conference (ECC). - Piscataway, NJ : IEEE Communications Society. - 9783952426937 ; , s. 1315-1320
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Konferensbidrag (refereegranskat)abstract
- We propose and test on real data a two-tier estimation strategy for inferring occupancy levels from measurements of CO2 concentration and temperature levels. The first tier is a blind identification step, based either on a frequentist Maximum Likelihood method, implemented using non-linear optimization, or on a Bayesian marginal likelihood method, implemented using a dedicated Expectation-Maximization algorithm. The second tier resolves the ambiguity of the unknown multiplicative factor, and returns the final estimate of the occupancy levels. The overall procedure addresses some practical issues of existing occupancy estimation strategies. More specifically, first it does not require the installation of special hardware, since it uses measurements that are typically available in many buildings. Second, it does not require apriori knowledge on the physical parameters of the building, since it performs system identification steps. Third, it does not require pilot data containing measured real occupancy patterns (i.e., physically counting people for some periods, a typically expensive and time consuming step), since the identification steps are blind.
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| 5. |
- Ebadat, Afrooz, 1986-
(författare)
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Experiment Design for Closed-loop System Identification with Applications in Model Predictive Control and Occupancy Estimation
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The objective of this thesis is to develop algorithms for application-oriented input design. This procedure takes the model application into account when designing experiments for system identification.This thesis is divided into two parts. The first part considers the theory of application-oriented input design, with special attention to Model Predictive Control (MPC). We start by studying how to find a convex approximation of the set of models that result in acceptable control performance using analytical methods when controllers with no closed-form control law, for e.g., MPC are employed. The application-oriented input design is formulated in time domain to enable handling of signals constraints. The framework is extended to closed-loop systems where two cases are considered i.e., when the plant is controlled by a general but known controller and for the case of MPC. To this end, an external stationary signal is designed via graph theory. Different sources of uncertainty in application-oriented input design are investigated and a robust application-oriented input design framework is proposed.The second part of this thesis is devoted to the problem of estimating the number of occupants based on the information available to HVAC systems in buildings. The occupancy estimation is first formulated as a two-tier problem. In the first tier, the room dynamic is identified using temporary measurements of occupancy. In the second tier, the identified model is employed to formulate the problem as a fused-lasso problem. The proposed method is further developed to be used as a multi-room estimator using a physics-based model. However, since it is not always possible to collect measurements of occupancy, we proceed by proposing a blind identification algorithm which estimates the room dynamic and occupancy, simultaneously. Finally, the application-oriented input design framework is employed to collect data that is informative enough for occupancy estimation purposes.
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| 6. |
- Ebadat, Afrooz, et al.
(författare)
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Multi-room occupancy estimation through adaptive gray-box models
- 2015
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Ingår i: IEEE 54th Annual Conference on Decision and Control (CDC). - Piscataway, NJ : IEEE Communications Society. - 9781479978861 ; , s. 3705-3711, s. 3705-3711
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Konferensbidrag (refereegranskat)abstract
- We consider the problem of estimating the occupancy level in buildings using indirect information such as CO2 concentrations and ventilation levels. We assume that one of the rooms is temporarily equipped with a device measuring the occupancy. Using the collected data, we identify a gray-box model whose parameters carry information about the structural characteristics of the room. Exploiting the knowledge of the same type of structural characteristics of the other rooms in the building, we adjust the gray-box model to capture the CO2 dynamics of the other rooms. Then the occupancy estimators are designed using a regularized deconvolution approach which aims at estimating the occupancy pattern that best explains the observed CO2 dynamics. We evaluate the proposed scheme through extensive simulation using a commercial software tool, IDA-ICE, for dynamic building simulation.
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| 7. |
- Hägg, Per, et al.
(författare)
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Input Signal Generation for Constrained Multiple-Input Multple-Output Systems
- 2014
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Ingår i: Proceedings of 19th World Congress of the International Federation of Automatic Control 2014. - : IFAC.
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Konferensbidrag (refereegranskat)abstract
- 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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| 8. |
- 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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| 9. |
- Trollberg, Olle, 1984-, et al.
(författare)
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On optimization of paper machines using economic model predictive control
- 2018
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Ingår i: Paper Conference and Trade Show, PaperCon 2018. - : TAPPI Press. - 9781510871892 ; , s. 286-293
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Konferensbidrag (refereegranskat)abstract
- In this paper we consider applying economic model predictive control (EMPC) for economic optimization of a paper machine. EMPC is used to optimize overall process targets, e.g., the economy, directly in the control layer. The basic idea in EMPC is that by combining a dynamic process-model with an economic model, it is possible to predict and optimize the future economic outcome with respect to the manipulated process variables. Periodically solving such an optimization problem with updated information from measurements corresponds to a feedback controller. The results presented here are based on simulations, using a grey-box model with parameters estimated from real data, that reveal that EMPC may improve several aspects of the economic performance of a paper machine. First, EMPC may automatically prioritize among an excessive number of inputs to determine which combinations of inputs to use in order to counter disturbances in the most economically efficient manner. Also, since EMPC makes use of dynamic optimization, it may utilize control inputs with zero steady-state gain which are not used for traditional set-point tracking. Second, since EMPC is predictive in nature, it may plan ahead and prepare the process for known changes such as grade-changes, hence reducing the transition-time with a significant reduction in production loss, and thereby significant improvements in profitability, especially for machines where grade-changes are frequent. Finally, we note that EMPC typically operates the process with constraints active, as is typical for economic optimization problems in general. This may cause problems with robustness since even small exogenous disturbances or unmodelled dynamics may cause constraint violations. We therefore suggest using an adaptive approach where a constraint margin is introduced in the EMPC optimization problem to ensure that the operating point is backed off from the actual constraints relevant for production, thereby improving the robustness.
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