| 1. |
- Atta, Khalid, et al.
(författare)
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Adaptive amplitude fast proportional integral phasor extremum seeking control for a class of nonlinear system
- 2019
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Ingår i: Journal of Process Control. - : Elsevier. - 0959-1524 .- 1873-2771. ; 83, s. 147-154
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present a modification of the fast phasor extremum-seeking control for the fast optimization of a class of Wiener-Hammerstein nonlinear dynamical systems. The proposed technique provides a significant improvement of the closed-loop system's performance. This study introduces a new adaptive amplitude technique that is used to adaptively adjust the perturbation amplitude to a small predetermined value in a neighbourhood of the system's unknown optimal equilibrium. An analysis of the system demonstrates that semi-global practical stability analysis of the overall system to the unknown optimum is achieved. The effectiveness of the proposed approach is illustrated using numerical examples. The approach is also implemented for the optimal operation of a lean burn combustion system.
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| 2. |
- Castaño Arranz, Miguel, et al.
(författare)
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On the selection of control configurations for uncertain systems using gramian-based Interaction Measures
- 2016
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Ingår i: Journal of Process Control. - : Elsevier BV. - 0959-1524 .- 1873-2771. ; 47, s. 213-225
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Tidskriftsartikel (refereegranskat)abstract
- critical step in the control design of industrial processes is the Control Configuration Selection (CCS), where each actuator is associated with a set of measurements which will be used in the calculation of the control action.A possible solution to the CCS problem is given by the gramian-based Interaction Measures (IMs), which are derived from nominal process models. This paper introduces the derivation of uncertainty bounds for a gramian-based IM using models with uncertainty described in multiplicative form. An alternative to this model-based approach is presented, where uncertainty bounds are estimated from a tailored experiment.In addition, a procedure for robust CCS is introduced. This procedure integrates the calculated uncertainty bounds in the design of the control configuration.
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| 3. |
- Ebadat, Afrooz, et al.
(författare)
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Model Predictive Control oriented experiment design for system identification : A graph theoretical approach
- 2017
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Ingår i: Journal of Process Control. - : ELSEVIER SCI LTD. - 0959-1524 .- 1873-2771. ; 52, s. 75-84
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Tidskriftsartikel (refereegranskat)abstract
- We present a new approach to Model Predictive Control (MPC) oriented experiment design for the identification of systems operating in closed-loop. The method considers the design of an experiment by minimizing the experimental cost, subject to probabilistic bounds on the input and output signals due to physical limitations of actuators, and quality constraints on the identified model. The excitation is done by intentionally adding a disturbance to the loop. We then design the external excitation to achieve the minimum experimental effort while we are also taking care of the tracking performance of MPC. The stability of the closed-loop system is guaranteed by employing robust MPC during the experiment. The problem is then defined as an optimization problem. However, the aforementioned constraints result in a non-convex optimization which is relaxed by using results from graph theory. The proposed technique is evaluated through a numerical example showing that it is an attractive alternative for closed-loop experiment design.
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| 4. |
- Gajjar, Shriram, et al.
(författare)
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Real-time fault detection and diagnosis using sparse principal component analysis
- 2018
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Ingår i: Journal of Process Control. - : Elsevier. - 0959-1524 .- 1873-2771. ; 67, s. 112-128
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Tidskriftsartikel (refereegranskat)abstract
- With the emergence of smart factories, large volumes of process data are collected and stored at high sampling rates for improved energy efficiency, process monitoring and sustainability. The data collected in the course of enterprise-wide operations consists of information from broadly deployed sensors and other control equipment. Interpreting such large volumes of data with limited workforce is becoming an increasingly common challenge. Principal component analysis (PCA) is a widely accepted procedure for summarizing data while minimizing information loss. It does so by finding new variables, the principal components (PCs) that are linear combinations of the original variables in the dataset. However, interpreting PCs obtained from many variables from a large dataset is often challenging, especially in the context of fault detection and diagnosis studies. Sparse principal component analysis (SPCA) is a relatively recent technique proposed for producing PCs with sparse loadings via variance-sparsity trade-off. Using SPCA, some of the loadings on PCs can be restricted to zero. In this paper, we introduce a method to select the number of non-zero loadings in each PC while using SPCA. The proposed approach considerably improves the interpretability of PCs while minimizing the loss of total variance explained. Furthermore, we compare the performance of PCA- and SPCA-based techniques for fault detection and fault diagnosis. The key features of the methodology are assessed through a synthetic example and a comparative study of the benchmark Tennessee Eastman process.
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| 5. |
- Garpinger, Olof, et al.
(författare)
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Software-based optimal PID design with robustness and noise sensitivity constraints
- 2015
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Ingår i: Journal of Process Control. - : Elsevier BV. - 1873-2771 .- 0959-1524. ; 33:9, s. 90-101
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Tidskriftsartikel (refereegranskat)abstract
- Even though PID control has been available for a long time, there are still no tuning methods including derivative action that have gained wide acceptance in industry. Also, there is still no general consensus for when one should use PID, PI or even I control on a process. The focus of this article is to present a new method for optimal PID control design that automatically picks the best controller type for the process at hand. The proposed PID design procedure uses a software-based method to find controllers with optimal or near optimal load disturbance response subject to robustness and noise sensitivity constraints. It is shown that the optimal controller type depends on maximum allowed noise sensitivity as well as process dynamics. The design procedure thus results in a set of PID, PI and I controllers with different noise filters that the user can switch between to reach an acceptable control signal activity. The software is also used to compare PI and PID control performance with equivalent noise sensitivity and robustness over a large batch of processes representative for the process industry. This can be used to show how much a particular process benefits from using the derivative part.
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| 6. |
- Guzmán, José Luis, et al.
(författare)
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Performance indices for feedforward control
- 2015
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Ingår i: Journal of Process Control. - : Elsevier BV. - 1873-2771 .- 0959-1524. ; 26, s. 26-34
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, a performance benchmark for the assessment of two feedforward control architectures for the load disturbance compensation problem is proposed. In particular, two indices are devised so that the advantage of using a feedforward compensator with respect to the use of a feedback controller only is quantified. Furthermore, these metrics will help to make quantitative comparisons among different feedforward control schemes and tuning rules. Analysis and simulation results are given to demonstrate the effectiveness of the proposed approach.
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| 7. |
- Holmqvist, Anders, et al.
(författare)
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Open-loop optimal control of batch chromatographic separation processes using direct collocation
- 2016
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Ingår i: Journal of Process Control. - : Elsevier BV. - 0959-1524. ; 46, s. 55-74
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Tidskriftsartikel (refereegranskat)abstract
- This contribution presents a novel model-based methodology for open-loop optimal control of batch high-pressure liquid chromatographic (HPLC) separation processes. The framework allows for simultaneous optimization of target component recovery yield and production rate with respect to a parameterization of the input elution trajectory and fractionating interval endpoints. The proposed methodology implies formulating and solving a large-scale dynamic optimization problem (DOP) constrained by partial differential equations (PDEs) governing the multi-component system dynamics. It is based on a simultaneous method where both the control and state variables are fully discretized in the temporal domain, using direct local collocation on finite elements, and the state variables are discretized in the spatial domain, using an adaptive finite volume weighted essentially non-oscillatory (WENO) scheme. The direct transcription of the DOP described by Modelica, and its extension Optimica, code into a sparse nonlinear programming problem (NLP) is thoroughly presented. The NLP was subsequently solved using CasADi's (Computer algebra system with Automatic Differentiation) interface to the primal-dual interior point method IPOPT. The advantages of the open-loop optimal control strategy are highlighted through the solution of a challenging ternary complex mixture separation problem of human insulin analogs, with the intermediately eluting component as the target, for a hydrophobic interaction chromatography system. Moreover, the high intercorrelation between the shape of the optimal elution trajectories and the fractionation interval endpoints is thoroughly investigated. It is also demonstrated that the direct transcription methodology enabled accurate and efficient computation of optimal cyclic-steady-state solutions, which govern that state and control variables conform to periodicity constraints imposed on column re-generation and re-equilibration. By these means, the generic methods and tools developed here are applicable to continuous chromatographic separation technologies, including the continuous simulated moving bed (SMB) and the multicolumn counter-current solvent gradient purification (MCSGP) process.
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| 8. |
- Ioli, D., et al.
(författare)
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A compositional modeling framework for the optimal energy management of a district network
- 2019
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Ingår i: Journal of Process Control. - : Elsevier Ltd. - 0959-1524 .- 1873-2771. ; 74, s. 160-176
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a compositional modeling framework for the optimal energy management of a district network. The focus is on cooling of buildings, which can possibly share resources to the purpose of reducing maintenance costs and using devices at their maximal efficiency. Components of the network are described in terms of energy fluxes and combined via energy balance equations. Disturbances are accounted for as well, through their contribution in terms of energy. Different district configurations can be built, and the dimension and complexity of the resulting model will depend both on the number and type of components and on the adopted disturbance description. Control inputs are available to efficiently operate and coordinate the district components, thus enabling energy management strategies to minimize the electrical energy costs or track some consumption profile agreed with the main grid operator.
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| 9. |
- Isaksson, Alf, et al.
(författare)
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Using horizon estimation and nonlinear optimization for grey-box identification
- 2015
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Ingår i: Journal of Process Control. - : Elsevier. - 0959-1524 .- 1873-2771. ; 30, s. 69-79
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Tidskriftsartikel (refereegranskat)abstract
- An established method for grey-box identification is to use maximum-likelihood estimation for the nonlinear case implemented via extended Kalman filtering. In applications of (nonlinear) model predictive control a more and more common approach for the state estimation is to use moving horizon estimation, which employs (nonlinear) optimization directly on a model for a whole batch of data. This paper shows that, in the linear case, horizon estimation may also be used for joint parameter estimation and state estimation, as long as a bias correction based on the Kalman filter is included. For the nonlinear case two special cases are presented where the bias correction can be determined without approximation. A procedure how to approximate the bias correction for general nonlinear systems is also outlined. (C) 2015 Elsevier Ltd. All rights reserved.
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| 10. |
- 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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