| 1. |
- Abdalmoaty, Mohamed R., 1986-, et al.
(författare)
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Identification of a Class of Nonlinear Dynamical Networks⁎
- 2018
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Ingår i: IFAC-PapersOnLine. - : Elsevier B.V.. - 2405-8963. ; 51:15, s. 868-873
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Tidskriftsartikel (refereegranskat)abstract
- Identification of dynamic networks has attracted considerable interest recently. So far the main focus has been on linear time-invariant networks. Meanwhile, most real-life systems exhibit nonlinear behaviors; consider, for example, two stochastic linear time-invariant systems connected in series, each of which has a nonlinearity at its output. The estimation problem in this case is recognized to be challenging, due to the analytical intractability of both the likelihood function and the optimal one-step ahead predictors of the measured nodes. In this contribution, we introduce a relatively simple prediction error method that may be used for the estimation of nonlinear dynamical networks. The estimator is defined using a deterministic predictor that is nonlinear in the known signals. The estimation problem can be defined using closed-form analytical expressions in several non-trivial cases, and Monte Carlo approximations are not necessarily required. We show, that this is the case for some block-oriented networks with no feedback loops and where all the nonlinear modules are polynomials. Consequently, the proposed method can be applied in situations considered challenging by current approaches. The performance of the estimation method is illustrated on a numerical simulation example.
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| 2. |
- 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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| 3. |
- Valenzuela, Patricio E., et al.
(författare)
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Analysis of averages over distributions of Markov processes
- 2018
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Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 98, s. 354-357
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Tidskriftsartikel (refereegranskat)abstract
- In problems of optimal control of Markov decision processes and optimal design of experiments, the occupation measure of a Markov process is designed in order to maximize a specific reward function. When the memory of such a process is too long, or the process is non-Markovian but mixing, it makes sense to approximate it by that of a shorter memory Markov process. This note provides a specific bound for the approximation error introduced in these schemes. The derived bound is then applied to the proposed solution of a recently introduced approach to optimal input design for nonlinear systems.
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