On dealing with measured disturbances in the modifier adaptation method for real-time optimization

Navia, Daniel; Puen, Antonio; Quintanilla, Paulina; Briceño, Luis; Bergh, L.

doi:10.1016/j.compchemeng.2019.06.004

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…Hybrid plants are real processes with some simulated components. They have been used in metallurgical processes as an alternative to validate the performance of supervisory algorithms in more realistic situations than the ones given by computational experiments (Bergh and Yianatos, 2014;Navia et al, 2019Navia et al, , 2016. The concept of hybrid plants is based on the fact that the essential phenomena occurring in a real process can be divided into two aspects: fluid dynamics and physicochemical mechanisms.…”

Section: Hybrid Laboratory Plant Case Studymentioning

confidence: 99%

Economic MPC with Modifier Adaptation using Transient Measurements

Oliveira-Silva

Prada

Navia

2021

31st European Symposium on Computer Aided Process Engineering

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Section: Hybrid Laboratory Plant Case Studymentioning

confidence: 99%

Economic MPC with Modifier Adaptation using Transient Measurements

Oliveira-Silva

Prada

Navia

2021

31st European Symposium on Computer Aided Process Engineering

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“…Recently, Gaussian process regression techniques or neural networks have also been investigated [27,28], to limit the impact of plant-model mismatch when the model is used to predict plant gradients. Alternatively, [29] and [30] suggest to integrate measurements (or estimates) of disturbances in the RTO framework.…”

Section: Introductionmentioning

confidence: 99%

Output modifier adaptation with filter-based constraints

Papasavvas

François

2020

Journal of Process Control

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Modifier adaptation (MA) and output modifier adaptation (MAy) are iterative model-based real-time optimization (RTO) algorithms that have the proven ability to drive plants to their optimal operating condition upon convergence despite disturbances and modeling uncertainty, provided the model at hand satisfies model adequacy conditions. But there is no guarantee that constraints are satisfied before convergence. In this article, an improvement of the formulation of MA and MAy is proposed that is proven to bring significant improvements w.r.t. these two limitations-model adequacy and feasibility of iterates. While standard MA or MAy suggests to perform optimization and filtering sequentially, it is proposed to integrate the input filtering stage in the modified model-based optimization problem by means of additional filter-based constraints. The corresponding approach, labeled "KMAy", is (i) proven to preserve constraint qualification despite additional constraints, (ii) proven to preserve the property of MA methods to converge to the true plant optimal inputs, (iii) proven to significantly relax the model adequacy condition-leading it to be independent of the constraints of the optimization problem, (iv) shown to increase the chances of converging from the safe side of the plant constraints and (v) shown to support the choice of input filtering, instead of output or modifier filtering, if the input filter is appropriately chosen. A method for the automatic selection of the largest filter gain with the five aforementioned assets, while minimizing the filter-induced conservatism, is also proposed. The performances of KMAy with and without adaptive gain are successfully illustrated by means of the optimization of a benchmark simulated chemical reactor.

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“…To enhance the control quality of orthogonal collocation method under disturbances, an online re-optimization strategy in two layers is proposed for handling disturbance and uncertainty within semi batch process by Fakhrony et al [20] . Furthermore, adding estimator in optimal control problem is also a meaningful way [21] . While, these methods are still open-loop methods, closed-loop characteristics cannot be guaranteed under disturbances.…”

Section: Introductionmentioning

confidence: 99%

Design of LQR Tracking Controller Combined With Orthogonal Collocation State Planning for Process Optimal Control

Liu

Teng

et al. 2020

IEEE Access

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As one of the most important optimization methods for process optimal control, orthogonal collocation method has been widely used. However, this kind of optimization method is generally an open-loop optimization frame, which makes the model disturbances greatly affect the control performance and quality. In order to improve the optimal control performance of dynamic systems with disturbances, this work proposed an orthogonal collocation optimization-based linear quadratic regulator (OC-LQR) control method for process optimal control problems. Firstly, the orthogonal collocation method is derived in detail to transform the optimal control problem so that optimal state curves can be accordingly calculated. Then, an improved LQR controller design is proposed by using the obtained optimal state curves so as to construct the feedback control frame. On this basis, a state planning-based LQR tracking control is established with closed-loop control characteristics to tackle model disturbance problem. Meanwhile, the detailed Simulink model of the proposed control method is constructed for simulation execution. Finally, the proposed control structure is tested on a classical process optimal control problem with model disturbance (white noise) test and Gaussian mixture noise test to verify the performance of the proposed method. Simulation studies show that the control performance of the proposed method is excellent, where the mean absolute error of state curve tracking averagely reduces by 34.73% and the minimal performance index improves by 64.44% when compared with the classical orthogonal optimal control method. Simulation results demonstrate the effectiveness of the proposed planning-based tracking control framework.

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On dealing with measured disturbances in the modifier adaptation method for real-time optimization

Cited by 7 publications

References 26 publications

Economic MPC with Modifier Adaptation using Transient Measurements

Economic MPC with Modifier Adaptation using Transient Measurements

Output modifier adaptation with filter-based constraints

Design of LQR Tracking Controller Combined With Orthogonal Collocation State Planning for Process Optimal Control

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