Active Directional Modifier Adaptation with Trust Region- Application to Energy-Harvesting Kites

Jonin, Matthieu; Singhal, Martand; Diwale, Sanket Sanjay; Jones, Colin N.; Bonvin, Dominique

doi:10.23919/ecc.2018.8550509

Cited by 3 publications

(12 citation statements)

References 12 publications

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“…However, the choice of n k does not affect the composite step s k and hence does not affect the convergence and the performance of Algorithm 4. This can be observed by replacing n k þ t with the composite-step variable s in problems (16). The equivalent problem (18) only relies on c m,k ðu k þ n k Þ and this quantity is the same for any optimum of problem (15).…”

Section: Algorithm Descriptionmentioning

confidence: 99%

“…Scaling is very important for the trust-region method to avoid illconditioned problems and ensure the solution reaches desired precision. 11 For practical RTO problems, input and output variables usually have quite different orders of magnitude, so they must be normalized by their range before solving problems (15) and (16).…”

Section: Scaling and Smoothness Reformulationsmentioning

confidence: 99%

“…In the process engineering community, trust-region optimization is applied to RTO, [15][16][17][18] black-box optimization problems, 19,20 as well as nonlinear model predictive control. 21,22 The trust-region technique controls the update step length according to the local quality of the model.…”

Section: Introductionmentioning

confidence: 99%

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A globally convergent composite‐step trust‐region framework for real‐time optimization

Zhang,

Li,

Wang

et al. 2023

AIChE Journal

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Inaccurate models limit the performance of model‐based real‐time optimization (RTO) and even cause system instability. Therefore, a RTO framework that guarantees global convergence in the presence of plant‐model mismatch is desired. In this regard, the trust‐region framework is intuitive and simple to implement for unconstrained problems. Constrained RTO problems are converted to unconstrained ones by the penalty function, and global convergence is guaranteed if the penalty coefficient is large enough. However, a sufficiently large penalty coefficient is hard to determine and may lead to numerical difficulties. This paper addresses this issue and proposes a novel composite‐step trust‐region framework for constrained RTO problems that handles inequality constraints directly. The trial step is decomposed into a normal step that improves feasibility and a tangential step that reduces the cost function. A rigorous proof of its global convergence property is given.

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Section: Algorithm Descriptionmentioning

confidence: 99%

Section: Scaling and Smoothness Reformulationsmentioning

confidence: 99%

See 1 more Smart Citation

A globally convergent composite‐step trust‐region framework for real‐time optimization

Zhang,

Li,

Wang

et al. 2023

AIChE Journal

View full text Add to dashboard Cite

show abstract

“…It might be interesting to investigate the addition of constraints that are never active at u k , as they will not a↵ect the geometrical similarity between the plantbased and model-based optimization problems at u k . For example, step-size limitation between two consecutive RTO iterations, as with trust-region (TR) methods [18,19,20], could be suitable. However, this is beyond the scope of this article.…”

Section: Infeasible Infeasiblementioning

confidence: 99%

“…So far, apart from using additional plant information, which is most often not available, such as Lipschitz constants or quadratic upper bounds [14,15,16], there is no way to ensure "absolute" plant feasibility at each iteration in the general case. One approach could be to combine trust region [17] and MA [18,19,20], reducing thus the distance between two consecutive iterates, therefore maintaining the next inputs in a region whereby the modified model is the most reliable. Methods enabling the estimation of the plant steady state during the transient operation have also been proposed [21,22,23,24], with emphasis on the convergence rate to the plant optimal operating conditions.…”

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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A Globally Convergent Composite-Step Trust-Region Framework for Real-Time Optimization with Plant-Model Mismatch

Zhang,

Li,

Wang

et al. 2023

Preprint

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Inaccurate models limit the performance of model-based real-time optimization (RTO) and even cause system instability. Therefore, a RTO framework that can guarantee global convergence with the presence of plant-model mismatch is desired. In this regard, the trust-region framework is simple to implement and guarantees globally convergent for unconstrained problems. However, it remains to be seen if the trust-region strategy can handle inequality constraints directly with the common model adaptation method. This paper addresses this issue and proposes a novel composite-step trust-region framework that guarantees global convergence for constrained RTO problems. The trial step is decomposed into a normal step that improves feasibility and a tangential step that reduces the cost function. In each iteration, the model optimization problem with relaxed constraints is solved. The proof of the global convergence property under structural plant-model mismatch is given.

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Active Directional Modifier Adaptation with Trust Region- Application to Energy-Harvesting Kites

Cited by 3 publications

References 12 publications

A globally convergent composite‐step trust‐region framework for real‐time optimization

A globally convergent composite‐step trust‐region framework for real‐time optimization

Output modifier adaptation with filter-based constraints

A Globally Convergent Composite-Step Trust-Region Framework for Real-Time Optimization with Plant-Model Mismatch

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