arXiv subjects

E. M. Turan

Publications and source records attributed to E. M. Turan.

3 recordsLinked to original sources

arXiv2025

ORACLE: A rigorous metric and method to explore all near-optimal designs for energy systems

Optimization models are fundamental tools for providing quantitative insights to decision-makers. However, models, objectives, and constraints do not capture all real-world factors accurately. Thus, instead of the single optimal solution, real-world stakeholders are often interested in the near-optimal space -- solutions that lie within a specified margin of the optimal objective value. Solutions in the near-optimal space can then be assessed regarding desirable non-modeled or qualitative aspects. The near-optimal space is usually explored by so-called Modelling to Generate Alternatives (MGA) methods. However, current MGA approaches mainly employ heuristics, which do not measure or guarantee convergence. We propose a method called ORACLE, which guarantees generation and exploration on the \emph{entire near-optimal} space by exploiting convexity. ORACLE iteratively approximates the near-optimal space by introducing a metric that both measures convergence and suggests exploration directions. Once the approximations are refined to a desired tolerance, any near-optimal designs can be generated with negligible computational effort. We compare our approach with existing methods on a sector-coupled energy system model of Switzerland. ORACLE is the only method able to guarantee convergence within a desired tolerance. Additionally, we show that heuristic MGA methods miss large areas of the near-optimal space, potentially skewing decision-making by leaving viable options for the energy transition off the table.

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arXiv2025

Learning a convex cost-to-go for single step model predictive control

For large uncertain systems, solving model predictive control problems online can be computationally taxing. Using a shorter prediction horizon can help, but may lead to poor performance and instability without appropriate modifications. This work focuses on learning convex objective terms to enable a single-step control horizon, reducing online computational costs. We consider two surrogates for approximating the cost-to-go: (1) a convex interpolating function and (2) an input-convex neural network. Regardless of the surrogate choice, its behavior near the origin and its ability to describe the feasible region are crucial for the closed-loop performance of the new MPC problem. We address this by tailoring the surrogate to ensure good performance in both aspects. We conclude with numerical examples, in which we compare the convex surrogates to using a standard neural network in the objective, solely using an LQR cost-to-go, and to using a neural network to learn a control policy. The proposed approaches are shown to achieve better performance with less data.

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arXiv2024

Closed-loop training of static output feedback neural network controllers for large systems: A distillation case study

The online implementation of model predictive control for constrained multivariate systems has two main disadvantages: it requires an estimate of the entire model state and an optimisation problem must be solved online. These issues have typically been treated separately. This work proposes an integrated approach for the offline training of an output feedback neural network controller in closed loop. Online this neural network controller computers the plant inputs cheaply using noisy measurements. In addition, the controller can be trained to only make use of certain predefined measurements. Further, a heuristic approach is proposed to perform the automatic selection of important measurements. The proposed method is demonstrated by extensive simulations using a non-linear distillation column model of 50 states.

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