arXiv subjects

Martin Doff-Sotta

Publications and source records attributed to Martin Doff-Sotta.

11 recordsLinked to original sources

arXiv2026

Computationally Tractable Robust Nonlinear Model Predictive Control using DC Programming

We propose a computationally tractable, tube-based robust nonlinear model predictive control (MPC) framework using difference-of-convex (DC) functions and sequential convex programming. For systems with differentiable discrete time dynamics, we show how to construct systematic, data-driven DC model representations using polynomials and machine learning techniques. We develop a robust tube MPC scheme that convexifies the online optimization by linearizing the concave components of the model, and we provide guarantees of recursive feasibility and robust stability. We present three data-driven procedures for computing DC models and compare performance using a planar vertical take-off and landing (PVTOL) aircraft case study.

math.OC↗

arXiv2026

Modeling and Control of Hybrid Distribution Transformers for Simultaneous Grid Services

Hybrid distribution transformers (HDTs) integrate conventional transformers with partially rated power electronic converters to improve power quality, enable advanced ancillary services and increase penetration of renewable energy sources in the national power grid. In this paper, we present an averaged mathematical model of a three-phase HDT equipped with two back-to-back voltage source converters connected in a series-shunt configuration. Cascaded PI controllers are designed in the synchronously rotating dq0 reference frame to regulate load voltage, compensate reactive power, achieve grid frequency regulation, and perform load phase balancing. Simulation results implemented in Python confirm that these simple yet effective control mechanisms allow HDTs to offer simultaneous grid services without introducing complexity. The complete model, control architecture, and implementation steps are detailed, enabling further validation and adoption.

eess.SY↗

arXiv2025

Optimal Placement of Smart Hybrid Transformers in Distribution Networks

Hybrid transformers are a relatively new technology that combine conventional power transformers with power electronics to provide voltage and reactive power control capabilities in distribution networks. This paper proposes a novel method of determining the optimal location and utilisation of hybrid transformers in 3-phase distribution networks to maximise the net present value of hybrid transformers based on their ability to increase the export of power produced by distributed generators over their operational lifespan. This has been accomplished through sequential linear programming, a key feature of which is the consideration of nonlinear characteristics and constraints relating to hybrid transformer power electronics and control capabilities. Test cases were carried out in a modified version of the Cigre European Low Voltage Distribution Network Benchmark, which has been extended by connecting it with two additional low voltage distribution test networks. All test case results demonstrate that the installation and utilisation of hybrid transformers can improve the income earned from exporting excess active power, justifying their installation cost (with the highest net present value being £6.56 million, resulting from a 45.53 percent increase in estimated annual profits due to coordinated HT compensation).

eess.SY↗

arXiv2025

Deep learning adaptive Model Predictive Control of Fed-Batch Cultivations

Bioprocesses are often characterised by nonlinear and uncertain dynamics, posing particular challenges for model predictive control (MPC) algorithms due to their computational demands when applied to nonlinear systems. Recent advances in optimal control theory have demonstrated that concepts from convex optimisation, tube MPC, and differences of convex functions (DC) enable efficient, robust online process control. Our approach is based on DC decompositions of nonlinear dynamics and successive linearisations around predicted trajectories. By convexity, the linearisation errors have tight bounds and can be treated as bounded disturbances within a robust tube MPC framework. We describe a systematic, data-driven method for computing DC model representations using deep learning neural networks with a special convex structure, and explain how the resulting MPC optimisation can be solved using convex programming. For the problem of maximising product formation in a cultivation with uncertain model parameters, we design a controller that ensures robust constraint satisfaction and allows online estimation of unknown model parameters. Our results indicate that this method is a promising solution for computationally tractable, robust MPC of bioprocesses.

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arXiv2023

Handling nonlinearities and uncertainties of fed-batch cultivations with difference of convex functions tube MPC

Bioprocesses are often characterized by nonlinear and uncertain dynamics. This poses particular challenges in the context of model predictive control (MPC). Several approaches have been proposed to solve this problem, such as robust or stochastic MPC, but they can be computationally expensive when the system is nonlinear. Recent advances in optimal control theory have shown that concepts from convex optimization, tube-based MPC, and difference of convex functions (DC) enable stable and robust online process control. The approach is based on systematic DC decompositions of the dynamics and successive linearizations around feasible trajectories. By convexity, the linearization errors can be bounded tightly and treated as bounded disturbances in a robust tube-based MPC framework. However, finding the DC composition can be a difficult task. To overcome this problem, we used a neural network with special convex structure to learn the dynamics in DC form and express the uncertainty sets using simplices to maximize the product formation rate of a cultivation with uncertain substrate concentration in the feed. The results show that this is a promising approach for computationally tractable data-driven robust MPC of bioprocesses.

q-bio.QM↗

arXiv2023

Data-driven robust MPC of tiltwing VTOL aircraft

This paper investigates robust tube-based Model Predictive Control (MPC) of a tiltwing Vertical Take-Off and Landing (VTOL) aircraft subject to wind disturbances and model uncertainty. Our approach is based on a Difference of Convex (DC) function decomposition of the dynamics to develop a computationally tractable optimisation with robust tubes for the system trajectories. We consider a case study of a VTOL aircraft subject to wind gusts and whose aerodynamics is defined from data.

eess.SY↗

arXiv2023

Robust trajectory optimisation for transitions of tiltwing VTOL aircraft

We propose a method to generate robust and optimal trajectories for the transition of a tiltwing Vertical Take-Off and Landing (VTOL) aircraft leveraging concepts from convex optimisation, tube-based nonlinear Model Predictive Control (MPC) and Difference of Convex (DC) functions decomposition. The approach relies on computing DC decompositions of dynamic models in order to exploit convexity properties and develop a tractable robust optimisation that solves a sequence of convex programs converging to a local optimum of the trajectory generation problem. The algorithm developed is applied to an Urban Air Mobility case study. The resulting solutions are robust to approximation errors in dynamic models and provide safe trajectories for aggressive transition manoeuvres at constant altitude.

math.OC↗

arXiv2022

Predictive energy management for hybrid electric aircraft propulsion systems

We present a Model Predictive Control (MPC) algorithm for energy management in aircraft with hybrid electric propulsion systems consisting of gas turbine and electric motor components. Series and parallel configurations are considered. By combining a point-mass aircraft dynamical model with models of electrical losses and losses in the gas turbine, the fuel consumed over a given future flight path is minimised subject to constraints on the battery, electric motor and gas turbine. The optimization is formulated as a convex problem under mild assumptions and its solution is used to define a predictive energy management control law that takes into account the variation in aircraft mass during flight. We investigate the performance of algorithms for solving this problem. An Alternating Direction Method of Multipliers (ADMM) algorithm is proposed and compared with a general purpose convex interior point solver. We also show that the ADMM implementation reduces the required computation time by orders of magnitude in comparison with a general purpose nonlinear programming solver, making it suitable for real-time supervisory energy management control.

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arXiv2021

Fast optimal trajectory generation for a tiltwing VTOL aircraft with application to urban air mobility

We solve the minimum-thrust optimal trajectory generation problem for the transition of a tiltwing Vertical Take-Off and Landing (VTOL) aircraft using convex optimisation. The method is based on a change of differential operator that allows us to express the simplified point-mass dynamics along a prescribed path and formulate the original nonlinear problem in terms of a pair of convex programs. A case study involving the Airbus A3 Vahana VTOL aircraft is considered for forward and backward transitions. The presented approach provides a fast method to generate a safe optimal transition for a tiltwing VTOL aircraft that can further be leveraged online for control, and guidance purposes.

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arXiv2021

Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset

Hateful memes pose a unique challenge for current machine learning systems because their message is derived from both text- and visual-modalities. To this effect, Facebook released the Hateful Memes Challenge, a dataset of memes with pre-extracted text captions, but it is unclear whether these synthetic examples generalize to `memes in the wild'. In this paper, we collect hateful and non-hateful memes from Pinterest to evaluate out-of-sample performance on models pre-trained on the Facebook dataset. We find that memes in the wild differ in two key aspects: 1) Captions must be extracted via OCR, injecting noise and diminishing performance of multimodal models, and 2) Memes are more diverse than `traditional memes', including screenshots of conversations or text on a plain background. This paper thus serves as a reality check for the current benchmark of hateful meme detection and its applicability for detecting real world hate.

cs.CV↗

arXiv2020

Optimal energy management for hybrid electric aircraft

A convex formulation is proposed for optimal energy management in aircraft with hybrid propulsion systems consisting of gas turbine and electric motor components. By combining a point-mass aircraft dynamical model with models of electrical and mechanical powertrain losses, the fuel consumed over a planned future flight path is minimised subject to constraints on the battery, electric motor and gas turbine. The resulting optimisation problem is used to define a predictive energy management control law that takes into account the variation in aircraft mass during flight. A simulation study based on a representative 100-seat aircraft with a prototype parallel hybrid electric propulsion system is used to investigate the properties of the controller. We show that an optimisation-based control strategy can provide significant fuel savings over heuristic energy management strategies in this context.

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