Keywords: Robust Control and Stabilization, Multi-Objective Control and Optimization, Control Design for Hybrid Systems
Abstract: Optimized Active Disturbance Rejection Control for Type 1 Diabetes is presented as a personalized, closed-loop strategy for safe glycemic regulation. Each patient is modeled with a low-order second-order-plus-dead-time surrogate, and a linear ADRC with a reduced-order extended-state observer estimates and cancels lumped disturbances in real time. A simple two-parameter tuning (bandwidth and gain scaling) yields patient-specific settings that are inexpensive to compute and practical for embedded deployment. Validation on the UVA/Padova simulator with ten virtual adults under two demanding meal/fasting protocols shows stable behavior without hypoglycemic events and glucose profiles close to normoglycemia across fasting, postprandial, and nocturnal periods. These results support ADRC as a robust and efficient alternative for rapid, per-patient initialization in artificial-pancreas applications.

Keywords: Optimal Control, Stochastic Optimization
Abstract: We consider the problem of optimal investment in a multi-asset market with borrowing, unbounded random coefficients, and the power utility from terminal wealth. The resulting optimization problem, due to the higher interest rate for borrowing than for lending, is a multi-input stochastic optimal control problem with a nonlinear system dynamics and unbounded random coefficients. A certain piece-wise completion of squares method and a linear backward stochastic differential equation are used to find an explicit closed-form solution as a linear state-feedback control, the gain of which can have up to five different regimes.

Keywords: Optimal Control, Stochastic Optimization
Abstract: We consider an optimal control problem for linear stochastic systems with discrete and distributed state-delay, and random time-horizon. We derive an explicit closed-form solution for this problem under a general quadratic-linear cost functional, in terms of solutions to a system of coupled Riccati and partial differential equations. The resulting optimal control law takes an affine state-feedback form, depending on the current state, the delayed state, and the integral of past state values.

Keywords: Applications in Transportation
Abstract: This article presents the modeling, simulation, and development of Wheel-E, a reaction-wheel-stabilized unicycle robot with a prismatic leg actuated by quasi-direct drives. A nonlinear model was derived using Lagrangian mechanics, and a comprehensive MATLAB/Simulink simulation incorporating motor and sensor models was developed. A 3Dprinted prototype with an STM32-based real-time control system was also built.

Keywords: Optimization for Learning and Control, Numerical Methods for Optimization, Applications in Economics, Management and Environmental Science
Abstract: This paper introduces a methodology for identifying and simulating financial and economic systems using stochastically structured reservoir computers (SSRCs). The framework combines structure-preserving embeddings with graph-informed coupling matrices to model inter-agent dynamics while enhancing interpretability. A constrained optimization scheme guarantees compliance with both stochastic and structural constraints. Two empirical case studies, a nonlinear stochastic dynamic model and regional inflation network dynamics, demonstrate the effectiveness of the approach in capturing complex nonlinear patterns and enabling interpretable predictive analysis under uncertainty.

Keywords: Data-driven Models and Decision-Making
Abstract: Presented study investigates low-cost methods for detecting demagnetization and mechanical imbalance in BLDC motors using vibration and current measurements. Experiments employed MEMS and piezoelectric accelerometers, with data sampled at 1 kHz and 50 kHz. At 1 kHz, SVM achieved 100% accuracy using a single axis of the piezoelectric sensor, while MEMS-based and neural network models underperformed. For 50 kHz data, downsampling to 2 kHz enabled reliable imbalance detection, but demagnetization detection remained challenging. Welch PSD analysis confirmed characteristic frequency components.

Keywords: Applications in Energy and Power Systems, Data-driven Models and Decision-Making
Abstract: Zinc is distributed widely in nature, and Zinc-air batteries are gaining considerable interest among researchers due to the increasing demand for energy storage systems. To ensure the effective integration of these batteries through reliable Battery Management Systems, it is essential to establish appropriate battery models. This paper explores the dynamical modeling of secondary zinc-air cells for control purposes, presenting an electrical circuit model designed to estimate their voltage output. Additionally, the model is extended to assess the voltage output of a series-connected module made up of two cells. The analysis also examines the impact of differently aged cells on voltage estimation and module performance, laying the groundwork for future monitoring of potential faults within battery management systems.