Pablo Velarde

This paper proposes a Generalized Predictive Control (GPC) approach for the inner current-control loop of a multiphase induction motor drive. The method is based on a CARIMA model, which inherently provides integral action, allowing steady-state error rejection without the need for state or disturbance observers. In contrast to state-space Model Predictive Control (MPC), the proposed formulation enables the straightforward inclusion of system delays and yields an analytical solution when constraints are not considered, reducing computational complexity. Additionally, it is shown that the GPC is able to cope with modelling errors due to different dynamics at different operating points. The effectiveness of the approach is evaluated on a five-phase induction machine, showing competitive performance and improved robustness against modelling uncertainties.

The increasing integration of photovoltaic (PV) systems into modern power grids poses significant operational challenges, including variability in solar generation, fluctuations in demand, degradation of power quality, and reduced reliability under uncertain conditions. Addressing these challenges requires advanced control strategies that can manage uncertainty while coordinating storage, inverter-level actions, and power quality functions. This paper proposes a unified stochastic Model Predictive Control (SMPC) framework for the optimal management of photovoltaic (PV) systems under uncertainty. The approach integrates chance-constrained optimization with Value-at-Risk (VaR) modeling to ensure system reliability under variable solar irradiance and demand profiles. Unlike conventional deterministic MPCs, the proposed method explicitly addresses stochastic disturbances while optimizing energy storage, generation, and power quality. The framework introduces a hierarchical control architecture, where a centralized SMPC coordinates global energy flows, and decentralized inverter agents perform local Maximum Power Point Tracking (MPPT) and harmonic compensation based on the instantaneous power theory. Simulation results demonstrate significant improvements in energy efficiency from 78% to 85%, constraint satisfaction from 85% to 96%, total harmonic distortion reduction by 25%, and resilience (energy supply loss reduced from 15% to 5% under fault conditions), compared to classical deterministic approaches. This comprehensive methodology offers a robust solution for integrating PV systems into modern grids, addressing sustainability and reliability goals under uncertainty.

This work presents a novel two-layer distributed control system for energy management in an energy community, integrating stochastic and distributed model predictive control with a fault-tolerant mechanism. Unlike conventional approaches, the proposed method allows agents to autonomously detect, isolate, and adapt to faults in a fully distributed manner, without relying on a central coordinator. This is achieved through a smart contract deployed on a blockchain network, which facilitates decentralized coordination and fault management. The lower layer implements a fault-tolerant mechanism that detects, isolates, and adapts the control parameters of affected agents. It minimizes false positives through residual signal analysis and dynamically adjusts operational constraints to maintain system stability and performance. When multiple agents are affected, fault-related information is exchanged among them, enabling a coordinated response. This allows proactive actions such as disabling power lines linked to faulty agents or adjusting power transactions in response to reduced output capacity from a neighboring agent. The upper layer employs chance-constrained model predictive control to handle uncertainties in energy demand and renewable generation, ensuring robust decision-making under variability. Energy scheduling is managed through blockchain-based smart contracts, providing a secure and decentralized coordination framework. The effectiveness of the proposed scheme is validated through simulations, demonstrating its ability to optimize energy distribution while enhancing fault resilience in energy communities.

Este artículo presenta una formulación detallada del control predictivo basado en modelo con escenarios ponderados (WS-MPC, por sus siglas en inglés) orientada a sistemas lineales con incertidumbres. El enfoque propuesto combina técnicas de optimización robusta y generación de múltiples escenarios dinámicos del sistema, asignando una ponderación diferenciada al escenario de peor caso dentro del problema de control. Esta estrategia permite una mayor resiliencia ante perturbaciones estocásticas y modelado incierto, mejorando la capacidad del controlador para anticiparse a comportamientos no esperados. El WS-MPC adapta dinámicamente las acciones de control en función de la evolución de todos los escenarios considerados, manteniendo la factibilidad y estabilidad del sistema ante condiciones desfavorables. Los resultados de simulación demuestran que el enfoque propuesto incrementa la robustez operativa del sistema y proporciona una respuesta más segura y confiable frente a variaciones inesperadas. En particular, este controlador se ha implementado en un caso de estudio académico centrado en la gestión del inventario de una tienda.