This work introduces a safe data-driven control methodology, Data-Enabled Predictive Control (DeePC), for the control of blood glucose in type-1 diabetic patients. DeePC utilizes input-output trajectory data directly without requiring a system model or state estimation like other modelbased algorithms. The control strategy is validated using the Bergman Minimal Model, a well-established framework for glucose-insulin dynamics. Comparative simulations are conducted against Proportional-Integral-Derivative (PID) and Model Predictive Control (MPC) strategies. Results show that DeePC achieves comparable or superior glycemic regulation, particularly under model uncertainty, by maintaining normoglycemia and reducing hypoglycemia risk. The findings demonstrate the robustness and potential of DeePC in biomedical applications where model accuracy is uncertain. Future works would include computational efficiency improvements and handling uncertainties in meal estimation.

Epilepsy is a non-communicable neurological disorder that causes recurrent and unprovoked seizure. Ideally, predicting seizures would represent a step forward in making life easier for those who suffer from epilepsy. This work aims to predict the occurence of epileptic seizures via a novel approach that combines Dynamic Mode Decomposition (DMD), that is a data-driven modelling technique for dynamical systems with a deep learning classifier, that is based on a convolutional neural network. The resulting two-stage data-driven predictor was tailored for the analysis of encephalographic (EEG) data. The validity analysis of the approach is carried out over the CHB-MIT Scalp EEG Database, demonstrating its applicability for seizures’ recognition and prediction on real clinical data.

Accurate path tracking is one of the main challenges autonomous vehicles have to deal with. It is known that, when dealing with real hardware systems, the presence of parametric uncertainty and unmodelled aspects of the system dynamics affects all model-based control approaches, hindering their nominal performance guarantees. To compensate for this issue, data-driven schemes have drawn significant attention from the scientific community thanks to their inherent ability to learn from experience, thus automatically compensate for system uncertainties and time-varying behaviours. This work aims to develop a reinforcement learning-based longitudinal and lateral dynamics control introducing mismatch and motion penalization metrics, validating the resulting controller in a simulated Formula SAE skid-test scenario employing the Sapienza Fast Charge Formula SAE Electric Racing Team dynamical model.

Autonomous driving has emerged as a technology to revolutionize the future of transportation and completely re-define the landscape of road systems. Accurate control algorithms are crucial to ensure the safety and efficiency of autonomous vehicles; in particular, incorporating actuator dynamics into the vehicle dynamics model can improve the response of the system to control commands, with clear safety implications. This work proposes pulse width modulation-based control strategies for the steering and brake actuators of a Formula SAE driverless vehicle. Extensive simulation tests and real-world experiments on a Formula SAE skid-test scenario validate the proposed approach.

Model Predictive Control (MPC) is a popular control strategy that relies on the availability of a prediction model to estimate future system trajectories over a finite time horizon. Recently, researchers have introduced Neural Networks (NNs) into the MPC framework for the development of data-driven prediction models. In MPC, the control actions are computed by solving iteratively, at each time-step, an optimization problem subject to state and input constraints. Finding the optimal solution to such a problem is a crucial challenge in the data-driven setting, due to the complexity and black-box nature of data-driven models such as NNs. This paper addresses this challenge by proposing a hierarchical deep NN formed by a set of cascading one-step NN predictors whose combination constitutes an interpretable prediction model over the entire prediction horizon. Thanks to the proposed NN architecture, it is shown that the resulting optimal control problem is tractable, as it can be solved by employing efficient iterative algorithms, and interpretable, so that input and state constraints can be enforced seamlessly. The effectiveness of the proposed method is validated through numerical simulations. Note to Practitioners—Model Predictive Control (MPC) is a widely used methodology in the industry which typically relies on the availability of a model in the form of step response, transfer function or state-space models. In some cases, the explicit model might not be available or its accuracy may be not sufficient for the required closed-loop performance. This paper aims to develop a simple and practical framework for deploying a model-free data-driven MPC solution based on deep learning. This objective is pursued by suggesting a novel approach using simple neural networks in a cascading interpretable structure. Such networks are used to predict the one-step evolution of the system, and their cascade represents the MPC prediction model over an arbitrary long prediction horizon. We characterize such a neural model focusing on its interpretability and tractability, deriving the resulting optimal control problem to be solved in a receding horizon strategy. We then show that the MPC optimization can be solved efficiently using highly efficient iterative algorithms that can be implemented in practice. Numerical simulations involving the use of the Alternating Direction Method of Multipliers (ADMM) algorithm show its effectiveness for both linear and nonlinear systems.

Topology optimization in decentralised federated learning settings enables the design of policies aimed at minimizing the number of communication rounds needed to reach algorithmic convergence. Given a federation of autonomous agents, finding the optimal topology which guarantees that the underlying graph is connected is still an open issue. This paper proposes a novel energy-aware topology optimization algorithm with the goal to derive an optimal topology which maximizes the algebraic connectivity of the corresponding graph in presence of energy and communication constraints. The effectiveness of the proposed approach is validated in the context of a consensus-based federated learning algorithm over an e-Health scenario.

This paper presents a decentralised control strategy for the management of simultaneous charging sessions of electric vehicles. The proposed approach is based on the model predictive control methodology and the Lagrangian decomposition of the constrained optimization problem which is solved at each sampling time. This strategy allows the computation of the charging profiles in a decentralised way, with limited information exchange between the electric vehicles. The simulation results show the potential of the proposed approach in relation to the problem of shaving the aggregated power withdrawal from the electricity distribution grid, while still satisfying drivers’ preferences for charging.

This paper proposes a control strategy for an Energy Storage System (ESS) installed on a secondary substation of an electricity distribution line in order to mitigate power variations with respect to the day-ahead planning caused by renewable energy sources on the distribution line.In particular, the aim of the controller is to keep the power profile of at primary substations close to a reference profile foreseen on a day-ahead basis while guaranteeing the stable operation of its ESS, in terms of their state-of-charge dynamics. The inclusion of the ESS contribution to the network operation is attained by the integration of properly defined power flow constraints in a Model Predictive Control Framework. The proposed approach has been validated through numerical simulations, representative of real operative scenarios.