In the field of power optimization of photovoltaic panels (PV), there exist many maximum power point tracking (MPPT) control algorithms, such as: the perturb and observe (P&O) one, the algorithms based on fuzzy logic and the ones using a neural network approaches. Among these MPPT control algorithms, P&O is one of the most widely used due to its simplicity of implementation. However, the major drawback of this kind of algorithm is the lack of accuracy due to oscillations around the PPM. Conversely, MPPT control using neural networks have shown to be a very efficient solution in term of accuracy. However, this approach remains complex.In this paper we propose an original optimization of the P&O MPPT control with a neural network algorithm leading to a significant reduction of the computational cost required to train it, ensuring a good compromise between accuracy and complexity. The algorithm has been applied to the models of two different types of solar panels, which have been experimentally validated

Goal: The goal of this paper is to propose a model-based control design framework, adapted to the development of control modules for medical devices. A particular example is presented in which instantaneous heart rate is regulated in real-time, by modulating, in an adaptive manner, the current delivered to the vagus nerve by a neuromodulator. Methods: The proposed framework couples a control module, based on a classical PI controller, a mathematical model of the medical device, and a physiological model representing the cardiovascular responses to vagus nerve stimulation (VNS). In order to analyze and evaluate the behavior of the device, different control parameters are tested on a “virtual population,” generated with the model, according to the Latin Hypercube sampling method. In particular, sensitivity analyses are applied for the identification of a domain of interest in the space of the control parameters. The obtained control parameter domain has been validated in an experimental evaluation on six sheep. Results: A range of control parameters leading to accurate results was successfully estimated by the proposed model-based design method. Experimental evaluation of the control parameters inside such a domain led to the best compromise between accuracy and time response of the VNS control. Conclusion: The feasibility and usefulness of the proposed model-based design method were shown, leading to a functional, real-time closed-loop control of the VNS for the regulation of heart rate.