Power flow management of triple active bridge for fuel cell applications

<p>The power conditioning circuits which are used in fuel cell systems should carefully be designed to prolong the life span of the system, for the reason of the dynamic nature, such that the unexpected and extreme changes in load decreases the life of the fuel cells. This paper presents the triple active bridge (TAB) and it’s average small signal modelling, which is used for design of the system controllers for stable operation. The extended symmetrical optimum method is used for realized the proportional integral (PI) controller, to control the output/Load voltage and power flow in the fuel cell/Source with a guaranteed minimum phase margin for the system with a variable process gain in addition to other accepted desired performances. This method ensures the maximum phase margin at a minimum required value at the desired gain crossover frequency with a compromise between system’s peak overshoot, rise time and settling time. This model and this approach helps in designing TAB suitable for healthy and uninterrupted fuel cell power generation systems as a part of a renewable /clean energy system. MATLAB/Simulink is used to simulate the proposed controllers with TAB.</p>

Control solutions in mechatronics systems

This paper presents control solutions dedicated to a class of controlled plants widely used in mechatronics systems, characterized by simplified mathematical models of second-order and third-order plus integral type. The conventional control solution is focused on the Extended Symmetrical Optimum method proposed by the authors in 1996. There are proposed six fuzzy control solutions employing PI-fuzzy controllers. These solutions are based on the approximate equivalence in certain conditions between fuzzy control systems and linear ones, on the application of the modal equivalence principle, and on the transfer of results from the continuous-time conventional solution to the fuzzy solutions via a discrete-time expression of the controller where Prof. Milic R. Stojic's book [1] is used. There is performed the sensitivity analysis of the fuzzy control systems with respect to the parametric variations of the controlled plant, which enables the development of the fuzzy controllers. In addition, the paper presents aspects concerning Iterative Feedback Tuning and Iterative Learning Control in the framework of fuzzy control systems. The theoretical results are validated by considering a real-world application.