The use of several different sources to feed a load jointly is convenient in many applications, in particular those where two or more renewable energy sources are employed. These applications include energy harvesting, hybrid vehicles, and off-grid systems. A multi-input converter able to admit sources of different characteristics and select the output power of each source is necessary in such applications. Several topologies of multi-input converters have been proposed to this aim; however, most of them are controlled by simple strategies based on a small signal model of multi-input converters. In this work, a low cost high gain step-up multi-input converter is analyzed. A nonlinear model is derived. Using this model, a detailed design procedure is proposed. A 500 W converter prototype was constructed to confirm that the model predicted the real behavior of the converter. Using the nonlinear model, indirect voltage control of basic converters was extended to the multi-input converter. The obtained controller had a fast performance, and it was robust under load and input voltage variations. With the obtained model, the proposed design procedure, and the controller, a converter that was initially proposed for photovoltaic applications was enabled to be used in a broader range of applications. The herein exposed ideas for modeling, the design procedure, and control could be also applied to other multi-input converters.

In control research and design it is frequently necessary to explore, evaluate, tune and compare many control strategies. These activities are assisted by software tools of increasing complexity; however, even with the existing high performance tools these activities are very time consuming due to they imply hundred if not thousand of simulations. If the process of doing such simulations is not automated it can be a very time consuming task. There has been proposed evolutionary algorithms (EA) that in the search for an optimal control automatically generate many control structures. However, the space of possible controllers for any dynamical system is huge. Hence it is mandatory to restrict the search space. The best way to restrict the controller search space is to let the designer influence the search direction. In this paper we propose a software tool for control research that has as its main part an EA that produce only controllers having a pre-specified morphology. By specifying a controller morphology the designer can influence the search direction without losing the exploration capability of evolutionary algorithms. The EA is endowed with a cost function tailored for fast evaluation of closed-loop controller performance. The use of the tool is illustrated by searching an sliding mode and similar controllers for an unstable linear and two nonlinear systems.