Our motivation focuses on answering a simple question: What is the minimum robotic structure necessary to solve a navigation problem? Our research deals with environments that are unknown, dynamic, and denied to sensors. In particular, the paper addresses problems concerning how to coordinate the navigation of multiple autonomous mobile robots without requiring system identifi cation, geometric map building, localization or state estimation. The proposed navigation algorithm uses the gradient of the environment to set the navigation control. This gradient is continuously modifi ed by all the robots in the form of local communication.

In research related to control of DC/DC converters, artifi cial intelligence techniques are a great improvement  in  the  design  and  performance. However, some of these tools require the use of trial and error strategies in the design, making it diffi cult to obtain an optimal structure. In this pa-per, we propose a direct control based on artifi cial neural network, whose design has been optimized using  bio-inspired  searching  strategies,  with  the idea of    optimizing simultaneously two different but important aspects of the network: architecture and  weights  connections.  The  control  was  successfully  applied  to  a  boost  type  converter. The results obtained allow us to observe the dynamic performance of the scheme, in which the response time  and  variation  in  the  output  voltage  can  be concluded  that  the  criteria  used  for  the  control loop design were appropriate.

Este artículo presenta el diseño, desarrollo y prueba en laboratorio de un circuito rectificador de alimen­tación monofásica, concebido para la alimentación de potencia eléctrica de sistemas UPS de hasta 5 kW, y con la característica particular de contar con un esquema de reducción de contenido armóni­co en la señal de corriente de entrada, de simple implementación y alta velocidad de respuesta. El prototipo implementado se dotó con un pequeño y muy económico microcontrolador PIC12F675 de microchip como unidad central de control, y se diseñó el circuito de potencia para trabajar en el límite de operación de modo continuo, con la idea de mejorar la eficiencia del sistema.De igual manera, se implementó un esquema de control por histéresis como estrategia general para la reducción del contenido armónico, comparando la corriente de entrada con una señal de referencia sinusoidal construida y sincronizada por el microcontrolador para la potencia y el voltaje nominal de salida. Con el fin de verificar la funcionalidad y el desempeño del esquema de diseño, se realizaron simulaciones del circuito de potencia y de su control, y se constru­yó un prototipo de laboratorio de 300 W. En ambos casos, se observó un excelente comportamiento tanto en respuesta dinámica como en reducción de contenido armónico.

Controlling DC/DC converters (topologies widely used in the active reduction of harmonic content for single-phase nonlinear low-power equipment) raises great design challenges due to the mathematical model's complexity and its highly nonlinear dynamic characteristics. Artificial intelligence techniques, such as neuronal networks, suppose great improvements in design and final performance, given their capacity for learning complex dynamics and generalising their behaviour. This work was aimed at proposing (and evaluating dynamic response later on) direct control link with neuronal networks which also allowed eliminating test elements and error in its design. Artificial neuronal network-based direct control was designed as well as possible using bioinspired search models. This simultaneously optimized two different but fundamental aspects of the network: architecture and the weight of the connections. The control was applied to a boost converter. The results led to observing the scheme's dynamic performance; response time and exit voltage delta led to concluding that the criteria selected for designing the control were appropriate and represented a contribution towards developing control applications of DC/DC switchmode systems.