Adaptive sequential Monte Carlo approach for real-time applications

This paper presents an adaptive Sequential Monte Carlo approach for real-time applications. Sequential Monte Carlo method is employed to estimate the states of dynamic systems using weighted particles. The proposed approach reduces the run-time computation complexity by adapting the size of the particle set. Multiple processing elements on FPGAs are dynamically allocated for improved energy efficiency without violating real-time constraints. A robot localisation application is developed based on the proposed approach. Compared to a non-adaptive implementation, the dynamic energy consumption is reduced by up to 70% without affecting the quality of solutions. © 2012 IEEE.

FPGA, physics-based modeling of IGBT and pin diode for hardware co-simulation of complex power electronic converters and systems

This paper presents the steps and the challenges for implementing analytical, physics-based models for the insulated gate bipolar transistor (IGBT) and the PIN diode in hardware and more specifically in field programmable gate arrays (FPGAs). The models can be utilised in hardware co-simulation of complex power electronic converters and entire power systems in order to reduce the simulation time without compromising the accuracy of results. Such a co-simulation allows reliable prediction of the system's performance as well as accurate investigation of the power devices' behaviour during operation. Ultimately, this will allow application-specific optimisation of the devices' structure, circuit topologies as well as enhancement of the control and/or protection schemes.