Energy-aware MPC co-design for DC-DC converters

In this paper, we propose an integrated controller design methodology for the implementation of an energy-aware explicit model predictive control (MPC) algorithms, illustrat- ing the method on a DC-DC converter model. The power consumption of control algorithms is becoming increasingly important for low-power embedded systems, especially where complex digital control techniques, like MPC, are used. For DC-DC converters, digital control provides better regulation, but also higher energy consumption compared to standard analog methods. To overcome the limitation in energy efficiency, instead of addressing the problem by implementing sub-optimal MPC schemes, the closed-loop performance and the control algorithm power consumption are minimized in a joint cost function, allowing us to keep the controller power efficiency closer to an analog approach while maintaining closed-loop op- timality. A case study for an implementation in reconfigurable hardware shows how a designer can optimally trade closed-loop performance with hardware implementation performance

Degradation control for electric vehicle machines using nonlinear model predictive control

Electric machines (motors and generators) are over actuated systems. In this paper, we show how to exploit this actuation redundancy in order to mitigate machine degradation while simultaneously ensuring that the desired closed loop performance is maintained. We formulate a multiobjective optimization problem with a cost function having terms representing closed loop performance and component degradation for an inverter-fed permanent magnet synchronous motor. Such machines are important as they are widely used as the prime mover of commercial electric vehicles. The resulting optimal control problem is implemented online via a nonlinear model predictive control (NMPC) scheme. The control framework is validated for standard vehicle drive cycles. Results show that the NMPC scheme allows for better closed loop performance and lower degradation than standard industrial controllers, such as the field-oriented control method. Hence, this paper demonstrates how the remaining useful life of a machine can be increased by appropriate controller design without compromising performance

Uma avaliação experimental da plataforma parallella utilizando controle preditivo baseado em modelo como um estudo de caso

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2017.Nas últimas décadas, o poder computacional de sistemas embarcados têm crescido de forma muito rápida. Em geral, tais sistema são projetados para operar sob restrições como portabilidade (peso e tamanho), consumo de recursos, baixo consumo de energia e dissipação de potência. Assim, motivado pelos fatores supracitados e pelo avanço tecnológico, assim como pela demanda crescente de desempenho por parte das aplicações embarcadas, têm surgido vários processadores e plataformas de hardware que fazem uso de arquiteturas multicore, com destaque para a Parallella, uma plataforma de alto desempenho e baixo consumo energético. Nesse sentido, o presente trabalho traz a proposta de se avaliar tal plataforma sob uma abordagem experimental, como foco em seu coprocessador Epiphany de 16 cores, quando utilizada como um acelerador em software para aplicações de controle preditivo baseado em modelo como um estudo de caso, devido sua relevância para o grupo de pesquisa do LEIA (Laboratório de Sistemas Embarcados e Aplicações de Circuitos Integrados – Universidade de Brasília). Os resultados mostram que, apesar de restrições críticas como o tamanho da memória local dos cores, a plataforma Parallella se apresenta como uma arquitetura em potencial, podendo ser vista como uma alternativa à aceleração de algoritmos em hardware. Melhorias futuras como a expansão do número de núcleos do MPSoC Epiphany e da memória local dos mesmos, como previsto pelos fundadores do projeto, poderão alavancar ainda mais o uso de tal arquitetura em aplicações embarcadas.In the last decades, the computational power of embedded systems has grown very fast. In general, such systems are designed to operate under constraints such as portability, resource consumption, low power consumption and power dissipation. Thus, due to the aforementioned factors and technological advances, as well as the increasing demand for performance by embedded applications, there have been several processors and hardware platforms that make use of multicore architectures, with emphasis on a Parallella, a platform of high performance and low consumption. In this sense, the present work presents a proposal to evaluate such platform in an experimental approach, focusing on its Epiphany 16-core co-processor, when used as a software accelerator for model-based predictive control applications as a case study, due to its relevance to the research group of LEIA (Laboratory of Embedded Systems and Applications of Integrated Circuits - University of Brasilia). The results show that, despite critical constraints such as the local memory size of the cores, a Parallella platform presents itself as a potential architecture and can be seen as an alternative to accelerating hardware algorithms. Future improvements such as the expansion of the number of MPSoC Epiphany cores and their local memory, as predicted by the founders of the project, can leverage the use of this architecture in embedded application